Search Results (13,925)

Protein–protein interactions (PPIs) represent a vast and largely underexplored landscape of therapeutic targets, yet their structural features—including large, flat, and dynamic interfaces—have historically limited their druggability. In this context, cyclic peptides have emerged as a powerful class of PPI modulators, sitting at the interface between biologics and small molecules, and thus garnering key advantages of both classes. Their conformational constraint enhances binding affinity, proteolytic stability and, in some instances, cell permeability, thus enabling access to intracellular targets. This review provides an updated overview of cyclic peptides as modulators of PPIs, focusing on both conceptual foundations and practical strategies for their discovery and optimization. The main discovery approaches include natural sources, de novo design based on secondary structure mimetics, high-throughput screening, and computational approaches. Integration of these complementary strategies is crucial to enhance success rates in the discovery of effective and developable cyclic peptides. Accordingly, the present review aims to provide a practical guide for researchers entering this rapidly growing field, outlining current opportunities, methodological advances, and remaining challenges in the development of cyclic peptide-based PPI modulators. Full article

Tarragon (Artemisia dracunculus) essential oil contains bioactive phytochemicals that may interact with hepatic transporters involved in drug disposition. This study used molecular docking and interaction analysis to evaluate the binding potential of compounds identified in tarragon essential oil samples from the Romanian market toward organic anion transporters OATP1B1 and OATP1B3, using multiple cryo-EM structures representing distinct conformational states. All investigated compounds were predicted to bind within the cavities of OATP1B1 and OATP1B3, exhibiting moderate predicted binding scores ranging from –3.956 to –6.583 kcal/mol, whereas the reference ligands resolved in the experimental structures showed binding scores ranging from –7.152 to –11.212 kcal/mol. Eugenol and its oxygenated derivatives exhibited relatively higher scores, likely due to their ability to form both hydrophobic and hydrogen-bonding interactions, whereas monoterpene hydrocarbons relied mainly on hydrophobic contacts. Interaction profiling predicted for both transporters binding environments dominated by aromatic and hydrophobic residues, alongside key polar residues contributing to hydrogen bonding. Binding patterns varied across OATP1B1 conformations, indicating state-dependent ligand recognition. Overall, the results suggest that tarragon essential oil constituents may interact with OATP1B1 and OATP1B3. Experimental studies are required to confirm the functional and clinical relevance of these findings. Full article

User experience (UX) evaluation is central to software quality, yet it remains difficult to integrate into software engineering workflows in a systematic, explainable, and early-stage manner. This paper presents TraceUX, a framework for operationalizing UX heuristics and design guidance into a rule-based static evaluation pipeline that combines machine-interpretable formalization, executability-aware assessment, context-sensitive scoring, and actionable reporting. The framework is instantiated using Apple Human Interface Guidelines, Swift abstract syntax trees, and mobile games, and implemented in a proof-of-concept tool named TraceHIG. Evaluation was conducted in four layers: analysis of the full rule repository, controlled synthetic validation with injected violations, baseline assessment of 12 public Swift game projects, and a case study on one project. The full repository contained 206 rules; after excluding non-iOS yet platform-specific rules, 193 rules were retained for the downstream experiments. In controlled validation, 216 injected violations yielded 99.2% precision, 61.6% recall, and an F1-score of 0.760. In baseline analysis, overall project scores ranged from 41.6 to 88.0, reflecting rule-conformance spread under the instantiated rule base rather than direct measures of UX quality. The case study demonstrated that profile-aware scoring can yield materially different UX assessments for the same codebase under different game configurations, highlighting the importance of app profiling in static UX evaluation. These findings show that a meaningful subset of UX knowledge can be operationalized into explainable, context-aware static analysis that provides structured and actionable decision support while complementing, rather than replacing, manual and empirical UX evaluation. Full article

Real-world speech restoration must handle coupled distortions, including acoustic noise and reverberation, codec artifacts, clipping, and artifacts left by upstream enhancement systems. Token-based generative systems offer a flexible route for such universal restoration, but discrete audio tokens can discard fine acoustic detail, and aggressive generative decoding may over-process inputs that are already close to clean speech. We propose AudioVAE-MASR, a continuous-latent masked autoregressive framework for multi-distortion speech restoration. A frozen AudioVAE maps clean and degraded speech into paired continuous latent sequences; a Conformer-based branch extracts the degraded-condition sequence $C_y$ from degraded latents; a two-stream masked autoregressive encoder-decoder conditions masked clean-latent recovery on both degraded context and visible clean tokens; and a lightweight diffusion head models the masked clean tokens in the continuous latent space. On the released CCF AATC 2025 blind test set, the main inference setting ($K=16$, temperature $0.5$) achieved WAcc 0.793, SIG 3.401, BAK 3.987, OVRL 3.111, PESQ 1.780, and ESTOI 0.798. Relative to the degraded input, these results improved WAcc and DNSMOS but did not improve PESQ; relative to the organizer baseline, they improved WAcc, SIG, OVRL, and PESQ but remained lower in BAK. A local subjective MOS evaluation with five listeners gave an overall mean score of 4.08 for AudioVAE-MASR, compared with 3.70 for the degraded input and 4.59 for the clean reference. Distortion-type, ablation, and parameter-sensitivity analyses further show that codec inputs remain vulnerable to over-restoration and that longer iterative decoding does not provide a consistent gain. The study therefore presents AudioVAE-MASR as a transparent continuous-latent restoration framework and identifies the fidelity-control problems that must be solved before such generative restoration can match the strongest lightweight discriminative systems. Full article

