Search Results (135)

Aptamers are single-stranded DNA or RNA oligonucleotides screened by systematic evolution of ligands by exponential enrichment (SELEX) methods, which are widely used in food analysis. Aptamers have the advantages of low molecular weight, ease of preparation, simplicity of chemical modification, and structural stability. Aptamers generated by SELEX are typically 80–100 bases in length, and the affinity of the aptamer can be improved by sequence optimization. Methods of aptamer optimization commonly include truncation, mutation, and chemical modification, and molecular docking, molecular dynamics, circular dichroism, and isothermal titration to assess often the binding performance of the aptamer to the target. Optimized aptamers usually enhance the affinity of the aptamer for the target and increase its sensitivity in the detection of pesticides, heavy metals, fungal toxins, pathogenic bacteria, and other objects. This paper focuses on truncation, mutation, chemical modification, the introduction of rare nucleotides, and computer-aided design. It provides an overview of non-immobilized optimization metrics. Full article

A salient feature of modern material surfaces used in cutting-edge technologies is their structural and spatial complexity, which endows them with novel properties and multifunctionality. The quantitative characterization of material complexity is a challenge that must be addressed to optimize their production and performance. While numerous metrics exist to quantify the complexity of spatial structures in various scientific domains, methods specifically tailored for characterizing the spatial complexity of material surface morphologies at the micro- and nanoscale are relatively scarce. In this paper, we utilize the concept of multiscale entropy to quantify the complexity of surface morphologies of rough surfaces across different scales and investigate the effects of amplitude fluctuations (i.e., surface height distribution) in both stepwise and smooth self-affine rough surfaces. The crucial role of the binning scheme in regulating amplitude effects on entropy and complexity measurements is highlighted and explained. Furthermore, by selecting an appropriate binning strategy, we analyze the impact of 2D imaging on the complexity of a rough surface and demonstrate that imaging can artificially introduce peaks in the relationship between complexity and surface amplitude. The results demonstrate that entropy-based spatial complexity effectively captures the scale-dependent heterogeneity of stepwise rough surfaces, providing valuable insights into their structural properties. Full article

Accurate spatial mapping of Tailing Impoundments (TIs) is vital for environmental sustainability in mining ecosystems. While remote sensing enables large-scale monitoring, conventional methods relying on single-sensor data and traditional machine learning-based algorithm suffer from reduced accuracy in cluttered environments. This research proposes a deep learning framework leveraging multi-source high-resolution imagery to address these limitations. An upgraded You Only Look Once (YOLO) model is introduced, integrating three key innovations: a multi-scale feature aggregation layer, a lightweight hierarchical fusion mechanism, and a modified loss metric. These components enhance the model’s ability to capture spatial dependencies, optimize inference speed, and ensure stable training dynamics. A comprehensive dataset of TIs across varied terrains was constructed, expanded through affine transformations, spectral perturbations, and adversarial sample synthesis. Evaluations confirm the framework’s superior performance in complex scenarios, achieving higher precision and computational efficiency than state-of-the-art detectors. Full article

(1) Background: The highly pathogenic avian influenza virus H5N1 clade 2.3.4.4b is an emerging threat that poses a great risk to the poultry industry. A few human cases have been linked to the infection with this clade in many parts of the world, including the USA. Unfortunately, there are no specific vaccines or antiviral drugs that could help prevent and treat the infection caused by this virus in birds. Our major objective is to identify/repurpose some (novel/known) antiviral compounds that may inhibit viral replication by targeting some key viral proteins. (2) Methods: We used state-of-the-art machine learning tools such as molecular docking and MD-simulation methods from Biovia Discovery Studio (v24.1.0.321712). The key target proteins such as hemagglutinin (HA), neuraminidase (NA), Matrix-2 protein (M2), and the cap-binding domain of PB2 (PB2/CBD) homology models were validated through structural assessment via DOPE scores, Ramachandran plots, and Verify-3D metrics, ensuring reliable structural representations, confirming their reliability for subsequent in silico approaches. These approaches include molecular docking followed by molecular dynamics simulation for 50 nanoseconds (ns), highlighting the structural stability and compactness of the docked complexes. (3) Results: Molecular docking revealed strong binding affinities for both sofosbuvir and GS441524, particularly with the NA and PB2/CBD protein targets. Among them, GS441524 exhibited superior interaction scores and a greater number of hydrogen bonds with key functional residues of NA and PB2/CBD. The MM-GBSA binding free energy calculations further supported these findings, as GS441524 displayed more favorable binding energies compared to several known standard inhibitors, including F0045S for HA, Zanamivir for NA, Rimantadine and Amantadine for M2, and PB2-39 for PB2/CBD. Additionally, 50 ns molecular dynamics simulations highlighted the structural stability and compactness of the GS441524-PB2/CBD complex, further supporting its potential as a promising antiviral candidate. Furthermore, hydrogen bond monitor analysis over the 50 ns simulation confirmed persistent and specific interactions between the ligand and proteins, suggesting that GS441524 may effectively inhibit the NA, and PB2/CBD might potentially disrupt PB2-mediated RNA synthesis. (4) Conclusions: Our findings are consistent with previous evidence supporting the antiviral activity of certain nucleoside analog inhibitors, including GS441524, against various coronaviruses. These results further support the potential repurposing of GS441524 as a promising therapeutic candidate against H5N1 avian influenza clade 2.3.4.4b. However, further functional studies are required to validate these in silico predictions and support the inhibitory action of GS441524 against the targeted proteins of H5N1, specifically clade 2.3.4.4b. Full article

