Search Results (3,230)

Mitochondrial dysfunction is increasingly recognized as a major contributor to central nervous system (CNS) disorders, including neurodegenerative and neuropsychiatric diseases. Animal models are essential for elucidating disease mechanisms and supporting the development of new therapeutic strategies. Among these models, non-mammalian organisms offer distinct advantages, including low cost, rapid life cycles, genetic tractability, and suitability for large-scale, high-throughput studies. Organisms such as Saccharomyces cerevisiae, Dictyostelium discoideum, Caenorhabditis elegans, Drosophila melanogaster, and Danio rerio have substantially advanced the understanding of mitochondrial processes relevant to CNS pathology. Studies using these models have revealed conserved mechanisms involving mitophagy, mitochondrial quality control, respiratory function, bioenergetic signaling, and neurodegenerative pathways. Their strengths, including scalability, live imaging capacity, and efficient genetic manipulation, have accelerated disease modeling and therapeutic discovery. However, simplified physiology, evolutionary distance from humans, and the incomplete representation of complex CNS organization limit their translational relevance and often require validation in higher-order organisms. Nevertheless, integrating these models into CNS research, particularly alongside emerging technologies, provides a powerful strategy for linking fundamental mitochondrial biology with translational neuroscience. This review summarizes the use of non-mammalian models in neuroscience research, with an emphasis on mitochondrial dysfunction in CNS disorders and their potential to support future therapeutic advances. Full article

Sarcopenia is an age-related progressive decline in skeletal muscle mass, strength, and physical performance that increases the risk of falls, disability, and reduced quality of life among older adults. Its pathogenesis is multifactorial and involves chronic low-grade inflammation, hormonal disturbances, insulin resistance, and mitochondrial dysfunction, leading to an imbalance between muscle protein synthesis and degradation. The aim of this study was to summarise current knowledge regarding the mechanisms underlying sarcopenia, contemporary diagnostic methods, and the effectiveness of modern nutritional and exercise-based strategies, with particular emphasis on technologies supporting patient monitoring. This study is a structured narrative review conducted across PubMed, Scopus, and Web of Science databases, with the literature search completed on 1 March 2026. Separate searches were performed for thematic sections, including pathophysiology, diagnosis, physical activity, nutritional interventions, plant-derived compounds, and digital health technologies. While the core search focused on publications from 2023–2025, specific time-bound deviations were applied: the search for plant-derived compounds was extended back to 2020, and combined interventions were searched up to March 2026 to ensure the inclusion of the most recent evidence. The review included 53 peer-reviewed primary studies (RCTs and observational) and secondary literature (reviews and meta-analyses) involving individuals aged ≥60 years. The most robust evidence supports multicomponent interventions, particularly the synergy between resistance training and adequate protein intake (1.2–1.5 g/kg/day), often supplemented with leucine, vitamin D, omega-3 fatty acids, and creatine. Such strategies effectively counteract anabolic resistance by combining mechanical loading with the stimulation of the mTORC1 signalling pathway, leading to significant improvements in muscle mass, strength, and physical function. While isolated protein or micronutrient supplementation shows limited effectiveness in the absence of exercise, their role as supportive elements in multimodal strategies is well-documented. Furthermore, emerging digital health technologies—including wearable sensors and telerehabilitation—are proving essential for clinical practice, enabling precise, continuous monitoring of physical activity and gait parameters under free-living conditions, which enhances both patient adherence and long-term therapeutic outcomes. Full article

Background: Considerable inter-individual variability exists in exercise-induced adaptations among older adults. Although microbial ecosystems have been linked to muscle function and physical performance, the role of the oral microbiota in exercise responsiveness remains unclear. Objective: To explore whether baseline oral microbiota characteristics are associated with training-induced changes in relative handgrip strength (rHGS) in older adults. Methods: This preliminary exploratory longitudinal study included 18 community-dwelling older adults who completed a 16-week supervised exercise intervention. Oral microbiota composition was assessed at baseline using 16S rRNA gene sequencing. Participants were classified as responders when ΔrHGS was >0 and as non-responders when ΔrHGS was ≤0; this operational threshold did not account for measurement error or clinically meaningful change. Associations between baseline microbiota variables and ΔrHGS were examined using group comparisons, Spearman correlations, FDR correction, and exploratory linear regression models. Results: Responders showed higher baseline bacterial genus richness than non-responders at the nominal level (86.15 ± 8.90 vs. 71.60 ± 13.32; p = 0.023), although this difference did not remain significant after FDR correction (pFDR = 0.069). Baseline richness was positively associated with ΔrHGS (ρ = 0.578, p = 0.012, pFDR = 0.036) and remained associated with ΔrHGS in exploratory sensitivity models. Genus-level findings did not remain significant after FDR correction and were interpreted as exploratory candidate signals. Conclusions: In this preliminary cohort, greater baseline oral microbiota richness was associated with larger improvements in rHGS after exercise training. These hypothesis-generating findings require confirmation in larger studies with functional microbiome assessment before causal or predictive interpretations can be made. Full article

