Search Results (711)

Many vertebrates have evolved resistance to snake venom as a result of coevolutionary chemical arms races. In Australian skinks (family Scincidae), who often encounter venomous elapid snakes, the frequency, diversity, and molecular basis of venom resistance have been unexplored. This study investigated the evolution of neurotoxin resistance in Australian skinks, focusing on mutations in the muscle nicotinic acetylcholine receptor (nAChR) α1 subunit’s orthosteric site that prevent pathophysiological binding by α-neurotoxins. We sampled a broad taxonomic range of Australian skinks and sequenced the nAChR α1 subunit gene. Key resistance-conferring mutations at the toxin-binding site (N-glycosylation motifs, proline substitutions, arginine insertions, changes in the electrochemical state of the receptor, and novel cysteines) were identified and mapped onto the skink organismal phylogeny. Comparisons with other venom-resistant taxa (amphibians, mammals, and reptiles) were performed, and structural modelling and binding assays were used to evaluate the impact of these mutations. Multiple independent origins of α-neurotoxin resistance were found across diverse skink lineages. Thirteen lineages evolved at least one resistance motif and twelve additional motifs evolved within these lineages, for a total of twenty-five times of α-neurotoxic venoms resistance. These changes sterically or electrostatically inhibit neurotoxin binding. Convergent mutations at the orthosteric site include the introduction of N-linked glycosylation sites previously known from animals as diverse as cobras and mongooses. However, an arginine (R) substitution at position 187 was also shown to have evolved on multiple occasions in Australian skinks, a modification previously shown to be responsible for the Honey Badger’s iconic resistance to cobra venom. Functional testing confirmed this mode of resistance in skinks. Our findings reveal that venom resistance has evolved extensively and convergently in Australian skinks through repeated molecular adaptations of the nAChR in response to the enormous selection pressure exerted by elapid snakes subsequent to their arrival and continent-wide dispersal in Australia. These toxicological findings highlight a remarkable example of convergent evolution across vertebrates and provide insight into the adaptive significance of toxin resistance in snake–lizard ecological interactions. Full article

Parkinson’s disease (PD), a progressive neurodegenerative disorder, imposes growing clinical and socioeconomic burdens worldwide. Despite landmark discoveries in dopamine biology and α-synuclein pathology, translating mechanistic insights into effective, personalized interventions remains elusive. Recent advances in molecular profiling, neuroimaging, and computational modeling have broadened the understanding of PD as a multifactorial systems disorder rather than a purely dopaminergic condition. However, critical gaps persist in diagnostic precision, biomarker standardization, and the translation of bench side findings into clinically meaningful therapies. This review critically examines the current landscape of PD research, identifying conceptual blind spots and methodological shortfalls across pathophysiology, clinical evaluation, trial design, and translational readiness. By synthesizing evidence from molecular neuroscience, data science, and global health, the review proposes strategic directions to recalibrate the research agenda toward precision neurology. Here I highlight the urgent need for interdisciplinary, globally inclusive, and biomarker-driven frameworks to overcome the fragmented progression of PD research. Grounded in the Accelerating Medicines Partnership-Parkinson’s Disease (AMP-PD) and the Parkinson’s Progression Markers Initiative (PPMI), this review maps shared biomarkers, open data, and patient-driven tools to faster personalized treatment. In doing so, it offers actionable insights for researchers, clinicians, and policymakers working at the intersection of biology, technology, and healthcare delivery. As the field pivots from symptomatic relief to disease modification, the road forward must be cohesive, collaborative, and rigorously translational, ensuring that laboratory discoveries systematically progress to clinical application. Full article

Type 2 diabetes mellitus (T2DM) demands safer and more effective therapies to control postprandial hyperglycemia. Here, we report the synthesis and in vitro evaluation of ten salicylic acid-derived Schiff base derivatives (4a–4j) as α-glucosidase inhibitors. Compounds 4e, 4g, 4i, and 4j exhibited potent enzyme inhibition, with IC₅₀ values ranging from 14.86 to 18.05 µM—substantially better than acarbose (IC₅₀ = 45.78 µM). Molecular docking and 500 ns molecular dynamics simulations revealed stable enzyme–ligand complexes driven by π–π stacking, halogen bonding, and hydrophobic interactions. Density Functional Theory (DFT) calculations and molecular electrostatic potential (MEP) maps highlighted key electronic factors, while ADMET analysis confirmed favorable drug-like properties and reduced nephrotoxicity. Structure–activity relationship (SAR) analysis emphasized the importance of halogenation and aromaticity in enhancing bioactivity. Full article

