Search Results (14,470)

Background: This study aimed to determine whether incorporating radiologic contrast during the excretory (urographic) phase enhances the detection of recurrence on [68Ga]Ga-PSMA-11 PET/CT in patients with biochemical relapse (BCR) following radical prostatectomy (RP). Methods: A single-center retrospective analysis included 43 men with BCR after RP who underwent [68Ga]Ga-PSMA-11 PET/CT. Each patient underwent two comparative assessments. In the first assessment, whole-body PET images acquired at 60 min post-injection were fused with the non-contrast CT from early dynamic pelvic imaging (PET/CTd), and local recurrence and pelvic nodal involvement were evaluated according to PROMISE V2 and E-PSMA frameworks by two blinded readers. In the second assessment, the same PET dataset was fused with the excretory-phase CT urography (CT-U) obtained during the same imaging session at 60 min post-injection, and the same parameters were re-evaluated. Endpoints included surgical-bed classification, peri-ureteric nodal status, reader confidence, ureter visualization/opacification, and interpretation time. Inter- and intra-observer agreement was assessed, and discrepancies were resolved by consensus. Results: Surgical-bed positivity decreased from 12/43 (27.9%) on PET/CTd to 5/43 (11.6%) on PET/CT-U, leading to reclassification in seven patients (p = 0.016). Reader confidence improved significantly in five cases (p < 0.005). Peri-ureteric nodal status was changed in four patients (two positive-to-negative and two negative-to-positive), with overall positivity unchanged (5/43 vs. 5/43; p = 1.000). Ureter visualization improved markedly (inadequate: 31 vs. 10 cases), reducing diagnostic uncertainty by 50%. CT-U opacification was ≥50% in most cases (κ = 0.814), enabling reliable delineation of the ureteral course. Inter-reader agreement remained strong (surgical bed κ: 0.944 vs. 0.876; nodes κ: 0.896 both). Interpretation time decreased for both readers (senior: 3.12 vs. 2.10 min (−32.7%); junior: 4.06 vs. 2.42 min (−40.4%)). Conclusions: Adding an excretory-phase CT urography to [68Ga]Ga-PSMA-11 PET/CT improves diagnostic confidence, reduces interpretive uncertainty in the surgical bed, clarifies peri-ureteric nodal findings, enhances ureter visualization, and shortens interpretation time. CT-U is a practical enhancement to low-dose PET/CT protocols for BCR after RP. Full article

Neural metric learning is often assessed by retrieval accuracy, but a learned dissimilarity can rank examples well while failing to have norm-like algebraic structure. This paper studies a precise finite question: within a Euclidean-anchored residual family of neural dissimilarities, can one reduce sampled defects of homogeneity, subadditivity, and dyadic reconstruction on latent differences without destroying retrieval performance? The construction is inspired by asymptotically metrically normable (AMN) vector spaces, but its claims are finite, sampled, and latent: it does not prove global AMN rigidity or certify a metric on the input space. The framework is motivated by the observation that many learned similarities have the form $K=exp(-E/\tau )$ and therefore encode an unbounded distance-like quantity or squared distance-like quantity behind a bounded affinity. The AMN-relevant object is this cost, not the bounded kernel value. We formalize bounded-perturbation stability of the large-scale specific energy $E(nv,0)/n$, the conversion of subadditivity into multiplicative affinity consistency, and the quotient interpretation in which directions of zero large-scale cost are collapsed. The mathematical development then introduces finite dyadic diagnostics, learned-gauge and convex-unit-ball interpretations, finite norm-envelope witnesses, dyadic stability bounds, and refinement towers of witness norms. The empirical part reports full official Fashion-MNIST experiments with supervised-contrastive and proxy-anchor-style Euclidean baselines, post hoc audits for shrinkage, residual flexibility, off-training scales, and latent extrapolation, and a ten-seed full-query/full-gallery UCI Human Activity Recognition benchmark. The results show that Euclidean objectives can be stronger for Recall@1, whereas AMN-inspired residual regularization substantially reduces finite norm-like defects inside the residual family. The contribution is therefore a finite diagnostic and regularization framework for learned latent dissimilarities, not a state-of-the-art retrieval objective. Full article