This study explores current applications of exergaming in healthcare, with a focus on how the Otago Exercise Program—a structured, evidence-based program designed to improve strength and balance in older adults—integrated into the FallSensing exergames, contributes to improving older adults’ functioning. It also aims to generate evidence to support the optimization of sensor-based technologies for more personalized and adaptable exercise interventions. Community-dwelling older adults (≥60 years) were recruited from facilities in Coimbra, Portugal, and allocated into an exergames group (IG; n = 27) and a control group (CG; n = 34). The CG maintained usual daily activities, while the IG completed an 8-week (16-session) exergame-based program. After completing the program, the CG showed a decline in functional ability, whereas the IG demonstrated significant improvements in the Step Test (p = 0.001), Four-Stage Balance Modified Test (p = 0.001), Self-Efficacy for Exercise Scale (p = 0.009), and Activities and Participation Profile Related to Mobility questionnaire (p < 0.001). Exergaming was safe and effective in enhancing functional ability, participation, and self-efficacy in older adults. However, careful consideration of exercise frequency, intensity, and participants’ age is recommended when prescribing exergame-based interventions. These results also highlight another interesting topic among physiotherapists who prescribe and monitor exergames, that technology developers should consider exercise-time monitoring systems that integrate physical (e.g., eye, facial, and mouth features) and physiological signals to enhance fatigue detection accuracy. Full article

Hybrid systems integrating grid-following (GFL) and grid-forming (GFM) inverters are increasingly deployed in renewable-energy-dominated power systems. However, impedance coupling between the two inverter types may induce low-frequency oscillations and high-frequency resonances, particularly under weak-grid conditions and varying power injections. This paper clarifies the stability mechanism of a hybrid GFL/GFM inverter system and develops a frequency-segmented active damping strategy. Small-signal impedance models are first derived for the GFL inverter, the GFM inverter, and the overall hybrid system, incorporating the control loops, digital delay, LC filters, interconnection branch impedances, and external grid impedance. Impedance decomposition, Bode plots, and Nyquist criteria are then employed to quantify the influence of power operating points and grid strength on system stability. The results indicate that increasing the GFL inverter output power weakens the stability margins in both low- and high-frequency ranges, whereas variations in the GFM inverter output power provide only limited impedance reshaping in the targeted oscillation bands. On this basis, a low-frequency damping loop is designed on the GFM inverter side, while a high-frequency damping loop based on capacitor-current feedback is implemented on the GFL inverter side. Simulation results confirm that the proposed strategy suppresses low-frequency oscillations and high-frequency harmonic components, maintains stable operation in the hybrid system under high GFL power injection, and reduces the THD of the PCC current from 14.52% to 0.62%. Full article

Reliable medium–long-term wind energy projection is essential in the planning, financing, and operation of large-scale offshore wind development. This study classified projection methods into three categories: statistical/empirical and climate-signal-driven methods, dynamical models with reanalysis and regional downscaling, and machine/deep learning and hybrid methods for bias correction, downscaling, and direct data-driven projection. Then, this study reviewed the technical framework, representative studies, and comparative strengths and limitations. The main finding was that the state of the art increasingly converged on “dynamical simulation plus statistical or machine learning correction”. Next, seven main bottlenecks, along with the countermeasures, were systematically presented: (i) difficult data quality control and insufficient observational representativeness, especially offshore; (ii) divergent, even contradictory, conclusions for the same region across data sources and research groups; (iii) large uncertainty in extrapolating 10 m winds to the continually rising turbine hub height; (iv) difficulty in quantifying and communicating non-stationarity and uncertainty to decision-makers; (v) engineering conversion errors from projected “wind resource” to deliverable “electricity”; (vi) systematic biases in the marine atmospheric boundary layer, strong winds, and extreme conditions; and (vii) unresolved reliability, interpretability, and out-of-distribution generalization of AI models. Correspondingly, three mutually reinforcing strands of countermeasures were proposed: first, strengthening the observational and benchmarking foundation through unified, open, quality-controlled observation networks with data-provenance standards and shared reference datasets and intercomparison protocols; second, advancing physics–data integration and uncertainty quantification through hybrid and physics-informed correction, regime-specific bias correction of boundary-layer and extreme-wind errors, and probabilistic frameworks that delivered and clearly communicated credible intervals; and third, closing the resource-to-electricity gap by embedding power-curve convolution, wake-loss modeling, and availability and technology derating into the projection workflow, with the aim of improving medium–long-term wind energy projection accuracy. Full article