Data-driven aeration optimization is an effective approach for reducing energy consumption in wastewater treatment plants (WWTPs). However, in information-limited scenarios, newly established or emerging-market WWTPs often lack historical labels for aeration actions, making it difficult to construct high-precision surrogate models. Conventional cross-plant model deployments face severe data distribution shifts, and standard multi-objective optimization algorithms are prone to generating non-physical extrapolation errors, such as achieving compliance with “zero aeration” under low-concentration conditions. To break through inter-plant data barriers, this study proposes an intelligent aeration decision-making framework that integrates cross-domain transfer learning with physics-informed constraints. First, this study designs an adversarial network incorporating a state-action decoupling bypass. By employing a gradient reversal layer (GRL) to extract domain-invariant representations while the decoupling bypass preserves the physical sensitivity of control commands, this network achieves robust cross-plant knowledge transfer. Second, this study proposes a physics-informed multi-objective particle swarm optimization (PI-MOPSO) algorithm, which embeds the theoretical oxygen demand as a physical penalty into the fitness function, ensuring the physical reliability of the optimization decisions. Experiments demonstrate that the surrogate model restricts the prediction errors for effluent chemical oxygen demand (COD) and effluent ammonium nitrogen removal rates to within 1%. Validated by statistical tests, the improved algorithm effectively circumvents non-physical prediction biases. Its Pareto front achieves a spacing metric of 0.0027, outperforming baseline algorithms in hypervolume stability. This framework provides reliable aeration scheduling references conforming to biochemical dynamics for target WWTPs lacking historical action labels, offering a promising theoretical foundation for future practical engineering applications. Full article

Extant research has documented elevated rates of alcohol use among the fire service. While some studies have sought to examine the role of drinking motives in firefighter alcohol use, findings are limited by a lack of exploration into the validity of established alcohol use measures among this population. The present study explored the factor structure of the Alcohol Use Disorders Identification Test (AUDIT) and the revised, short-form Drinking Motives Questionnaire (DMQ-R-SF) among a large sample of career firefighters in the southern U.S. (N = 679). Participants were included in this secondary analysis if they reported any lifetime alcohol use and completed the measures of interest. Confirmatory factor analyses supported the established three-factor AUDIT and four-factor DMQ-R-SF. SEM results indicated that coping-motivated alcohol use was statistically significantly positively associated with each AUDIT subscale (i.e., hazardous consumption, dependence symptoms, and harmful consequences). Notably, conformity-motivated alcohol use was inversely associated with hazardous consumption. Findings underscore the importance of understanding and addressing alcohol use among firefighters, particularly drinking to cope with negative affect. Full article

Automatic Drum Transcription (ADT) remains challenging due to overlapping spectral content among drum instruments, dense transient activity, and the increased difficulty associated with fine-grained full-kit transcription. This study investigates whether combining explicit spectral structuring with temporally extended modeling improves ADT performance under a controlled evaluation protocol. A frequency-structured architecture is proposed that integrates a convolutional front end, a Frequency-Structured Soft-Gated Module (FSSM) for coarse frequency-aware processing, and a Dilated Conformer encoder for multi-scale temporal context modeling while preserving frame-level output alignment. Experiments are conducted on the ENST Drums dataset using both 3-class and consolidated 8-class transcription settings. Under a fixed ±50 ms event-level evaluation protocol, the proposed model achieved mean Micro-F1 scores of 0.920 ± 0.001 and 0.872 ± 0.002 in the 3-class and 8-class settings, respectively, outperforming a reproduced CRNN baseline trained and evaluated under identical preprocessing, training, and evaluation protocols. Ablation studies indicate that FSSM contributes the largest performance improvement among the evaluated architectural components, while class-wise analyses show improvements across all instrument categories in the 8-class setting. Zero-shot evaluation on external datasets further demonstrates that the proposed architecture maintains higher performance than the baseline under distribution shift, although substantial degradation remains relative to in-domain evaluation. These findings suggest that combining explicit spectral structuring and temporally extended modeling can improve full-kit drum transcription performance under the evaluated conditions while highlighting the continuing challenges of cross-dataset generalization. Full article