The enzyme ABH2, one of nine human DNA dioxygenases of the AlkB family, belongs to the superfamily of Fe(II)/α-ketoglutarate-dependent dioxygenases and plays a crucial role in the direct reversal repair of nonbulky alkyl lesions in DNA nucleobases. ABH2 has broad substrate specificity, directly oxidizing DNA damages such as N¹-methyladenine, N³-methylcytosine, 1,N⁶-ethenoadenine, 3,N⁴-ethenocytosine, and a number of others. In our investigation, we sought to uncover the subtleties of the mechanisms governing substrate specificity in ABH2 by focusing on several critical amino acid residues situated in its active site. To gain insight into the function of this enzyme, we performed a functional mapping of its active site region, concentrating on pivotal residues, participating in forming a damaged binding pocket of the enzyme (Val99 and Ser125), as well as the residues directly involved in interactions with damaged bases, namely Arg110, Phe124, Arg172, and Glu175. To support our experimental data, we conducted a series of molecular dynamics simulations, exploring the interactions between the ABH2 mutant forms, bearing corresponding substitutions and DNA substrates, and harboring various types of methylated bases, specifically N¹-methyladenine or N³-methylcytosine. The comparative studies revealed compelling data indicating that alterations in most of the studied amino acid residues significantly influence both the binding affinity of the enzyme for DNA and its catalytic efficiency. Intriguingly, the findings suggest that the mutations impact the catalytic activity of ABH2 to a greater extent than its ability to associate with DNA strands. Collectively, these results show how changes to the active site affect molecular dynamics and reaction kinetics, improving our understanding of the substrate recognition process in this pivotal enzyme. Full article

Tissue hypoxia is commonly observed in head cancers and contributes to both molecular and functional changes in tumour cells. It is known to stimulate erythropoiesis, angiogenesis, and metabolic alterations within tumour cells. Glioblastoma, a type of brain tumour, is characterized by rapid proliferation and aggressive growth. Recent studies have indicated that natural products may hold potential as components of cancer therapy. Among these, Polish propolis and its active compound, quercetin, have demonstrated promising anti-cancer properties. The aim of this study was to evaluate the concentrations of selected cytokines—specifically IL-6, IL-9, vascular endothelial growth factor (VEGF), platelet-derived growth factor (PDGF-BB), interferon gamma-induced protein 10 (IP-10), and monocyte chemoattractant protein-1 (MCP-1)—produced by astrocytes of the CCF-STTG1 cell line. The cytotoxic effects of ethanolic extract of propolis (EEP) and quercetin were assessed using the MTT assay. Astrocytes were stimulated with lipopolysaccharide (LPS, 200 ng/mL) and/or IFN-α (100 U/mL), followed by treatment with EEP or quercetin (25–50 µg/mL) under hypoxic conditions for two hours. Cytokine concentrations were measured using the xMAP Luminex Multiplex Immunoassay and the Multiplex Bead-Based Cytokine Kit. Our study demonstrated that Polish propolis and its component quercetin modulate the tumour microenvironment in vitro, primarily by altering the levels of specific cytokines. The HCA analysis revealed that IL-6 and MCP-1 formed a distinct cluster at the highest linkage distance (approximately 100% of Dmax), suggesting that their expression patterns are significantly different from those of the other cytokines and that they are more similar to each other than to the rest. PCA analysis showed that EEP-PL (50 μg/mL) with IFN-α and EEP-PL (50 μg/mL) with LPS exert similar activities on cytokine secretion by astrocytes. Similar effects were demonstrated for EEP-PL 50 μg/mL + LPS + IFN-α, EEP-PL 25 μg/mL + IFN-α and EEP-PL 25 μg/mL + LPS + IFN-α. Our findings suggest that Polish propolis and quercetin may serve as promising natural agents to support the treatment of stage IV malignant astrocytoma. Nonetheless, further research is needed to confirm these results. Full article

