Search Results (1,089)

Sulfur fumigation, as a highly effective method for preservation and appearance enhancement, has been widely applied in fruits, vegetables, and food products. However, excessive sulfur fumigation can pose safety risks. Currently, there is limited research on the bound sulfites produced by sulfur fumigation, and no consensus has been reached regarding their structure and toxicity. Using ultra-performance liquid chromatography–quadrupole time-of-flight tandem mass spectrometry (UPLC-Q-TOF-MS/MS), a total of 34 compounds were identified in 12 lily bulb samples subjected to different sulfur fumigation durations. These derivatives were all hypothesized to form via nucleophilic addition to carbon–carbon double bonds. Based on multivariate statistical analysis, 9 characteristic markers were established to rapidly differentiate between non-fumigated (NF) and sulfur-fumigated (SF) samples. The practicality of this strategy was validated using 18 commercial batches. Molecular docking simulations predicted that the modifications might enhance toxicity toward liver injury-related targets, both by altering the spatial conformation of the compounds and because the sulfonic acid group itself serves as an ideal hydrogen-bond acceptor. Overall, mild fumigation led to a gradual accumulation of free sulfur dioxide in lily bulbs, increased the total content of phenolic components and antioxidant capacity, and did not generate excessive bound sulfur dioxide. However, with further extension of fumigation time, the content of sulfur-containing derivatives rose rapidly, accompanied by a noticeable decline in antioxidant activity. This study elucidates the sulfur-driven chemical transformation mechanisms in lily bulbs and establishes a targeted methodology for the quality control and safety assessment of processed herbal products. Full article

Despite its widespread use for mitigating multicollinearity in count data models, Poisson ridge regression (PRR) remains methodologically constrained by the choice of the ridge parameter $k$. Existing studies predominantly evaluate ridge parameter estimators using only the mean squared error (MSE) criterion, largely neglecting their distributional properties and estimation stability. Such a narrow evaluation framework may yield unreliable inference, particularly under high correlation and small sample sizes. This study makes two original contributions to the PRR literature. First, we conduct a comprehensive comparison of 13 commonly used ridge parameter estimators and introduce two new estimators that exhibit superior empirical performance. Second, we extend performance evaluation beyond MSE by incorporating outlier ratios and conformity to normality, thereby establishing a multidimensional framework that explicitly addresses distributional robustness and estimator stability. Monte Carlo simulations across 180 scenarios—varying the number of predictors, sample size, correlation level, and intercept value—show that several estimators deemed optimal under MSE perform poorly in terms of outlier prevalence and normality. In contrast, the proposed estimators consistently achieve a balanced performance between error minimization and distributional stability. Two real-data applications further support these findings. Full article

Entropic soft-min relaxations are widely used to obtain smooth approximations of minimum operators in optimization, machine learning, and control. The accuracy of this approximation is governed by an inverse temperature (or sharpness) parameter that controls the trade-off between smoothness and fidelity, yet its principled selection is typically heuristic. This work studies the data-driven calibration of the inverse temperature parameter governing the entropic soft-min relaxation, with explicit guarantees on the relaxation error between the soft-min operator and the infimum of the cost function. After establishing monotonicity properties and approximation bounds for the relaxation error, we introduce a conformal calibration rule that selects the smallest inverse temperature ensuring that the approximation error satisfies a prescribed tolerance with distribution-free finite-sample validity. The resulting selector adapts to the distribution of candidate cost-vector geometries represented in the calibration sample, enabling regime-specific inverse temperature selection in heterogeneous settings. Numerical experiments, including an adaptive cruise control application with safety filtering, show that the proposed method accurately tracks oracle calibration inverse temperatures and achieves near-target coverage in the exchangeable setting covered by the theory, while an additional shifted evaluation illustrates the role of this assumption. Full article

Both frequentist and Bayesian approaches are presented in this paper for a multiple step-stress accelerated life test. It is assumed that the lifetime distributions of experimental units under each stress level conform to a two-parameter exponentiated Rayleigh distribution. Additionally, the distributions corresponding to each stress level are related via the cumulative exposure model. In a step-stress experiment, with the applied stress level on the rise, the failure process of experimental units is accelerated, which gives rise to a reduction in their expected lifetime. This order restriction is explicitly incorporated into the statistical inference. Under the classical framework, via reparameterization, the order-restricted maximum likelihood estimates (MLEs) of unknown parameters are provided, and asymptotic confidence intervals are constructed based on the observed Fisher information matrix. In the Bayesian framework, we conduct the Bayesian analyses and obtain credible intervals using the importance sampling techniques. Extensive simulation studies are conducted, and a real dataset is analyzed for illustrative purposes. Full article