Global requirement of clean, cost-effective and sustainable energy has stimulated massive research and development in photocatalytic materials that have the potential to harvest solar based energy while mitigating the environmental issues. Among various materials, perovskite oxides have emerged as a promising energy resource. Owing to the structural versatility, optical and electrical properties, chemical inertness allows the use of material of multifunctional prospects. Currently Strontium titanate (SrTiO₃), a vital perovskite oxide having a band gap nearly ~3.2 eV, is showing significant function for photocatalytic water splitting, carbon dioxide conversion and degradation of organic pollutants. Though within the UV spectrum, its intrinsic photocatalytic behavior is limited to approaches such as graphene junctions, noble-metal support, and post-synthetic heat treatment seem to promote the adsorption within visible-light. Strontium titanate also demonstrates photo charge separation efficiency, and long-term catalytic durability. Moreover, modifications and hydrothermal synthesis have proven extremely efficient for nano-based engineering, control over crystal diameter, defects, and shape, which can result in magnificent composites that can be promising substitutes. Therefore, further research is imperative regarding these material application prospects. This comprehensive review provides insights into details on the potential of nanoengineering and composite approaches to reduce the inherent limitations of perovskite oxides, especially Strontium titanate, and enabling additional applications in next-generation photovoltaic and solar energy harvesting technologies. Full article

Despite the widespread use of planar flow casting (PFC) for amorphous alloy ribbons, previous two-dimensional (2D) numerical studies have primarily focused on isolated flow or thermal behaviors, lacking a systematic quantification of how key casting parameters collectively influence melt puddle geometry and ribbon thickness. To fill this gap, this work establishes a coupled air–melt two-phase 2D Volume of Fluid (VOF) model based on the continuity, momentum, and energy equations. An analysis was conducted on how various parameters affect the melt puddle behavior and ribbon thickness. The results indicate that, as the roller speed (U) increases from 21 m/s to 30 m/s, the detachment length (Ln) decreases by 31%. Over the same interval, the puddle length (L) decreases by 30%. When the ejection speed (V) increases from 1.4 m/s to 2.0 m/s, the ejection temperature ($T_e$) increases from 1433 K to 1733 K, and the slit width (W) increases from 0.4 mm to 0.6 mm, Ln rises by roughly 39.7–133%, while L increases by approximately 32.3–112%. To produce thinner amorphous ribbons for loss reduction, high roller speed, low ejection speed, and small nozzle slit are crucial parameters. Full article

Underground coal gasification (UCG) requires rigorous geological suitability evaluation to reduce project risks, and scientific site selection is critical for success. Taking the No. 15 coal seam in the Yushe–Wuxiang Block (Qinshui Basin) as the focus, this study evaluates the feasibility of deep UCG using a multi-criteria decision-making framework. A hierarchical evaluation model comprising four primary and 10 secondary geological indicators (e.g., coal thickness, parting coefficient, fault fractal dimension, roof lithology) was constructed. Subjective weights were derived from the Analytic Hierarchy Process (AHP) and combined with objective weights from the coefficient of variation method. The TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method was then applied to rank seven development units. Results indicate that the No. 15 coal seam has reasonable potential for UCG implementation. The most favorable areas (Blocks II and VII) are characterized by thick coal seams (>5 m), low parting coefficients (<8%), simple fault networks (fractal dimension ≤0.5–1.05), and competent mudstone roofs. Blocks III, V, and VI are moderately favorable, while Blocks I and IV are marginally favorable. These findings provide a prioritized roadmap for pilot-scale UCG testing in the Yushe–Wuxiang Block. Full article

