Search Results (5,365)

Background: The insulin-like growth factor 2 receptor (IGF-2R), also known as the cation-independent mannose 6-phosphate receptor (CI-M6PR), is emerging as a critical receptor for brain function and disease. IGF-2R, in fact, plays a key role in long-term memory, and its activation by several ligands shows beneficial effects in multiple neurodevelopmental and neurodegenerative disease models. Thus, its targeting is very promising for neuropsychiatric therapeutic interventions. IGF-2R’s main known functions are transport of lysosomal enzymes and regulation of developmental tissue growth, but in the brain, it also controls learning-dependent protein synthesis underlying long-term memory. However, little is known about this receptor in brain cells, including its cell-type-specific and subcellular expression. Methods: We conducted a comprehensive investigation to comparatively assess IGF-2R protein levels in different brain cell types across various brain regions in adult male C57BL/6J mice using dual and multiplex immunofluorescent staining with cell-type-specific markers. The IGF-2R protein distribution was also compared with Igf2r mRNA expression in publicly available single-cell RNA sequencing databases. Results: A ranking of IGF-2R levels in the soma of various cell types in the hippocampus and cortical regions revealed that the highest enrichment is, by far, in excitatory and inhibitory neurons, followed by vascular mural cells and subpopulations of oligodendrocyte lineage cells, with low to undetectable levels in astrocytes, microglia, vascular endothelial cells, and perivascular fibroblasts. High levels of IGF-2R were also found in ependymal cells, choroid plexus epithelial cells, and a subpopulation of meningeal fibroblast-like cells. IGF-2R was found in dendritic and putative axonal compartments throughout the brain, with particularly high levels in the stratum lucidum. The receptor’s protein distribution aligned with that of the mRNA in mouse brain databases. Conclusions: These results suggest that IGF-2R-mediated functions in the brain vary across different cell types and subcellular compartments, with the most active roles in specific subpopulations of neurons, mural cells, ependymal cells, meningeal cells, and cells of the oligodendrocyte lineage. This study advances our understanding of IGF-2R’s distribution in the brain, which is essential for formulating new hypotheses about its functions and therapeutic targeting. Full article

This study compares the performance of empirical and hybrid deep learning (DL) models in estimating daily potential evapotranspiration (PET) in the Nakdong River Basin (NRB), South Korea, with the FAO-56 Penman–Monteith (PM) method as a reference. Two empirical models, Priestley–Taylor (P-T) and Hargreaves–Samani (H-S), and two DL models, a standalone Long Short-Term Memory (LSTM) network and a hybrid Convolutional Neural Network Bidirectional LSTM with an attention mechanism, were trained on a meteorological dataset (1973–2024) across 13 meteorological stations. Four input combinations (C1, C2, C3, and C4) were tested to assess the model’s robustness under varying data availability conditions. The results indicate that empirical models performed poorly, with a basin-wide RMSE of 5.04–5.79 mm/day and negative NSE (−10.37 to −13.99), and are therefore poorly suited to NRB. In contrast, DL models achieved significant improvements in accuracy. The hybrid CNN-BiLSTM Attention Mechanism (C1) produced the highest performance, with R² = 0.820, RMSE = 0.672 mm/day, NSE = 0.820, and KGE = 0.880, which was better than the standalone LSTM (R² = 0.756; RMSE = 0.782 mm/day). The generalization of heterogeneous climates was also verified through spatial analysis, in which the NSE at the station level consistently exceeded 0.70. The hybrid DL model was found to be highly accurate in representing the temporal variability and seasonal patterns of PET and is therefore more suitable for operational hydrological modeling and water-resource planning in the NRB. Full article

We present a multiscale model of taste that is both biophysically faithful and computationally efficient, enabling end-to-end simulation from receptor transduction to network-level coding. The novelty lies in coupling Hodgkin–Huxley taste receptor cells with Goldman–Hodgkin–Katz ion currents and modality-specific receptors (T1R/T2R, ENaC), to an Izhikevich spiking network equipped with realistic glutamatergic synapses and spike-timing-dependent plasticity. Training combines spike synchrony and a genetic approach in order to reach both globally optimized network structure and biomorphic synaptic plasticity. This hybrid design yields distinct, sparse spiking “fingerprints” for taste qualities and mixtures, and provides a practical foundation for neuromorphic gustatory sensors that require real-time, energy-efficient operation. Full article