Radiomics has produced tens of thousands of publications yet almost no handcrafted radiomic signatures in routine clinical use, and the reasons are increasingly understood to be problems of reproducibility and clinical translation rather than of algorithms. This critical narrative review argues that the field systematically generates paper-grade evidence—findings sufficient to publish—far faster than decision-grade evidence—findings sufficient to change clinical practice. Drawing on meta-scientific research, we describe seven fragility mechanisms (publication bias, analytical flexibility, underpowering, HARKing [hypothesizing after the results are known], citation distortion, cognitive bias, and misaligned incentives) and show why radiomics is structurally exposed to all of them simultaneously: high-dimensional feature spaces, acquisition-dependent measurement instability, segmentation variability, retrospective single-centre data, small samples, and leakage-prone validation. We then summarise empirical evidence on the radiomics literature, which remains pervaded by suboptimal methodological quality, near-absent negative results, limited external validation, sparse calibration and clinical-utility assessment, low data and code sharing, and a measurable retraction signal. We interpret these patterns as the output of a self-reinforcing system rather than isolated errors, and argue that better algorithms alone cannot resolve them. Finally, we argue that closing this gap requires not better models but evidentiary discipline: the consistent, enforceable application of standards the field already has, and the calibration of published claims to the strength of the underlying evidence. Full article

Colloidal crystals permeated by mobile ions exhibit a coupling between electrostatic and elastic degrees of freedom that renormalizes the effective screening length and induces wave-vector-dependent elastic softening. Building on our recently proposed continuum model, we perform a rigorous Gaussian fluctuation analysis to elucidate the stability limits of the homogeneous phase. By integrating out the electrostatic fluctuations, we derive the effective elastic modulus $\Gamma \left(q\right)$ as a function of wave vector q. We show that the modulus in the long-wavelength limit ($q\to 0$) remains identically equal to a bare modulus protected by perfect ionic screening. In contrast, the modulus in the short-wavelength limit ($q\to \infty$) softens as the electrostatic-elastic coupling strength $\xi$ increases, vanishing at a critical value $\xi =1$. For $\xi >1$, the fluctuation spectrum exhibits a negative eigenvalue for all wave vectors q larger than a critical (effective screening) wave vector $q_c$, signaling an ultraviolet instability of the uniform phase. In a real colloidal crystal, this divergence is regulated by the discrete lattice cutoff $q_{\max }\sim \pi /a$, confining the physical instability to a finite band $q_c<q<q_{\max }$. The macroscopic limit $q\to 0$ remains unconditionally stable for all $\xi$. The transition at $\xi =1$ thus marks the onset of short-wavelength mechanical failure, while macroscopic elastic stiffness remains intact. Our analysis clarifies the proper physical interpretation of the minimal coupling model and provides a consistent picture of how non-DLVO interactions can drive local structural collapse in charged colloidal crystals. Full article

We study indoor visible light positioning (VLP) in a fully three-dimensional setting under thermal noise and signal-dependent shot noise. Existing shot-noise-aware least-squares (LS) VLP methods largely rely on a fixed receiver height, so their key simplifications break down when the vertical coordinate is unknown. We derive a 3D likelihood-inspired surrogate, lift the nonlinear geometry into a weighted LS form, and add a geometry-aware regularizer that restores approximate consistency among redundant lifted variables. To make the iterative solver reliable in sparse layouts, we introduce room-feasible projection, consistent pilot reconstruction, clipped frozen weights, and damped objective-accepted updates. Each inner step remains a closed-form 4 × 4 linear solve. In deterministic synthetic simulations of an 8 × 8 × 3 m room with layouts containing 4, 9, and 16 light-emitting diodes (LEDs), high- and low-power regimes, and 400 test samples per scenario, the stabilized solver removes the catastrophic sparse-layout failures observed for the tested LS-family baselines and retains a 14×–23× runtime advantage over the nonlinear baseline. The nonlinear baseline often attains lower mean error in sparse and moderate layouts; the contribution here is the resulting speed–tail-control trade-off within the fixed-orientation line-of-sight model. The resulting estimator is intended for synthetic 3D VLP simulations; hardware validation is outside the present study. Full article