The fundamental issue in designing encapsulation-based self-healing concrete structures is the design of microcapsules. However there are few studies in the literature on this topic; not only is the fracture of microcapsules crucial for releasing the healing agent to heal fractures in the concrete matrix, but also the amount of the healing agent and expected crack widths. Therefore, in this paper, a novel design method of dimensioning microcapsules for encapsulation-based self-healing concrete (SHC) with consideration for a sufficient volume of healing agent to heal a specific crack width is developed. It is based on the configuration of the representative volume element (RVE) and the unit cell (UC), and associates them with the volume fraction (V_f) and the crack width as variables with applied periodic boundary conditions (PBCs). It is also validated through numerical fracture modeling using the eXtended Finite Element Method (XFEM), and cohesive surface (CS) technique. Effects of interfacial cohesive properties, the microcapsule size, and volume fraction on the load carrying capacity and the crack pattern are investigated numerically. The obtained results are in good agreement with the literature. The developed design method can serve as a valuable tool for obtaining a preliminary design of microcapsules for SHC. Full article

The outbreak of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has emerged as one of the most significant global health crises in recent history. Coronaviruses are a diverse group of RNA viruses classified into alpha, beta, gamma, and delta genera, with SARS-CoV-2 belonging to the beta-coronavirus family. The virus exhibits high transmissibility and causes a wide spectrum of clinical manifestations ranging from mild respiratory symptoms to severe complications such as acute respiratory distress syndrome, multi-organ failure, and death, particularly among elderly and immunocompromised individuals. Structurally, SARS-CoV-2 possesses a large single-stranded RNA genome encoding major structural proteins, including spike (S), envelope (E), membrane (M), and nucleocapsid (N) proteins, which play critical roles in host-cell recognition and viral infection. Understanding the molecular mechanisms of virus–host interactions, especially protein–protein interactions (PPIs), is essential for uncovering viral pathogenesis and identifying potential therapeutic targets. Traditional experimental techniques for PPI detection, such as yeast two-hybrid and affinity purification methods, are often expensive, labor-intensive, and prone to inaccuracies. Consequently, computational approaches based on machine learning (ML) and deep learning (DL) have gained significant attention for efficient and scalable PPI prediction. These methods use diverse biological information, including protein sequences, structural features, genomic data, Gene Ontology annotations, and interaction networks, to model complex biological relationships. This survey reviews computational approaches to PPI prediction, highlighting ML- and DL-based techniques, methodological advances, performance evaluation practices, and limitations that affect benchmark comparability. It also discusses biological databases and data sources commonly used in PPI studies and explicitly considers how models trained in coronavirus-centered settings may generalize to other viral families with different mechanisms of host interaction. Full article

Cod liver oil is a rich source of polyunsaturated fatty acids (PUFAs) but is highly susceptible to oxidative degradation, limiting its use in topical formulations. This study aimed to develop stable cod liver oil nanoemulsions for topical application and to evaluated the influence of surfactant ratio (lecithin/PEG-15 hydroxystearate: 2.5:1 and 1:1, w/w), emulsification method (ultrasonication or high-pressure homogenization), and vitamin E acetate supplementation on their physicochemical properties and oxidative stability. Eight nanoemulsions were characterized in terms of droplet size, polydispersity, ζ-potential, vitamin E acetate encapsulation efficiency, oxidative stability, film-forming capacity and cytocompatibility. Among the investigated formulations, F4 (2.5:1 lecithin/PEG-15 hydroxystearate, high-pressure homogenization, with vitamin E acetate) exhibited the most favorable characteristics, including a mean droplet size of 67.95 nm, ζ-potential of −63.12 mV and vitamin E acetate encapsulation efficiency of 32.59%. The formulation demonstrated good physicochemical stability under thermal, mechanical and photostability testing, improved oxidative stability, transient film-forming behavior with an initial occlusive effect, and no cytotoxicity toward human dermal fibroblasts. These findings indicate that nanoemulsion performance depends on the combined influence of formulation composition and processing conditions, with F4 representing a promising topical carrier for cod liver oil intended for interaction with the stratum corneum. Full article