Raspberry (Rubus idaeus L.) is a high-value horticultural crop recognized for its significant economic importance and exceptional nutritional profile. We analyzed 76 raspberry accessions (wild and cultivar) using simple sequence repeat (SSR) and sequence-related amplified polymorphism (SRAP) markers, and we established a standardized SRAP system for this species. Genetic similarity differed markedly between markers: SSR values spanned 0.47–0.98 (mean = 0.73), compared to the narrower range of 0.52–0.97 (mean = 0.75) for SRAP. Cultivar accessions exhibited higher intra-group homogeneity than wild accessions, and northeastern wild accessions showed more stable similarity metrics than Guizhou wild accessions. In hierarchical clustering, the resolution varied depending on the labeling marker. The cluster analysis by SSR markers identified two main clusters and further partitioned them into three clusters. In contrast, the SRAP system revealed two primary clusters, which subsequently diverged into five subclusters. SSR markers effectively captured population-level differentiation, whereas SRAP markers enabled precise discrimination of cultivars and ecotypes through non-coding region polymorphisms. Phylogenetic analyses confirmed closer genetic affinity between northeastern wild and cultivated accessions, which diverged significantly from Guizhou. This dual-marker approach revealed complementary information: SSR markers were used to survey genome-wide diversity, while SRAP markers were used to detect structural variations. Their integrated application enhances germplasm characterization efficiency and provides practical strategies for raspberry conservation and molecular breeding. Full article

User Identity Linkage (UIL) has emerged as a focal point of research in the field of network analysis and plays a critical role in the governance of cyberspace; related technologies can also be extended for applications in traffic safety and traffic management. The traditional random walk-based UIL method has achieved a balance between performance and interpretability, but it still faces several challenges, such as low discriminability of nodes, instability of feature extraction, and missing features in matching scenarios. To address these challenges, this paper presents Adap-UIL, a multi-feature UIL framework based on an Adaptive Graph Walk. First, we design and implement an Adaptive Graph Walk method based on the Restarted Affinity Coefficient (RAC), which enhances both the neighborhood and higher-order features of nodes, and then we integrate cross-network features to form Adap-UIL with a more enriched node representation, facilitating user identity linkage. Experimental results on real datasets show that the Adap-UIL model outperforms the benchmark models, especially in the P@5 and P@10 metrics by 5 percentage points, and it captures key features more efficiently and effectively. Full article

Among the multiple important properties that characterize strong S-boxes for symmetric cryptography and are used in their designs, this study focuses on two: the non-linearity property, a classical security metric, and the confusion coefficient variance property, a statistical proxy for side channel resistance under the Hamming weight leakage model. Given an S-box, two sets can be created: the set of affine-shifted S-boxes, where S-boxes have the same non-linearity value, and the set of Hamming weight classes, where S-boxes have the same confusion coefficient variance value. The inherent values of these two properties ensure resistance to cryptographic attacks; however, if the value of one property increases, it will imply a decrease in the value of the other property. In view of the aforementioned fact, attaining a trade-off becomes a complex undertaking. The impetus for this research stems from the following hypothesis: if an initial S-box already exhibits a trade-off, it would be advantageous to employ a method that generates new S-boxes while preserving the balance. A thorough review of the extant literature reveals the absence of any methodology that encompasses the aforementioned elements. The present paper proposes a novel methodology for generating an affine-shifted subset of S-boxes, ensuring that the resulting subset possesses the same confusion coefficient variance value. We provide insights on the optimal search strategy to optimize non-linearity and confusion coefficient variance. The proposed methodology guarantees the preservation of constant values on the designated. It is possible to incorporate these properties into a comprehensive design scheme, in which case the remaining S-box properties are to be examined. We also demonstrate that, despite the fact that this subset contains S-boxes with the theoretical resistance to side channel attacks under the Hamming weight model, the S-boxes are in different Hamming weight classes. Full article