Peptide binders, serving as a critical drug modality bridging small-molecule compounds and protein macromolecules, can effectively mimic the secondary structural elements of natural proteins. Peptides exhibit unique physicochemical advantages when targeting protein protein interaction (PPI) interfaces, which are typically characterized by flat surfaces and extensive contact areas. Recently, diffusion models represented by RFdiffusion have established a new computational paradigm for protein backbone generation by defining a denoising process over the rigid-body transformation group. However, in the de novo design of binders targeting “undruggable” PPI targets, this general paradigm encounters significant adaptability bottlenecks. First, its underlying rigid-body assumption struggles to accurately describe the dynamic induced-fit process of peptides at the binding interface. Second, it lacks sufficient robustness to the experimental resolution heterogeneity inherent in training data. Furthermore, the decoupled two-stage generation of sequence and structure severs the synergy of physicochemical properties, leading to backbones with idealized, singular secondary structures that lack authentic spatial binding capacity and reasonable side-chain physicochemical features. To address these challenges, this study proposes PPI-Diff, a novel generative framework. While preserving the generative capability of diffusion models, PPI-Diff introduces three core mechanisms: (1) a resolution-aware constraint mechanism that maps the measurement precision of experimental data into explicit contextual constraints to dynamically suppress geometric noise from low-resolution samples; (2) an internal-coordinate-driven manifold diffusion model that performs conformational evolution on a Riemannian manifold constructed by dihedral angles, balancing local stereochemical validity with the precise capture of flexible peptide conformations; and (3) a geometry-semantic synergistic modeling mechanism that leverages the evolutionary embeddings of a pre-trained protein language model (ESM-2) as latent variables to align structure generation with biophysical functions. Systematic benchmarking demonstrates that, on a strictly non-homologous test set, the binders generated by PPI-Diff significantly outperform existing baseline models in terms of interface contact density, stereochemical validity, and sequence novelty. Full article

This study examined the relationships between norm perceptions about innovation, innovation source and customer satisfaction with sample data from small-scale hospitality businesses in Ghana. We adopted the quantitative approach and correlational survey design using sample data from 465 small-scale hospitality firms. Partial Least Squares Structural Equation Modelling was used to analyse the data. Measurement model classification and validation procedures comprised construct specification, indicator reliability assessment, internal consistency reliability, convergent validity (AVE), discriminant validity (HTMT and Fornell–Larcker), and collinearity diagnostics within the PLS-SEM framework. Results showed that a significant negative relationship exists between subjective norms about innovation adoption and customer satisfaction. This finding diverges from the Theory of Planned Behaviour because, contrary to its assumption that subjective norms foster positive behavioural outcomes, socially driven innovation in small-scale hospitality settings may encourage conformity-based decisions that undermine customer-oriented value creation. However, a significant positive relationship was found to exist between subjective norm perceptions about innovation adoption and innovation source. A significant positive relationship was also found to exist between innovation source and customer satisfaction. Innovation source positively mediated the relationship between subjective norm perceptions about innovation adoption and customer satisfaction. The study’s findings are relevant for owners and managers of small-scale hospitality firms seeking to align innovation decisions with customer needs, as well as for policymakers aiming to strengthen industry support systems. It offers insights into how social influences and innovation sources can be leveraged to enhance service quality and customer satisfaction in small hospitality businesses. Full article

Understanding the dynamic conformations of proteins is important for rational drug discovery. While molecular dynamics (MD) simulation is the primary tool for this purpose, it is both resource- and time-consuming. Recent advances in deep learning offer an attractive alternative by generating conformational ensembles directly from protein sequences. However, the scope of applying such models to protein dynamics studies remains underexplored. Here, we tested the performance of a representative model, BioEmu, across several tasks related to protein dynamics. Our results show that BioEmu can not only generate multiple conformations but also effectively reproduce fundamental properties including residue flexibility, motion correlations, and local residue contacts. However, it fails to predict a mutation-induced shift in conformational distribution and exhibits a preference for higher-energy conformations over lower-energy ones in some cases, indicating that it does not reproduce a right Boltzmann-weighted ensemble. Furthermore, the BioEmu-generated conformations provide only limited improvement in ensemble docking. These findings delineate the current capabilities and limitations of sequence-based generative models for conformational sampling. Also, they highlight several directions for future development—that further energy-based fine-tuning is needed for tasks related to conformational distributions and atom-level generative model is required to study the intermolecular relationship. Full article