Quantum gate estimation and tomography pipelines routinely combine intrinsically defined likelihoods with priors or regularization terms specified in local Euclidean coordinates. This practice implicitly replaces the Haar reference measure on $SU\left(2\right)$ with Lebesgue measure, specifying a different statistical model rather than a reparametrization of the intended one. We show that omitting the associated chart-volume factor alters the optimization objective itself, modifying its gradient field and stationary-point structure. The gradient discrepancy $\nabla L_G-\nabla L_E=-\nabla log{J}_{exp}$ is nonzero for all $v\ne 0$ so that flat-coordinate surrogate objectives can converge to points that are non-stationary for the corresponding Haar-consistent objective even in regimes where local Gaussian approximations are assumed valid. We prove a formal non-equivalence proposition and validate a leading-order Fisher-information correction analytically and numerically. Large-scale multi-start optimization experiments ($N=11,900$ runs) demonstrate that the discrepancy is regime dependent and most pronounced under moderate-to-strong regularization or limited data. The fix requires a single-line modification to any gradient-based optimizer. These results identify reference-measure selection as an explicit modeling decision with direct consequences for optimization and inference in gate-set tomography, randomized benchmarking, and Bayesian gate estimation on curved parameter manifolds; quantitative validation is restricted to single-qubit systems, though the mechanism extends to any regularized optimization on a curved parameter manifold. Full article

We report the study of Cur@HKUST-1 composites, obtained through one-pot infiltration of HKUST-1 with curcumin (Cur) as a guest-sensitizing molecule. Cur features a HOMO energy above the valence band (VB) of HKUST-1, enabling modulation of the electronic structure of the host framework by introducing additional energy states within the bandgap. Structural characterization, including X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and thermogravimetric analysis (TGA), confirmed successful guest incorporation and preservation of HKUST-1 crystallinity. An initial Cur amount of 50% (relative to the BTC linker) was added to the synthetic mixture, and differential UV-vis analysis has shown an infiltration efficiency of 28.0%, corresponding to an infiltration degree of 14% in the Cur@HKUST-1 composite, highlighting a challenging loading process, primarily due to the size and conformations of the Cur structure. Textural analysis revealed a reduction in surface area and pore volume, consistent with a high degree of guest infiltration. Optical properties evaluated by diffuse reflectance UV-vis spectroscopy revealed new absorption bands and a notable decrease of 1.83 eV in the bandgap energy from 3.68 eV (HKUST-1) to 1.85 eV (Cur@HKUST-1) due to guest molecule infiltration. Density functional theory (DFT) calculations supported the experimental findings, showing that guest HOMOs promoted the formation of a new valence band (VB), while the original VB remains lower in energy. Density-of-states analysis confirmed that the new VB originates from 2p orbitals belonging to the guest, while the conduction band remains predominantly Cu-based from the HKUST-1 framework. Photoelectrochemical characterization revealed that the guest-modified material exhibits an enhanced photocurrent response compared to HKUST-1. Cur@HKUST-1 displayed higher stability and stronger photocurrent density, attributed to its narrower bandgap and increased charge carrier density. These results demonstrate the potential of rational guest selection to engineer band structure and improve the light-harvesting performance of MOFs in solar-driven applications. Full article

The increasing contamination of municipal wastewater by a broad spectrum of pharmaceuticals necessitates effective quaternary treatment stages. This pilot study evaluates an innovative combined technology: physical electrostatic pretreatment (conducted using experimental equipment based on a patented design (EP 2388068)) followed by biodegradation in real secondary effluent samples (COD_(Cr) 22.0 mg·L⁻¹ to 32.0 mg·L⁻¹). A total of 17 selected micropollutants were subjected to an 8-h exposure in a high-intensity electrostatic field (20 kV) and a subsequent 20-day microbial degradation using a mixed culture of erythropolis, R. rhodochrous, and R. degradans. Results demonstrate high substance-specific efficiency. The most significant synergistic effect was observed for moderately biodegradable compounds, particularly venlafaxine (improvement up to ~44%), trimethoprim (25–36%), and tramadol (31–58%), representing a ~30–37% efficiency increase over standalone biodegradation. For readily biodegradable (e.g., metoprolol) or highly persistent substances, the impact was inconsistent. Physical pretreatment alone at 20 kV exhibited low to moderate efficiency (up to ~30%) without the biological stage. This combined approach represents a promising synergistic solution for wastewater treatment plant intensification. The primary mechanism involves enhancement of target compound bioavailability induced by the electrostatic field, which subsequently accelerates microbial metabolism. Full article