The increasingly stringent environmental requirements, as well as the tendency to achieve significant savings of energy products in HMA production processes, prompted researchers to investigate the possibility of reducing the moisture of the stone aggregate which is used in production of hot asphalt mixtures. The goal of this paper is to determine the effect of various drying parameters on the aggregate moisture loss. The parameters which were analyzed and observed in various combinations were selected on the basis of the production process of an asphalt plant, and they are as follows: the air flow speed (3.86 m/s, 4.53 m/s and 5.94 m/s), the drying temperature (basic temperatures 33.1 °C, 50.4 °C and 71.7 °C) and the time of exposure of the aggregate to drying (30, 45 and 60 s). In order to research the effect of reduction in moisture of the stone material, a laboratory model of a belt dryer (chamber with a cover) was conceived and made with a drying device that can control the air flow speed from 3.86 m/s to 6.32 m/s and the temperature, ranging from 33 °C to 110 °C. Tests were carried out in order to determine the moisture loss of different aggregate fractions, namely 0/2, 2/4, 4/8, 8/11, from the total (natural) moisture of fractions that are used as aggregate in the production of hot mix asphalt (HMA). In all, there were 162 samples of aggregate prepared and tested. Results showed that for different aggregate fractions, the ranges of the value of the moisture loss are considerably different and that they depend on the parameters of drying and the natural moisture of the aggregate. It was noticed that there was less moisture loss in fractions at a lower air flow speed (3.86 m/s) than there was at higher speeds, while the highest aggregate moisture loss was noticed at an air flow speed of 5.94 m/s. For all duration times of drying, regardless of the drying temperature or speed, it is noticed that, with the prolongation of the drying time, the aggregate moisture loss becomes more intense. The drying temperature directly affects the reduction in the aggregate moisture; the higher the air flow temperature is, the more significant the moisture loss is during drying of the aggregate. The results of the linear regression and the coefficient of determination R² indicate a very firm connection between the loss of the aggregate moisture and the duration of the drying time. From the obtained equations, it is possible to calculate the reduction in the aggregate moisture for different lengths of drying duration and different drying temperatures. Full article

Valorization of environmental waste into sustainable energy and value-added products offers a strategic pathway for advancing circular economic development and resource sustainability. In this study, waste cooking oil was converted into biodiesel using biogenically generated CaO, prepared thermally at 900 °C. The reaction process was modeled and optimized with a Taguchi orthogonal array L9(3⁴), considering four factors at three levels to yield nine experimental conditions. The model reliability was statistically validated through analysis of variance (ANOVA) at 95% confidence level (p < 0.05), achieving a high determination coefficient (R²) of 0.9965. The maximum biodiesel yield of 91.08% was obtained under the optimal conditions of the methanol to oil ratio of 15:1, a catalyst loading of 4.5 wt%, a reaction time of 90 min, a temperature of 65 °C, and a constant stirring speed of 650 rpm. The fuel property analysis confirmed compliance with international biodiesel and diesel standards). Economic evaluation of the process showed that integrating waste cooking oil with reusable seashell-derived catalysts enabled the production of high-quality biodiesel at R23.20 (~USD 1.39)/L, highlighting a sustainable and cost-competitive alternative to conventional feedstock. The study contributes to advancing waste-to-energy technologies and supports the transition towards a circular and sustainable energy future. Full article

The mechanical properties of cubic (c-BN) and wurtzite (w-BN) boron nitride structures were investigated and compared using density functional theory (DFT) with several exchange–correlation functionals. This research focuses on determining the bulk modulus ($\mathit{B}$) and, for the first time, the stretching bond force constants ($k_r$), two fundamental parameters that describe the intrinsic stiffness and elastic resistance of these BN structures. Despite their structural similarity with the same tetrahedral coordination between atoms, c-BN and w-BN exhibit subtle differences in bond strength and compressibility that have not been fully clarified at the atomistic level. By systematically analyzing the influence of hybrid and semi-local functionals, consistent relationship between structural configuration and the predicted $\mathit{B}$ and $k_r$ values of both c-BN and w-BN structures were established and compared. These findings not only validate DFT as a reliable approach for assessing the mechanical properties of BN polymorphs, but also offer key parameters for machine learning and advanced multiscale modeling. Therefore, this theoretical study contributes to understanding the origin of mechanical properties in BN structures and supports their design in applications where a particular hardness and stability are required. Full article