Repetitive temperature fluctuations during transportation and storage promote ice crystal formation in salmon flesh, leading to protein denaturation, lipid oxidation, and quality loss. Tuna skin, a major by-product of tuna processing, is a potential source of antifreeze peptides (AFPs) but remains underutilized. This study examined the cryoprotective effects of tuna skin-derived AFPs on salmon cubes subjected to repeated freeze–thaw cycles. Cubes treated with AFPs from three groups of protein hydrolysates prepared using trypsin, pepsin, or neutral protease were evaluated for texture, color, water holding capacity (WHC), volatile odor profiles, protein conformation, biochemical indices, and microstructure. AFP treatment improved textural properties, maintained color stability, and reduced thawing, cooking, and centrifugal losses. The neutral protease-treated group exhibited the optimal cryoprotective ability and it also limited aldehyde and sulfide accumulation, preserved the retention rate of α-helix structure at 49% which was higher than 39% in controls, and enhanced Ca²⁺-ATPase activity to 1.75 μmol Pi·mg⁻¹·h⁻¹ with a 45.8% increase compared to controls, and significantly inhibited protein and lipid oxidation. Microstructural analysis showed compact fibers and intact sarcolemma in the neutral protease-treated group samples, contrasting with severe disruption in controls. This study showed that tuna skin AFPs mitigate freeze–thaw damage in salmon cubes by stabilizing proteins and reducing oxidative deterioration, highlighting their potential as natural, healthy cryoprotectants for seafood preservation, meeting the growing demand of the food industry for clean-label, low-calorie preservation solutions, while advancing the circular economy of aquatic processing via the valorization of tuna skin by-products for high-value seafood applications. Full article

Accurate forecasting of equity returns remains fundamentally constrained by weak short-horizon predictability, pronounced noise, and structural non-stationarity. While deep learning models have been widely applied to financial time series, most studies prioritize point prediction and provide limited guidance on reliable uncertainty quantification, particularly in emerging markets. This study developed an uncertainty-aware forecasting framework for the South African equity market by integrating variational mode decomposition (VMD), gated recurrent units (GRUs), and temporal conformal prediction (TCP) to construct distribution-free prediction intervals with finite-sample coverage guarantees. Using daily returns from the FTSE/JSE All Share Index, we first confirmed that baseline recurrent models applied directly to raw returns exhibited negligible out-of-sample explanatory power, consistent with weak-form market efficiency. Incorporating VMD enhanced representation learning and improved point forecast accuracy by isolating latent frequency components. However, model-based predictive variance alone proved insufficient for reliable calibration. Embedding the models within a rolling conformal prediction framework restored near-nominal coverage across multiple confidence levels while allowing interval widths to adapt dynamically to changing volatility regimes. Robustness analyses, including walk-forward validation, stress-regime evaluation, and block permutation negative control experiments, indicated that the observed performance was not driven by temporal leakage or alignment artifacts. The results further highlight a trade-off between interval sharpness and tail-risk protection, particularly during extreme market events. Overall, the findings support a shift from return-level prediction toward calibrated uncertainty estimation as a more stable and economically meaningful objective in non-stationary financial environments. Full article

Background/Objectives: Protein–protein interactions (PPIs) involving oncogenic drivers remain among the most intractable targets in cancer biology due to their dynamic conformations and limited accessibility to conventional small molecules. Although antibodies and inhibitors have achieved clinical success against targets such as PD-1/PD-L1 and MYC, challenges persist related to tissue penetration, intracellular delivery, resistance, and incomplete blockade of key interface hotspots. The objective of this study is to develop an integrated computational framework for systematically designing hotspot-conditioned de novo miniprotein binders to target these interfaces. Methods: We present DesignForge, a computational protein design pipeline that integrates energetic hotspot identification, generative backbone design, sequence optimization, and structural confidence evaluation. The framework combines hotspot mapping using an open force-field-based energetic analysis module with generative backbone sampling using BindCraft, sequence optimization using ProteinMPNN, and structural validation using AlphaFold2. This in silico pipeline was applied to three representative oncogenic interfaces: PD-1/PD-L1, MYC/MAX, and KRAS/RAF. Results: Computationally generated designs exhibited high predicted structural confidence, favorable interface energetics, and consistent engagement of identified hotspot residues across targets. AlphaFold2-Multimer structural modeling indicated that the candidate PD-1 mimetic scaffolds, MYC/MAX interface binders, and KRAS interaction candidates can adopt conformations compatible with the target interfaces. Energetic contact analysis further supported predicted engagement of key hotspot residues. These findings support the computational feasibility of hotspot-conditioned binder generation using a unified design workflow. Conclusions: DesignForge provides a reproducible computational framework for hotspot-guided de novo protein binder design targeting oncogenic protein–protein interfaces. The designs reported here represent computational predictions derived from structural modeling and energetic analysis. Experimental biochemical and cellular validation will be required to determine the functional activity of the proposed binders. Full article