NAD⁺ depletion is a defining feature of the aging cell, driven by a progressive decline in nicotinamide phosphoribosyltransferase (NAMPT) activity, the rate-limiting enzyme of the NAD⁺ salvage pathway. Pyrroloquinoline quinone (PQQ), a plant-derived redox-active quinone cofactor, elevates intracellular NAD⁺ by a mechanism that remains incompletely understood. We employed an integrated in silico approach combining molecular docking, density functional theory (DFT), and 100 ns molecular dynamics (MD) simulation to evaluate whether PQQ directly targets NAMPT. Docking against the NAMPT crystal structure (PDB: 7ENQ) yielded a binding free energy of −9.4 kcal/mol, with PQQ positioned in the allosteric activation site and forming hydrogen bonds at His191, Asp219, and Val242 together with π–π stacking at Tyr188, extending a known synthetic activator pharmacophore to a dietary ligand class. MM-GBSA analysis yielded binding free energy = −31.2 kcal/mol, confirming dominant electrostatic and van der Waals stabilization. In silico alanine mutagenesis of Tyr188 and Val242 reduced binding affinity to −7.2 and −7.0 kcal/mol respectively, with complete loss of allosteric-site contacts, validating the proposed mechanism computationally. DFT analysis revealed a HOMO–LUMO gap of 3.20 eV and electrophilicity index ω = 8.91 eV, consistent with non-covalent binding to nucleophilic residues. MD simulation confirmed retention of PQQ within the allosteric site over 100 ns. These data provide a structural and electronic framework for the NAD⁺-boosting activity of PQQ and a rationale for experimental validation. Full article

Autonomous Underwater Vehicles (AUVs) are widely employed for deep sea exploration and underwater operations, but their navigation performance is often degraded in complex environments due to time-varying measurement noise, abnormal observations, and Doppler Velocity Log (DVL) outages. To address these challenges, this paper proposes an integrated SINS/DVL/PS navigation framework that combines an Adaptive Huber and Sage–Husa Extended Kalman Filter (AHR-EKF) with a Nonlinear AutoRegressive with eXogenous inputs (NARX)-based velocity prediction model. The AHR-EKF effectively suppresses outliers and adapts to time-varying noise, thereby enhancing filter stability and state estimation accuracy. During DVL outages, the NARX model predicts short-term AUV velocity using propeller speed, velocity increments from the navigation system, and attitude information as exogenous inputs. This data-driven approach compensates for lag and mismatch in propeller-based velocity measurements, while capturing both short-term fluctuations and overall velocity trends. Simulations and sea trials were conducted to validate the method. In the simulation experiment during DVL outages, the V-NARX method achieved east and north positioning of RMS errors of 8.397 m and 6.530 m, compared with 24.699 m and 10.218 m for the V-RPM method. In the sea trial, the V-NARX method achieved east and north RMS errors of 41.160 m and 28.023 m, respectively, compared with 52.820 m and 67.057 m for V-RPM, corresponding to reductions of 22.1% and 58.2%. The proposed method maintains trajectory continuity and effectively suppresses rapid INS error accumulation during DVL outages, significantly enhancing emergency navigation capability under DVL outages. Although its positioning accuracy does not match that of normal DVL operation, the method provides a practical and reliable engineering solution for continuous AUV navigation when DVL is unavailable. Full article