The two-spotted spider mite, Tetranychus urticae Koch, is a major agricultural pest with a rapid propensity for developing acaricide resistance. Bifenazate targets mitochondrial cytochrome b (CYTB). While the G126S mutation is associated with resistance, its independent role remains unclear, as it often occurs with other SNPs. This study explores the molecular basis of bifenazate resistance in a Russian laboratory strain derived from a St. Petersburg greenhouse population. Disruptive selection with increasing bifenazate concentrations generated resistant and susceptible isofemale lines. AlphaFold2 structural modeling of CYTB indicated that G126S causes a steric clash, leading to conformational destabilization, whereas other reported mutations primarily affect the ligand-binding pocket. Oxford Nanopore sequencing revealed a low initial frequency of the G126S allele (<1%; 226/35,895 reads) in the unselected population. After one year of stepwise selection (0.00005–0.031% a.i.), the mutant allele frequency surged to 90% (7272/8056 reads). No other resistance-associated mutations were found in the analyzed cytb fragment. We report the first identification of the G126S mutation in a Russian T. urticae population and demonstrate rapid fixation under bifenazate selection. Within this genetic background, G126S alone appears sufficient to confer high-level resistance, emphasizing the population-specific nature of resistance evolution and the critical need for local monitoring. Full article

This paper studies the stability properties of a class of Hopfield-type neural networks involving conformable derivatives and piecewise constant arguments. By constructing an associated discrete-time formulation, the continuous system is expressed in a form that is more suitable for analysis. A Lyapunov-based approach is then developed to investigate the asymptotic and exponential stability of the equilibrium point of the resulting discrete system. The analysis provides conditions that depend on the system parameters and the conformable derivative order, offering insight into the convergence behavior of solutions. The proposed approach treats the discrete formulation as an analytical tool for studying the original model. A numerical example is included to illustrate the theoretical results. 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

We introduce the local kernel-based calculus, a unifying framework for local differential and integral operators based on an arbitrary positive continuous kernel function. This framework encompasses conformable, non-conformable, and our newly introduced local Euler-kernel derivatives as special cases. The parameter of the kernel is unrestricted and may take negative values, reflecting its role as a genuine parameter rather than an order of fractional differentiation. Within this general setting, we rigorously prove a complete set of foundational theorems: linearity, the product rule, continuity, Rolle’s theorem, the mean value theorem, and the fundamental theorem of calculus via the associated integral operator. We also derive a new formulation of the chain rule that expresses the chain rule entirely in terms of the kernel-based derivatives. While algebraically equivalent to the classical form, this representation preserves the intuitive structure of the chain rule without reference to the classical derivative. We further establish the Fundamental Theorem of Local Euler Calculus and its generalization, the Fundamental Theorem of Local Kernel-Based Calculus, confirming that the derivative and integral operators are genuine inverses, with the classical fundamental theorem recovered as special cases when the kernel reduces to unity. As an important illustration, we develop the local Euler calculus with the exponential kernel in full detail, providing explicit derivative and integral formulas for elementary functions. This special case demonstrates the simplicity and power of the functional approach. Overall, the local kernel-based calculus provides a solid, self-contained foundation that unifies a wide class of local operators and extends far beyond the traditional setting. Full article

Speed management remains a central challenge in road safety, particularly in road segments where geometric design, crash concentration, or downstream stopping conditions require drivers to reduce speed. Although conventional traffic signs provide regulatory guidance, recent behavioral approaches suggest that message framing may influence driver compliance by activating different cognitive and social associated psychological constructs. However, limited evidence exists on how traditional speed-reduction signs compare with urgency-based, risk-based, and social-norm messages in shaping drivers’ behavioral intention. This study examined the perceived effectiveness of five speed-reduction messages: a standard regulatory sign, an urgency-based version, a crash-risk warning, and two social-norm variants. A within-subject survey design was applied to 326 active drivers, using seven-point Likert scales to measure behavioral intention, perceived risk, social influence, credibility, and clarity. Descriptive comparisons showed that the urgency message obtained the highest behavioral intention score, followed by the standard regulatory and risk-warning messages, whereas both social-norm messages showed lower means and greater dispersion. A latent structural equation model showed good fit and indicated that stated behavioral intention was primarily associated with perceived risk and message credibility, whereas social influence and clarity did not add significant explanatory value once these appraisal constructs were considered. This pattern suggests that drivers’ stated intention to reduce speed is shaped less by social conformity or basic message comprehension and more by whether the sign is perceived as risk-relevant and credible. Field and simulator studies are still needed to determine whether these stated-intention patterns translate into observable speed reduction. Full article