Gold’s price reflects currency, opportunity-cost, and safe-haven channels whose strength shifts across regimes, motivating an empirical, data-driven forecasting approach. This study develops a monthly gold price forecasting system for ASM sales-timing decisions in Peru (January 2020–June 2026) using macro-financial predictors including a geopolitical risk index and three U.S. monetary indicators, none of which were Granger-causal and were therefore excluded from the production set. After confirming non-stationarity and Johansen cointegration (four vectors), thirty-two model-feature-set combinations, including Elastic Net, Bayesian Ridge, and a PCA factor, were compared under strict temporal validation with bounded hyperparameter search. The selected model, Ridge regression on the CONTROL feature set, achieved a cross-validation MAPE of 2.29% and test MAPE of 3.62% (official)/3.15% (extended sensitivity window). It was benchmarked against random walk, historical mean, and exponential smoothing and evaluated via the Diebold–Mariano, Clark–West, encompassing, and Model Confidence Set tests (low-power caveats given the small sample). A dual-horizon Monte Carlo simulation, robust to heavy-tailed shocks, projected USD 4482/oz (December 2026) and USD 5106/oz (December 2027). A sales-timing backtest showed a statistically significant result (−0.67%) versus a passive strategy, indicating calibrated price information alone does not yet yield a reliable trading edge, supporting the model’s role as decision support rather than an autonomous trading signal. Full article

Background: Depression-related liability is frequently accompanied by reduced physical function, yet the shared genetic architecture linking mood-related traits and physical-function decline remains incompletely characterized. Methods: We applied genomic structural equation modeling to European-ancestry GWAS summary statistics for five constituent phenotypes: depressive symptoms, depression diagnosis, grip strength, appendicular lean mass, and walking pace. A Depression–Physical Function shared genetic factor was constructed as a cross-trait genetic covariance dimension and evaluated using LDSC-based validation and leave-one-trait-out sensitivity analyses. We then performed factor GWAS, FUMA locus annotation, Bayesian fine-mapping, MAGMA gene-based analysis, transcriptome-wide association analysis, pathway enrichment, CELLECT/MAGMA cell-type specificity analysis, partitioned heritability analysis, and gsMap spatial transcriptomic mapping. Results: The shared factor showed good model fit and retained 755,397 quality-controlled variants for downstream analysis. The factor was positively genetically correlated with depression-related traits and negatively correlated with physical-function-related traits. FUMA identified 245 genome-wide significant SNPs, 44 lead SNPs, and 38 genomic risk loci, with 127 positional mapped genes. Fine-mapping prioritized one high-confidence locus. MAGMA identified 19 Bonferroni-significant genes and 326 FDR-significant genes, while TWAS identified 322 FDR-significant expression-associated genes. Integrating FUMA positional mapping, MAGMA gene-level association and TWAS expression-level association prioritized eight convergent genes: TMEM106B, CENPW, DRD2, LRFN5, NCAPG, DCAF16, SGIP1, and FAM120A. Functional enrichment highlighted postsynaptic structure, neuron spine, synaptic plasticity, and synapse organization. CELLECT/MAGMA prioritized brain non-myeloid neurons and glial populations, with additional endocrine-metabolic and immune-hematopoietic signals. Spatial transcriptomic mapping localized top signals to brain and spinal cord regions in the embryonic neuro-muscle reference. Partitioned heritability analysis showed enrichment in conserved, intronic, promoter, and chromatin-related genomic annotations. Conclusions: These findings support a shared polygenic covariance dimension linking depression-related liability with reduced physical-function-related genetic propensity. Downstream analyses prioritized candidate loci, genes, and biological contexts, with enrichment patterns consistent with neuronal, synaptic, and regulatory genomic processes. Full article