Neltuma pallida is a multi-purpose tree species of the seasonally dry tropical forests of northern Peru, where it provides essential ecological and socioeconomic functions. However, recurrent defoliation associated with the cecidomyiid gall midge Enallodiplosis discordis may compromise early seedling establishment and the success of forest restoration programs. This study evaluated the effects of larval infestation on foliar integrity and established quantitative damage thresholds in N. pallida seedlings under dry forest conditions. Insects collected from naturally infested plants were identified using an integrative taxonomic approach that combined classical morphological diagnosis with COI-based DNA barcoding obtained by Sanger sequencing. Morphological assessment assigned the defoliating dipteran to E. discordis, while BLASTn v2.17.0. analysis of the 576-bp partial COI sequence showed 92.6% identity and 100% query coverage with Cecidomyiidae records, supporting its taxonomic placement within this family. Field bioassays conducted over a 17-week period, in which 25 individual seedlings were evaluated (N = 25), revealed a strong and significant positive correlation between larval density and foliar damage percentage (r = 0.872; p < 0.001), with moderate damage levels predominating throughout the evaluation period. Despite sustained larval presence, seedlings did not reach severe damage categories, suggesting potential relative tolerance to partial defoliation under the evaluated field conditions. Temperature and relative humidity were not significantly associated with infestation intensity or foliar damage during the study period. Overall, these findings indicate that E. discordis-associated foliar damage represents a relevant, although not necessarily lethal, biotic constraint for the early regeneration of N. pallida under the field conditions assessed. The quantitative thresholds reported here provide useful criteria for dry forest restoration programs, phytosanitary monitoring, and integrated pest management strategies in the Peruvian dry forest. Full article

The autism spectrum disorder-associated SCN2A, ANK2, and SHANK-family genes encode molecularly distinct proteins that converge functionally on dendritic integration. Recent work established that ankyrin-B, encoded by ANK2, acts as an obligate dendritic scaffold for NaV1.2, encoded by SCN2A, in neocortical pyramidal neurons. Loss of this module mislocalizes dendritic NaV1.2, reduces dendritic Na⁺ influx, weakens backpropagating action potentials, and impairs synaptic maturation and long-term potentiation. SHANK proteins organize a complementary postsynaptic receptor scaffold within dendritic spines, coupling N-methyl-D-aspartate (NMDA), α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA), and metabotropic glutamate receptor (e.g., mGluR5) signaling to the actin cytoskeleton through layered PSD-95/GKAP/Homer interactions. Disruption of this scaffold can destabilize excitatory transmission, spine morphology, and plasticity. We propose that these dendritic shaft and spine-associated modules jointly regulate dendritic input–output gain and that their disruption may contribute to autism spectrum disorder by destabilizing, rather than uniformly shifting, excitatory integration across cortico-subcortical circuits relevant to sensory reactivity, behavioral flexibility, and social-valence processing. Here, we review the cortical evidence for this layered dendritic convergence and evaluate its potential relevance beyond the cortex. We assess the striatum, thalamus, and amygdala as subcortical sites where related dendritic scaffolding mechanisms may operate. The striatum provides the strongest current test case, with established roles for both NaV1.2 and SHANK3 in medium spiny neuron physiology and corticostriatal connectivity. Thalamic and amygdalar extensions are supported mainly by SHANK-related circuit and channelopathy data but lack direct evidence for ANK2–SCN2A involvement. The framework is experimentally testable: conditional Ank2 deletion in striatal, thalamic, and amygdalar cell types; dendritic Na⁺/Ca²⁺ imaging across Scn2a, Ank2, and Shank3 models; adult rescue experiments; and genetic-interaction designs would determine whether ankyrin-B supports dendritic excitability beyond the cortex and whether these genes converge on, rather than merely parallel, dendritic input–output gain. Validation in human subcortical tissue would then establish whether this dendritic scaffolding logic represents a shared point of convergence through which genetically distinct autism spectrum disorder-risk variants alter circuit function. Full article