Tomicus yunnanensis Kirkendall and Faccoli, a native bark beetle species and key pest of Pinus yunnanensis Franch. in southwestern China, relies on host-derived volatile organic compounds (VOCs) for host selection. To unravel these mechanisms, we collected VOCs from P. yunnanensis trunks across four infestation stages (healthy, early-infested, weakened, near-dead) using dynamic headspace sampling. Chemical profiling via gas chromatography–mass spectrometry (GC-MS) identified 51 terpenoids, with α-pinene as the most abundant component. VOC profiles differed markedly between healthy and early-infested trees, while gradual shifts in compound diversity and abundance occurred from the weakened to near-dead stages. Bioactive compounds were screened using gas chromatography–electroantennographic detection (GC-EAD) and a Y-tube olfactometer. Electrophysiological responses in T. yunnanensis were triggered by α-pinene, β-pinene, 3-carene, 2-thujene, and 4-allylanisole. Behavioral tests revealed that α-pinene, 3-carene, and 2-thujene acted as attractants, whereas β-pinene and 4-allylanisole functioned as repellents. These results indicate that infestation-induced VOC dynamics guide beetle behavior, with attractants likely promoting host colonization during early infestation and repellents signaling deteriorating host suitability in later stages. By mapping these chemical interactions, our study identifies potential plant-derived semiochemicals for targeted pest management. Integrating these compounds with pheromones could enhance the monitoring and control strategies for T. yunnanensis, offering ecologically sustainable solutions for pine ecosystems. Full article

The analytical solutions for groundwater inflow into tunnels are usually developed under the condition of circular tunnels. However, real-world tunnels often have non-circular cross-sections, such as arched, lens-shaped, or egg-shaped profiles. Accurately assessing water inflow for these diverse tunnel shapes remains challenging. To address this gap, this study developed a closed-form analytical solution for water inflow into a deeply buried arched tunnel using the conformal mapping method. When the tunnel circumference degenerates to a circle, the analytical solution degenerates to the widely used Goodman’s equation. The solution also showed excellent agreement with numerical simulations carried out using COMSOL. Based on the analytical solution, the impact of various factors on water inflow Q was further discussed: (1) Q decreases as the boundary distance $D^{\ast }$ increases. And the boundary inclination angle ($\alpha -\pi /2$) significantly affects Q only when the boundary is close to the tunnel ($D^{\ast }<20$); (2) Q increases quickly with the upper arc radius $r_1^{\ast }$, while it shows minimal variation with the change in the lower arc radius $r_2^{\ast }$. The findings provide a theoretical foundation for characterizing water inflow into arched tunnels, thereby supporting improved tunnel planning and grouting system design. Full article

We present a Doppler tomography study of the Be star HD 698, recently resolved via interferometry as a post-mass-transfer binary system consisting of a Be star and a stripped, pre-subdwarf companion. Based on 76 high-resolution optical spectra obtained between 2014 and 2023, we analyze the H$\alpha$ and H$\beta$ emission lines and apply Doppler tomography to map the structure of the circumstellar disk. The H$\alpha$ line reveals an asymmetric, multi-component velocity distribution, with an emission feature closely following the orbital motion of the companion. V/R variations in both H$\alpha$ and H$\beta$ lines are phase-locked with the companion’s orbital motion, indicating a tidally induced disk asymmetry. We discuss possible origins of the companion-centered H$\alpha$ emission, including a circumsecondary disk, a transient mass-transfer stream, and stellar wind. Full article

To address the challenges of weak fault features and strong non-stationarity in early-stage vibration signals, this study proposes a novel fault diagnosis method combining enhanced variational mode decomposition (VMD) with a structurally improved GoogLeNet. Specifically, an improved wild horse optimizer (IWHO) with tent chaotic mapping is employed to automatically optimize critical VMD parameters, including the number of modes K and the penalty factor α, enabling precise decomposition of non-stationary signals to extract weak fault features. The vibration signal is decomposed, and the top five intrinsic mode functions (IMFs) are selected based on the kurtosis criterion. Time–frequency features are then extracted from these IMFs and input into a modified GoogLeNet classifier. The GoogLeNet structure is improved by replacing standard n × n convolution kernels with cascaded 1 × n and n × 1 kernels, and by substituting the ReLU activation function with a parameterized TReLU function to enhance adaptability and convergence. Experimental results on two public rolling bearing datasets demonstrate that the proposed method effectively handles non-stationary signals, achieving 99.17% accuracy across four fault types and maintaining over 95.80% accuracy under noisy conditions. Full article