Aspartame, a widely used artificial sweetener, has been linked to various cancers, including kidney renal papillary cell carcinoma (KIRP). However, the molecular mechanisms underlying this association remain unclear. This study employed network toxicology and molecular docking to investigate potential mechanisms of aspartame-induced KIRP. Differentially expressed genes from TCGA were intersected with aspartame targets and KIRP-related genes, yielding 61 common targets. GO and KEGG analyses revealed enrichment in extracellular matrix degradation, signaling pathways, and immune microenvironment regulation. Univariate Cox regression identified 23 prognostically significant genes, from which multifactorial Cox regression with stepwise selection determined 8 core genes (APLNR, CYP2C19, EDNRA, KLK5, F2R, RAD51, AURKA, and TLR2). A risk model was constructed and validated through VIF analysis, Schoenfeld residual testing, and internal validation using a training–validation split. SHAP analysis identified EDNRA as the primary driver gene. Survival analysis demonstrated that the model effectively stratified KIRP patients, with risk score and tumor stage serving as independent prognostic factors. Molecular docking confirmed stable binding between aspartame and core target proteins. These findings provide mechanistic insights into aspartame-induced KIRP pathogenesis and establish a foundation for future experimental validation. Full article

In this study, a multi-objective optimization framework based on an artificial neural network (ANN) was developed for an inlet perforated distributor plate in a 24S24P 10 kWh cylindrical lithium-ion battery module using immersion cooling. A combined Newman, Tiedeman, Gu and Kim with Computational Fluid Dynamics (NTGK-CFD) model was used to generate a symmetrically designed space by varying the input variables, including hole size A (mm), hole spacing ΔH (mm), and coolant mass flow rate V_in (kg/s). A three-level full factorial design was used to generate 27 cases, then CFD simulations were performed to provide a training data for the ANN model to predict the output variables, including maximum temperature T_max, maximum temperature difference ΔT_max, and pressure drop ΔP. The results show that the ANN model provides a reliable predictive model, capable of reproducing the thermal-hydraulic behavior of the immersion-cooled battery module with high fidelity via correlation coefficients R of 0.997 for all three output variables. In addition, Pareto-based optimization shows designs that balance cooling efficiency and pumping power. The selected optimal solution maintains T_max within the optimal range at 37.97 °C while reducing ΔP by up to 44%, providing a practical solution for large-scale battery module thermal management in EVs. Full article

Spatially quantifying the soil carbon sequestration potential (SCSP) is crucial for targeting climate change mitigation strategies like carbon farming. However, static mapping approaches often fail by assuming that the drivers of soil organic carbon (SOC) are stationary. We hypothesized that the hierarchy of SOC controllers is fundamentally non-stationary, shifting from intrinsic stabilization capacity (pedology) in stable ecosystems to extrinsic flux kinetics (climate) in dynamic systems. We tested this by developing a land-use-specific (LULC; Cropland, Forest land, Grassland) ensemble machine learning (ML) framework to quantify the soil carbon saturation deficit (SCSD) across Croatia’s pedologically diverse landscape on 622 soil samples. The LULC-stratified ensemble models (SVM, RF, CUB) achieved moderate to good predictive accuracy under cross-validation (R² = 0.41–0.60). Crucially, the feature importance analysis (permutation MSE loss) proved our hypothesis: in Forest land, SOC was superiorly controlled by intrinsic capacity (Soil CEC, Soil pH), defining the mineralogical C-saturation “ceiling”; in Grasslands, control shifted to extrinsic C-input kinetics (Precipitation: Bio19, Bio12), which “fuel” the microbial carbon pump (MCP) via root exudation; and in Croplands, the model revealed a hybrid control, limited by remaining intrinsic capacity (CEC, Clay) but strongly influenced by C-loss kinetics (Temperature: Bio08), which regulates microbial carbon use efficiency (CUE). This study demonstrates that LULC-specific dynamic modeling is a prerequisite for accurately mapping SCSP. By identifying soils with both high intrinsic capacity (high CEC/Clay) and high degradation (high SCSD), our data-driven assessment provides a critical tool for spatially targeting carbon farming interventions for maximum climate mitigation return on investment (ROI). Full article