Accredited sensory certification is increasingly central to the credibility of EU Geographical Indications (GIs), particularly for wine, where typicity and regional identity compli-cate harmonization. In the absence of shared descriptors, reference standards, and decision rules, sensory control may produce inconsistent conformity outcomes across certification bodies. This study examines the main drivers and limitations of sensory harmonization in GI wine certification, with particular focus on Portugal’s mandatory batch-level sensory approval system. Using a structured narrative review and comparative analysis, it integrates sensory science literature with the EU regulatory framework requiring verifiable organoleptic characteristics in product specifications. National approaches from Portugal, Croatia, France, Germany, Greece, Italy, Romania and Spain are compared, alongside the harmonized IOC Panel Test model for virgin olive oil. Accreditation is analyzed through International Organization for Standardization standards ISO/IEC 17025 and ISO/IEC 17065, as well as EA guidance. Results show increasing convergence toward formal sensory certification but persistent divergence in how typicity is operationalized and translated into conformity decisions. The study proposes a conceptual framework in which harmonization focuses on evidential conditions rather than uniform sensory identities. Full article

Anomaly detection systems deployed in critical applications require not only high accuracy but also reliable uncertainty quantification and coverage guarantees. This paper is an empirical study—rather than a contribution of new conformal-prediction machinery—of class-conditional (Mondrian) conformal prediction for anomaly detection under extreme class imbalance, characterizing where standard conformal prediction fails and how class-conditional calibration restores valid coverage. Class-conditional conformal prediction constructs prediction sets that, under exchangeability, contain the true label with user-specified confidence (e.g., 90%), enabling systems to abstain on uncertain predictions. Unlike standard conformal prediction that fails catastrophically under extreme imbalance—achieving only 52.94% anomaly coverage at a 1:345 imbalance ratio—class-conditional calibration maintains 90.59% anomaly coverage by computing quantiles separately for each class. We apply the standard softmax-based nonconformity score $s=1-f_y\left(x\right)$ within each class, ensuring valid coverage for both normal and anomalous instances with coverage gaps ranging from 0.50% to 5.18% depending on dataset characteristics. Extensive experiments on three real-world datasets (Microsoft Azure KPI, Yahoo, NAB) demonstrate that the method achieves empirical coverage within 0.06–0.33% of theoretical targets at confidence levels $\alpha \le 0.05$; on the most imbalanced benchmark (Microsoft Azure KPI at a 1:345 ratio and $\alpha =0.10$), this corresponds to a 37.65 percentage point improvement in anomaly coverage over standard conformal prediction. We restate finite-sample coverage bounds and exchangeability conditions in the binary anomaly detection setting and validate them empirically through Monte Carlo simulation. Multi-model evaluation across XGBoost, Random Forest, and Neural Networks demonstrates the model-agnostic property of the framework, while also identifying conditions (poor base-classifier discrimination, small minority calibration sets) under which coverage may be marginally violated. Comparison with alternative uncertainty quantification methods (isotonic probability calibration, Monte Carlo dropout) shows that only conformal prediction provides formal guarantees while maintaining 90.59% anomaly coverage versus 76.47% and 84.71% for alternatives. The abstention mechanism identifies 34–66% of predictions as uncertain at high confidence levels (99%), enabling safety-critical systems to defer difficult cases to human experts while preserving baseline discrimination (ROC-AUC unchanged). Full article

Electron beam irradiation, extensively used for suppressing a wide range of pathogens contaminating food products, pharmaceuticals and biological raw materials, inevitably damages the surrounding proteins, stripping the product of its essential nutritional and functional properties. This issue can be addressed by adjusting the electron beam irradiation dose, bearing in mind the concentration of proteins in the product since it can affect the rate of radiation-induced modifications in proteins. The study investigates the impact of 7.5 MeV electron-beam irradiation on modifications in bovine serum albumin (BSA) molecules in 0.9% NaCl solution in the concentration range of 0.5–70 mg/mL, encompassing a wide range of protein concentrations in food products, pharmaceuticals and biological raw materials. Conformational changes and aggregation of BSA were evaluated using UV–Vis spectrophotometry at λ = 350 nm. Peptide bond rupture in protein native structures was assessed by performing HPLC-MS/MS analysis after trypsin hydrolysis using three selected peptides located in different domains of the BSA amino acid sequence. It was found that the rate of radiation-induced modifications increased with an increase in the irradiation dose but decreased markedly as BSA concentration increased. While at the BSA concentration of 0.5 mg/mL over 87% of BSA molecules underwent peptide bond rupture under irradiation with a dose of 5 kGy, a two-fold increase in the BSA concentration and irradiation dose enabled bond rupture in only 20% of BSA molecules. Our experimental approach resulting in the development of the dose and concentration model allows us to quantify the degree of radiation-induced protein modifications depending on the irradiation dose and protein concentration in food products, pharmaceuticals and biological raw materials. Full article