RNA origami enables genetically encoded, single-stranded RNA nanostructures that can self-assemble through co-transcriptional folding and are increasingly deployed as scaffolds for biosensing, synthetic biology, and nanomedicine. A recurring practical bottleneck is scalable, solution-phase readout of whether a designed scaffold has reached its intended base-paired architecture, whether it undergoes slow maturation or kinetic trapping, and how its stability is distributed across motifs. Here, we adapt microfluidic modulation spectroscopy (MMS) as a label-free structural biosensor for RNA folding by exploiting the rich 1760–1600 cm⁻¹ vibrational fingerprints of RNA bases and base pairs. MMS alternates between sample and composition-matched buffer measurements in a microfluidic transmission cell to automatically subtract the solvent background, enabling high-quality spectral measurement from microliter volumes under native solution conditions. Using a six-helix-bundle-with-clasp (6HBC) RNA origami as a model, we established an analysis workflow (baselined second derivative and constrained deconvolution) to quantify paired versus unpaired populations. Thermal ramping resolves multiple unfolding events and yields an unfolding barcode that differs between young and mature ensembles. Importantly, MMS tracks post-transcriptional maturation from a kinetically trapped young conformer toward a more compact, base-paired mature state, consistent with prior cryo-EM/SAXS observations for 6HBC RNA origami. Together, these results position MMS as a rapid, automated, and scalable complement to high-resolution structure determination for engineering dynamic RNA origami biosensors. Full article

Long-term stability of multienzyme protein systems is governed by preservation of conformational integrity and resistance to thermally induced structural destabilization. This study evaluated bovine pancreatin (BP) obtained by conventional extraction (CM) and ultrasound-assisted extraction (UAM) during 0–930 days of storage at 10–40 °C. Amylolytic (AA), proteolytic (PA), and lipolytic activities (LA), representing the functional enzymatic activity (EA) of the multienzyme protein system, were monitored to characterize degradation kinetics and activity loss associated with conformational destabilization. After 930 days at 20 ± 1 °C, UAM retained 76% of initial AA compared with 58% for CM, corresponding to a 31% higher residual activity in UAM. LA demonstrated comparatively high stability in both preparations (~84% retention), whereas PA exhibited delayed degradation and significantly higher residual values in UAM samples. Two-way ANOVA confirmed significant effects of extraction method, storage duration, and their interaction (p < 0.001), indicating method-dependent kinetic behavior. Elevated temperatures (35–40 °C) accelerated inactivation, consistent with increased molecular mobility and reduced conformational stability. The smoother degradation trajectories and lower apparent inactivation rates observed in UAM preparations suggest kinetic stabilization, potentially associated with improved conformational preservation and reduced extraction-induced structural stress. Both preparations complied with pharmacopoeial microbiological limits. These findings support the hypothesis that UAM enhances long-term functional stability of complex multienzyme systems through mechanisms related to conformational resilience. Full article