γ-Aminobutyric acid type A receptors (GABA_ARs) are pentameric ligand-gated ion channels mediating fast inhibitory neurotransmission in the mammalian brain. Although recent structural and kinetic studies have advanced understandings regarding their activation mechanisms, the molecular determinants coupling agonist binding to channel gating remain unclear. We investigated the contribution of the β₂E153 residue, located on loop B of the extracellular domain, to the activation of α₁β₂γ₂ GABA_ARs. Macroscopic and single-channel patch clamp recordings were used to characterize two β₂E153-mutants: charge reversal (β₂E153K) and hydrophobic substitution (β₂E153A). Both substitutions disrupted normal receptor kinetics, with β₂E153K selectively accelerating deactivation and β₂E153A affecting both deactivation and desensitization. Single-channel analysis showed that β₂E153A reduced open probability and mean open times, consistent with altered gating transitions inferred from kinetic modeling. Structural inspection suggested that β₂E153 forms electrostatic interactions with β₂K196 and β₂R207 to stabilize loop C and maintain the agonist-bound conformation. The disruption of this interaction likely destabilizes loop C, leading to weakened agonist binding and modified gating. Overall, our results identify β₂E153 as a key element in the long-range allosteric network linking the binding site to the channel gate in GABA_ARs. Full article

This work quantifies the environmental sensitivity of tartaric–sulfuric acid (TSA) anodized and sealed 6061 and 7075 aluminum. Five alloy–temper states (6061-T4, 6061-T62, 7075-T0, 7075-T62, and 7075-T73) were TSA-treated, pore sealed and then exposed for eight weeks (56 days) to ambient air, 11 wt.% NaCl brine, or a microbiological medium, with weekly +20 °C/−20 °C freeze–thaw cycles. Tensile tests assessing yield strength, ultimate strength, and elongation were conducted. Strength losses were modest in ambient conditions (<5%) but increased to ≈5–10% for yield and ≈2–9% for ultimate under saline and microbial conditions, particularly in the annealed 7075-T0 and peak-aged 7075-T62 states. Ductility was more sensitive, declining up to ≈30% for 6061-T4 and 6061-T62 in harsh media. Permutation-based inference within an additive screening model indicated that environmental exposure is strongly associated with the dominant share of the observed variability (R²_env ≈ 0.91–0.93 for yield, ultimate strength, and elongation), within the limits of the present dataset. These results suggest that freeze–thaw cycling, chloride exposure, and microbiological activity are consistent with the observed degradation trends. Over-aged 7075-T73 retained properties better than T62, highlighting the roles of temper and pore sealing quality in cold, saline, and microbiologically active service. Full article

We present a comprehensive mathematical analysis of a within-host dual-target HIV dynamics model, which explicitly incorporates the virus’s interactions with its two primary cellular targets: CD4${}^{+}$ T cells and macrophages. The model is formulated as a system of five nonlinear delay differential equations, integrating three distinct discrete time delays to account for critical intracellular processes such as the development of productively infected cells and the maturation of new virions. We first establish the model’s biological well-posedness by proving the non-negativity and boundedness of solutions, ensuring all trajectories remain within a feasible region. The basic reproduction number, ${\mathcal{R}}_0^d$, is derived using the next-generation matrix method and serves as a sharp threshold for disease dynamics. Analytical results demonstrate that the infection-free equilibrium is globally asymptotically stable (GAS) when ${\mathcal{R}}_0^d\le 1$, guaranteeing viral eradication from any initial state. Conversely, when ${\mathcal{R}}_0^d>1$, a unique endemic equilibrium emerges and is proven to be GAS, representing a state of chronic infection. These global stability properties are rigorously established for both the non-delayed and delayed systems using carefully constructed Lyapunov functions and functionals, coupled with LaSalle’s invariance principle. A sensitivity analysis identifies viral production rates ($p_1,p_2$) and infection rates (${\beta }_1,{\beta }_2$) as the most influential parameters on ${\mathcal{R}}_0^d$, while the viral clearance rate (m) and maturation delay (${\tau }_3$) have a suppressive effect. The model is extended to evaluate antiretroviral therapy (ART), revealing a critical treatment efficacy threshold ${ϵ}^{cr}$ required to suppress the virus. Numerical simulations validate all theoretical findings and further investigate the dynamics under varying treatment efficacies and maturation delays, highlighting how these factors can shift the system from persistence to clearance. This study provides a rigorous mathematical framework for understanding HIV dynamics, with actionable insights for designing targeted treatment protocols aimed at achieving viral suppression. Full article