The Achilles tendon, the largest and strongest tendon in the human body, exhibits dynamic adaptive changes in its structure and function through mechanobiological regulation. This review synthesizes the dual regulatory effects of mechanical loading on Achilles tendon homeostasis and pathology: Moderate mechanical stimulation activates integrin-mediated signaling pathways (including PI3K/Akt and MAPK/ERK cascades), promoting tenocyte proliferation/differentiation, collagen biosynthesis, and orderly remodeling of extracellular matrix (ECM), thereby enhancing tendon stiffness, elastic modulus, and ultimate tensile strength. Conversely, chronic overload or disuse conditions induce collagen disorganization, aberrant matrix metalloproteinase (MMP) expression, and inflammatory cascades, creating a predisposition to tendinopathy and degenerative disorders. Emerging evidence highlights the critical role of mechanotransduction in injury repair, with early-stage progressive loading regimens demonstrating enhanced healing outcomes through optimized ECM metabolism and biomechanical signal propagation. Clinically, individualized load management strategies, including blood flow restriction training and biomaterial-assisted mechanomodulation, show promise in injury prevention and rehabilitation. Future research integrating multi-omics approaches with intelligent load-monitoring technologies may clarify mechanobiological coupling mechanisms and facilitate precision interventions for Achilles tendon disorders. Full article

Tectonic coal, highly fragmented by geological stresses, cannot be sampled as intact specimens, making remolded (briquette) samples essential for mechanical testing. However, remolding conditions, particle grading, molding pressure, and moisture content, are typically selected empirically, lacking objective evaluation criteria. This study proposes an acoustic emission (AE)-based multi-parameter framework to optimize these conditions. Using an orthogonal design (three factors at three levels), we prepared remolded tectonic coal samples with varying gradings (1:1:1, 1:4:1, 1:8:1), pressures (15 MPa, 20 MPa, 25 MPa), and moisture contents (8%, 10%, 12%). Uniaxial compression tests were conducted, and five evaluation parameters were extracted: compressive strength, cumulative AE count, cumulative AE energy, energy conversion ratio k, and shear crack proportion r. Results show that AE parameters effectively reflect remolding quality in terms of signal activity, damage mode, and energy efficiency. Grading and pressure dominate cumulative AE metrics, while moisture content and pressure control k and r. Increasing medium-particle proportion or pressure enhances AE activity, k, and r; increasing moisture content suppresses them. The optimal remolding conditions for the studied coal are 1:8:1 grading, 25 MPa pressure, and 8% moisture content, with different condition priority sequences depending on optimization goals. This AE-based approach provides an objective, quantitative tool for tectonic coal remolding optimization, benefiting subsequent mechanical and permeability studies. Full article

Interpersonal synchrony is a time-dependent coordination pattern in which interacting partners’ body movements become temporally aligned. This study frames interpersonal synchrony as a human motion analysis problem and presents Synchrony Vision, an RGB-D sensor-based system for real-time monitoring and event-level analysis of interpersonal motion synchrony in free dialog. The system transforms Kinect-derived skeletal positions into joint acceleration signals, applies sensor-specific conditioning, detects movement peaks, and estimates event-level phase differences between two participants within a ±1.0 s window. The operator-facing interface supports live RGB-D monitoring, acceleration visualization, joint selection, millisecond-scale phase-difference histograms, four synchrony metrics (Frequency, Lead–lag, Width, and Strength), and exportable acceleration, timestamp, peak-pairing, and summary artifacts. We evaluated the deployed pipeline on 25 Kinect-tracked dyads engaged in unconstrained conversation. Across 200.6 min comprising 245,835 frames, the system detected 2681 synchrony events. The observed event rate exceeded circular-surrogate baselines, and dyad rankings remained stable across reasonable parameter settings. Motion Energy Analysis-style cross-correlation on the same acceleration signals also confirmed above-chance synchrony but produced different dyad rankings. These findings show that RGB-D skeletal sensing can extend human motion analysis from individual movement capture to transparent, event-level quantification of interpersonal coordination. Full article

Robust underwater target localization is essential for marine sensing and monitoring. However, RSS-based localization is affected by ray bending caused by sound-speed variations, anchor-node position perturbations, and non-line-of-sight (NLOS) propagation biases, resulting in a nonlinear and noise-sensitive estimation problem. To address these challenges, this paper proposes a Quantum-Inspired Animated Oat Optimization (QIAOO)-based localization algorithm for stratified underwater acoustic environments. First, a multi-error-coupled RSS localization model is established by jointly considering acoustic ray propagation effects, anchor-node perturbations, and NLOS propagation biases. Subsequently, the QIAOO algorithm is developed to solve the resulting nonlinear optimization problem, where quantum-inspired initialization, elite opposition-based learning, and a dynamic Cauchy–Gaussian mutation mechanism are incorporated to enhance search diversity and convergence performance. In addition, the Cramér–Rao Lower Bound (CRLB) is derived under the same localization model to provide a theoretical performance benchmark. Simulation results demonstrate that the proposed method outperforms benchmark algorithms in terms of localization accuracy and robustness under various scenarios. Full article