Traditional methods for geostressfield inversion face issues such as weak physical interpretability and insufficient generalization ability. This study pioneers the application of Physics-Informed Neural Network (PINN) to this problem, developing a data- and physics-driven inversion algorithm. The framework incorporates a constitutive-equation-based regularized loss function as a hard constraint during training to ensure physical consistency. To address boundary load uncertainty, two quantification approaches—Bayesian linear regression and surrogate model optimization—are proposed to establish 95% confidence intervals for boundary coefficients. Verification based on simple three-dimensional models and actual geological models of mines shows that PINN inversion achieves a mean absolute relative error as low as 0.0772%, with an error of 15.67% under sparse sampling conditions—significantly lower than the 31.07% error of the traditional Back propagation neural network. This demonstrates excellent robustness and data efficiency. In the practical engineering application of complex geological bodies, the average error of principal stress inversion is 9.35% with a minimum error of 0.137%. All inversion results fall within the permissible accuracy range of engineering, and the stress distribution conforms to basic laws, with an average error of 0.453 in the constitutive relation. Compared with BP neural network and multiple linear regression methods, it shows obvious accuracy advantages. This method provides a new solution for intelligent ground stress prediction with high accuracy, high efficiency, and strong physical interpretability, and also lays the foundation for early identification of geological disasters. Full article

The principal difficulty in studying the physico-mechanical and filtration-capacity properties of coals and host rocks under laboratory conditions using core samples lies in reproducing natural thermodynamic conditions characteristic of in situ depths. To address this issue, specialized equipment and methodologies for transferring measurement results are employed, including the Hoek–Brown failure criterion, the structural weakening coefficient, and the development of thermodynamic models. The reliability and accuracy of such measurements are determined by the degree of conformity between the adopted laboratory conditions and natural in situ conditions, the number of samples representing different lithological varieties, and the adequacy of sampling procedures ensuring representativeness. Particular challenges arise when sampling cleated and fractured coals formed under natural stress–strain conditions and contain methane, which significantly influences their physical properties. These difficulties are especially pronounced in prepared-for-mining high-gas-content coal seams of the Karaganda Basin at depths of approximately 700 m, where obtaining representative samples is technically complicated. Reliable values of the physico-mechanical properties of the coal–rock mass are essential for geomechanical calculations aimed at ensuring safe mining of high-gas-content seams through risk assessment of geodynamic phenomena, particularly in zones of geological disturbances, floor heave, and roof collapse. In this context, the use of a comprehensive suite of geophysical logging data from exploration boreholes makes it possible to obtain continuous, high-precision information on physico-mechanical and filtration-capacity properties. These methods are particularly important for characterizing the coal–rock mass in operating mines, since the natural state of host rocks and prepared coal seams is altered due to stress relief caused by mine workings, preliminary degasification measures, and hydraulic fracturing. The problem addressed is the need for reliable assessment of rock and coal seam parameters under natural thermodynamic stress–strain conditions, taking into account lithological composition, structural heterogeneity, fracture development, stratigraphic differentiation, and gas saturation. The aim of this study is to ensure efficient and safe coal extraction based on geomechanical calculations utilizing physico-mechanical and filtration-capacity properties of host rocks and gas-bearing coal seams, whether prepared for mining or not yet extracted. The research methods are based on an integrated complex of geophysical logging of exploration wells, specialized software tools, and statistical processing techniques to identify patterns in physico-mechanical and filtration-capacity properties of host rocks and coal seams under natural stress–strain conditions, as well as to determine the nature of changes in these properties within coal seams and roof and floor rocks in prepared mining areas. The physico-mechanical and filtration-capacity properties of host rocks and coals from the Lenin and Kazakhstanskaya mines were determined. Regularities governing the application of these parameters to coals of different formations and depths were established; fracture orientations and characteristics were evaluated; and relationships between changes in coal seam parameters and gas content were identified. A comprehensive methodological framework for studying the physical and capacity properties of the coal–rock mass under natural thermodynamic conditions has been developed. Its primary application is the investigation of coal seams prepared for mining to support geomechanical calculations for efficient and safe coal extraction, the implementation of degasification measures for high-gas-content seams, and the assessment of gas-dynamic risks based on the character of variations in physical parameters. Full article

Nuclear Magnetic Resonance (NMR) spectroscopy has long been a cornerstone in the structural elucidation of molecules, offering unique insights into atomic-level connectivity, conformation, and dynamics. Over the past decades, methodological and technological advances have significantly expanded its capabilities and applications. This manuscript charts the evolution of NMR from classical 1D/2D experiments to modern methods empowered by ultrahigh magnetic fields, cryogenic probes, non-uniform sampling, new methodologies, and hyperpolarization. We emphasize the growing synergy between experiment and computation, where automated analysis, quantum chemical calculations, and machine learning are dramatically enhancing the accuracy and efficiency of structure determination. We also highlight NMR’s broadening scope in areas ranging from complex mixtures and natural products to biomolecular and materials science. Full article