Triboelectric nanogenerators (TENGs) offer intrinsically high open-circuit voltages in the kilovolt range; however, conventional diode rectifier interfaces clamp the voltage prematurely, restricting access to the high-energy portion of the mechanical cycle and preventing delivery-centric control. This work develops a unified physical basis for contact–separation (CS) TENGs by confirming the consistency of the canonical $V_{oc}$–$C_s$ relation with a dual-capacitor energy model and analytically establishing that both terminal voltage and storable electrostatic energy peak near maximum plate separation. Leveraging this insight, a self-powered gas-discharge-tube (GDT) rectifier bridge is devised to replace two diodes and autonomously trigger conduction exclusively in the high-voltage window without auxiliary bias. An inductive buffer regulates the current slew rate and reduces $I^{2}R$ loss, while the proposed topology realizes two decoupled power rails from a single CS-TENG, enabling simultaneous sensing/processing and actuation. A low-power microcontroller is powered from one rail through an energy-harvesting module and executes an operator-based nonlinear controller to regulate the actuator-side rail via a MOSFET–resistor path. Experimental results demonstrate earlier and higher-efficiency energy transfer compared with a diode-bridge baseline, robust dual-rail decoupling under dynamic loading, and accurate closed-loop voltage tracking with negligible computational and energy overhead. These findings confirm the practicality of the proposed self-powered architecture and highlight the feasibility of integrating operator-theoretic control into TENG-driven rectifier interfaces, advancing delivery-oriented power extraction from high-voltage TENG sources. Full article

In this study, new improved inhibitors of the viral enzyme 3-chymotrypsin-like protease (3CL^pro) were designed using structure-based drug design techniques in an effort to discover more effective treatment of coronavirus disease 2019 (COVID-19). Three-dimensional models of 3CL^pro–inhibitor complexes were prepared by in situ modification of the crystal structure of the submicromolar covalent inhibitor IPCL6 for a set of 25 known inhibitors with published inhibitory potencies (${IC}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}$). The QSAR model was prepared with a reasonable correlation between the calculated free energies of formation of the 3CL^pro-IPCL complex (∆∆G_com) and the experimentally determined activities ${IC}_{50}^{\mathrm{e}\mathrm{x}\mathrm{p}}$, which explained approximately 92% of the variation in the 3CL^pro inhibition data. A similar agreement was achieved for the QSAR pharmacophore model (PH4) built on the basis of the active conformations of the IPCL inhibitors bound at the active site of the 3CL^pro. The virtual combinatorial library of more than 567,000 IPCL analogues was screened in silico using the PH4 model and resulted in the identification of 39 promising analogues. The best inhibitors designed in this study show high predicted affinity for the 3CL^pro protease, as well as favourable predicted ADME properties. For the best new virtual inhibitor candidate IPCL 80-27-74-4, the inhibitory concentration ${IC}_{50}^{\mathrm{p}\mathrm{r}\mathrm{e}}$ was predicted equal to 0.8 nM, which represents a significant improvement in the inhibitory potency of known IPCLs. Ultimately, molecular dynamics simulations of the 12 newly designed top-scoring IPCL inhibitors demonstrated that the 3CL^pro–inhibitor complexes exhibited good structural stability, confirming the potential for further development of the designed IPCL analogues. Full article

This study addresses the critical challenge of permanent deformation in asphalt pavements under high-temperature conditions by developing recycled high-density polyethylene (HDPE)-modified bitumen. Through systematic laboratory investigation, we quantified the dose-dependent effects of HDPE (2–9% wt.) on rheological and mechanical properties. Dynamic shear rheometry revealed a 472% increase in rutting resistance (G*/sinδ = 6.48 kPa) at 6% HDPE versus unmodified bitumen (1.13 kPa), alongside an 18–32% reduction in phase angle (58–88 °C). Rotational viscosity surged by 240% at 135 °C (1170 cP vs. 344 cP). Mechanically, Marshall Stability peaked at 19,000 N (46% enhancement) with 6% HDPE, while flow values minimized at 2.3 mm (15% reduction). Complementary tests confirmed superior temperature susceptibility control: penetration decreased by 50% and softening point increased by 43% (72.3 °C) at 9% HDPE, with Penetration Index shifting from −0.4 to +2.18. SEM microstructural analysis validated optimal polymer dispersion at 6%, forming a continuous reinforcing network, whereas agglomeration at higher doses induced defects. Statistical modeling identified a robust linear relationship for Marshall Quotient (Adjusted R² = 0.8383). The study establishes 6% HDPE as the optimal dosage, delivering synergistic high-temperature performance enhancement while utilizing recycled plastic. Future work should address long-term aging and field validation for sustainable pavement applications in tropical regions. Full article