We develop a symmetry-based variational theory that shows the coarse-grained balance of work inflow to heat outflow in a driven, dissipative system relaxed to the golden ratio. Two order-2 Möbius transformations—a self-dual flip and a self-similar shift—generate a discrete non-abelian subgroup of $\mathrm{PGL}(2,\mathbb{Q}\left(\sqrt{5}\right))$. Requiring any smooth, strictly convex Lyapunov functional to be invariant under both maps enforces a single non-equilibrium fixed point: the golden mean. We confirm this result by (i) a gradient-flow partial-differential equation, (ii) a birth–death Markov chain whose continuum limit is Fokker–Planck, (iii) a Martin–Siggia–Rose field theory, and (iv) exact Ward identities that protect the fixed point against noise. Microscopic kinetics merely set the approach rate; three parameter-free invariants emerge: a $62\%:38\%$ split between entropy production and useful power, an RG-invariant diffusion coefficient linking relaxation time and correlation length ${\mathcal{D}}_{\alpha }={\xi }^z/\tau$, and a $\vartheta ={45}^{\circ }$ eigen-angle that maps to the golden logarithmic spiral. The same dual symmetry underlies scaling laws in rotating turbulence, plant phyllotaxis, cortical avalanches, quantum critical metals, and even de-Sitter cosmology, providing a falsifiable, unifying principle for pattern formation far from equilibrium. Full article

We explore the realm of uncertainty theory by investigating diverse notions of convergence and statistical convergence concerning complex uncertain sequences. Complex uncertain variables can be described as measurable functions mapping from an uncertainty space to the set of complex numbers. They are employed to represent and model complex uncertain quantities. We introduce the concept of lacunary almost statistical convergence of order $\alpha$$(0<\alpha \le 1)$ for complex uncertain sequences, examining various aspects of uncertainty such as distribution, mean, measure, uniformly almost sure convergence and almost sure convergence. Additionally, we establish connections between the constructed sequence spaces by providing illustrative instances. Importantly, lacunary almost statistical convergence provides a flexible framework for handling sequences with irregular behavior, which often arise in uncertain environments with imprecise data. This makes our approach particularly useful in practical fields such as engineering, data modeling and decision-making, where traditional deterministic methods are not always applicable. Our approach offers a more flexible and realistic framework for approximating functions in uncertain environments where classical convergence may not apply. Thus, this study contributes to approximation theory by extending its tools to settings involving imprecise or noisy data. Full article

Background: Current cosmological fits typically assume a direct relation between cosmic time (t) and the scale factor ($a\left(t\right)$), yet this ansatz remains largely untested across diverse observations. Objectives: We (i) test whether a single power-law scaling ($a\left(t\right)\propto t^{\alpha }$) can reproduce late- and early-time cosmological data and (ii) explore whether a dynamically evolving ($\alpha \left(t\right)$), modeled as a scalar–tensor field, naturally induces directional asymmetry in cosmic evolution. Methods: We fit a constant-$\alpha$ model to four independent datasets: 1701 Pantheon+SH0ES supernovae, 162 gamma-ray bursts, 32 cosmic chronometers, and the Planck 2018 TT spectrum (2507 points). The CMB angular spectrum is mapped onto a logarithmic distance-like scale ($\mu ={log}_{10}{D}_{\ell }$), allowing for unified likelihood analysis. Each dataset yields slightly different preferred values for $H_0$ and $\alpha$; therefore, we also perform a global combined fit. For scalar–tensor dynamics, we integrate $\alpha \left(t\right)$ under three potentials—quadratic, cosine, and parity breaking (${\alpha }^{3}sin\alpha$)—and quantify directionality via forward/backward evolution and Lyapunov exponents. Results: (1) The constant-$\alpha$ model achieves good fits across all datasets. In combined analysis, it yields $H_0\simeq 70\mathrm{km}{\mathrm{s}}^{-1}{\mathrm{Mpc}}^{-1}$ and $\alpha \simeq 1.06$, outperforming $\Lambda$CDM globally ($\Delta \mathrm{AIC}\simeq 401254$), though $\Lambda$CDM remains favored for some low-redshift chronometer data. High-redshift GRB and CMB data drive the improved fit. Numerical likelihood evaluations are approximately three times faster than for $\Lambda$CDM. (2) Dynamical $\alpha \left(t\right)$ models exhibit time-directional behavior: under asymmetric potentials, forward evolution displays finite Lyapunov exponents (${\lambda }_L\sim {10}^{-3}$), while backward trajectories remain confined (${\lambda }_L<0$), realizing classical arrow-of-time emergence without entropy or quantum input. Limitations: This study addresses only homogeneous background evolution; perturbations and physical derivations of potentials remain open questions. Conclusions: The time-scaling approach offers a computationally efficient control scenario in cosmological model testing. Scalar–tensor extensions naturally introduce classical time asymmetry that is numerically accessible and observationally testable within current datasets. Code and full data are available. Full article

Background/Objectives: In the context of facial surgery, particularly reconstructive procedures, sensory recovery is a critical yet often underexplored aspect of functional rehabilitation. Sensory-motor recovery can be considered a key marker of integration following reconstructive surgery. Among sensory modalities, discriminative sensitivity is typically the last to recover, making its evaluation particularly relevant. While established norms for hand sensitivity exist in the literature, there is a paucity of data regarding facial sensitivity. The objective of this study was to evaluate the discriminative sensitivity of the face in a population of healthy women aged 45–60 years. Methods: A total of 20 healthy women were included between January and March 2013. Participants had no history of facial pathologies or trauma. Discriminative sensitivity was measured using the Disk-Criminator™ device across eight facial zones. A detailed mapping of the tested areas was performed. Data obtained were compared with the existing literature. Statistical analyses included Shapiro–Wilk tests for normality, followed by Student’s t-tests for group comparisons. To account for small sample size and verify robustness, non-parametric Mann–Whitney U tests were also performed. Adjustment for multiple comparisons was applied using the Bonferroni correction (adjusted α = 0.0125). Results: The mean age of participants was 52.3 years (±4.0 years). Discrimination threshold values ranged from 2.9 to 14.3 mm. Comparison with existing studies showed no significant age-related differences in zone 2R (cheek) and zone 8 (lower lip), suggesting stable sensitivity in these regions across adulthood. However, a significant decline in sensitivity with age was observed only in zone 1R (forehead), with a p-value < 0.001 after Bonferroni correction. Conclusions: We established a reference framework for cutaneous discriminative sensitivity across eight facial zones. These norms can serve as a baseline for the assessment and monitoring of patients with facial pathologies. Furthermore, our findings contribute to a better understanding of age-related sensory changes. Full article

The design of asphalt mixture has, for a long time, been an empirical and proof process, causing the mismatch between material design and pavement structure design. To enhance the rationality of asphalt pavement design, this study seeks a path to bridge the gap between asphalt mixture modulus and structural behavior. Firstly, pavement models with different base rigidities, including cement concrete base, cement-treated granular base, and granular base, were constructed to calculate the pavement responses under different dynamic modulus master curve parameters. The influence of master curve parameters on critical pavement responses was identified by the response surface method (RSM). Furthermore, a Whale Optimization Algorithm–Back Propagation (WOA-BP) artificial-neural-network-based pavement response prediction model was established. Then, a database mapping over 100 thousand pavement responses and dynamic modulus master curve parameters was built for determining the dynamic modulus master curve parameters by optimizing the pavement responses. The results show that the impacts of dynamic modulus master curve parameters on critical pavement responses depend on pavement structures. In general, parameter δ has the greatest impact, followed by α, while the effects of β and γ are relatively small. The Artificial Neural Network (ANN) performance prediction model, optimized by the WOA algorithm, has a high accuracy. The methodology for determining the dynamic modulus master curve parameter based on the critical response of pavement was successfully implemented. The findings can bridge the gap between material design and structure design of asphalt pavement and provide a basis for more accurate and reasonable asphalt pavement design. Full article

Unmanned Aerial Vehicles (UAVs) have become powerful tools for high-resolution, quantitative remote sensing in ecological and environmental studies. In this study, we present a novel approach to accurately mapping and estimating the biomass of Suaeda salsa using UAV-based visible-light imagery combined with hue angle inversion modeling. By integrating diffuse reflectance standard plates into the flight protocol, we converted RGB pixel values into reflectance and derived hue angle metrics with enhanced radiometric accuracy. A hue angle cutoff threshold of 249.01° was identified as the optimal cutoff to distinguish Suaeda salsa from the surrounding land cover types with high confidence. To estimate biomass, we developed an exponential inversion model based on hue angle data calibrated through extensive field measurements. The resulting model—Biomass = 3.57639 × 10⁻¹⁵ × e^0.12201×α—achieved exceptional performance (R² = 0.99696; MAPE = 3.616%; RMSE = 0.02183 kg/m²), indicating strong predictive accuracy and robustness. This study highlights a cost-effective, non-destructive, and scalable method for the real-time monitoring of coastal vegetation, offering a significant advancement in remote sensing applications for wetland ecosystem management. Full article