The effective management of High-Level Liquid Waste (HLLW) is critical for environmental and human health protection. The presence of platinum group metals (PGMs) in HLLW, particularly their refractory nature due to their high melting points, complicates vitrification processes. This study presents a targeted adsorption strategy using COF-42 for Pd²⁺ sequestration in HLLW systems. The comprehensive characterization of COF-42 and its Pd-loaded counterpart (Pd@COF-42) via PXRD, FT-IR, TGA, XPS, and SEM confirms structural robustness and successful Pd²⁺ incorporation. Kinetic and thermodynamic analyses reveal pseudo-second-order adsorption behavior with a maximum capacity of 170.6 mg/g, highlighting the exceptional Pd²⁺ affinity. Systematic optimization identifies HNO₃ concentration (≤3 M) and adsorbent dosage (≤30 mg) as critical parameters governing adsorption efficiency through protonation–deprotonation equilibria. Furthermore, COF-42 exhibits superior selectivity for Pd²⁺ over 13 competing metal ions and maintains ~80% adsorption efficiency after four regeneration cycles. These performance metrics originate from the synergistic interplay of (1) framework flexibility enabling adaptive Pd²⁺ coordination, (2) hierarchical porosity facilitating ion diffusion, and (3) dense –NH/–NH₂ groups acting as electron-rich chelation sites. Full article

Biosurfactants have emerged as promising agents for environmental remediation due to their ability to complex, chelate, and remove heavy metals from contaminated environments. This review evaluates their potential for recovering critical minerals from waste materials to support renewable energy production, emphasizing the role of biosurfactant–metal interactions in advancing green recovery technologies and enhancing resource circularity. Among biosurfactants, rhamnolipids demonstrate a high affinity for metals such as lead, cadmium, and copper due to their strong stability constants and functional groups like carboxylates, with recovery efficiencies exceeding 75% under optimized conditions. Analytical techniques, including Inductively Coupled Plasma Mass Spectrometry (ICP-MS), Fourier-Transform Infrared spectroscopy (FTIR), and Scanning Electron Microscopy (SEM), are instrumental in assessing recovery efficiency and interaction mechanisms. The review introduces a Green Chemistry Metrics Framework for evaluating biosurfactant-based recovery processes, revealing 70–85% lower Environmental Factors compared to conventional methods. Significant research gaps exist in applying biosurfactants for extraction of metals like lithium and cobalt from batteries and other waste materials. Advancing biosurfactant-based technologies hold promise for efficient, sustainable metal recovery and resource circularity, addressing both resource scarcity and environmental protection challenges simultaneously. Full article

The subjects of our study are affine hypersurfaces $f:M\to {\mathbb{R}}^{2n+2}$ considered with a transversal vector field C, which is $\tilde{J}$-tangent. By $\tilde{J}$ we understand the canonical paracomplex structure on ${\mathbb{R}}^{2n+2}$. The vector field C induces on the hypersurface f an almost paracontact structure $(\phi ,\xi ,\eta )$. We obtain a complete classification of hypersurfaces admitting a metric induced almost paracontact structure with respect to the second fundamental form. We show that, in this case, the $\tilde{J}$-tangent transversal vector field is restricted to centroaffine and the hypersurface must be a piece of hyperquadric. It is demonstrated that these hyperquadrics have a very specific form. A three-dimensional example is also given. Moreover, we establish an equivalence relation between almost paracontact metric structures, para $\alpha$-contact metric structures, and para $\alpha$-Sasakian structures. Methods of affine differential geometry, as well as paracomplex/paracontact geometry, are used. Full article

Understanding fish habitats is essential for fisheries management, habitat restoration, and species protection. Automated fish detection is a key tool in these applications, which enables real-time monitoring and quantitative analysis. Recent advancements in high-resolution cameras and machine learning technologies have facilitated image analysis automation, promoting remote fish tracking. However, many of these detection methods require large volumes of annotated data, which involve considerable effort and time. Additionally, their practical implementation remains challenging in environments with limited data. Hence, this study proposes an anomaly-based fish detection approach by integrating Patch Distribution Modeling with data augmentation techniques, including Affine Transformations, Neural Filters, and SinGAN. Field experiments were conducted in Lake Izunuma-Uchinuma, Japan, using an electrofishing boat to acquire data. Evaluation metrics, such as AUROC and F1-score, assessed detection performance. The results indicate that, compared to the original dataset (AUROC: 0.836, F1-score: 0.483), Neural Filters (AUROC: 0.940, F1-score: 0.879) and Affine Transformations (AUROC: 0.942, F1-score: 0.766) improve anomaly detection. However, SinGAN exhibited no measurable enhancement, indicating the necessity for further optimization. This shows the potential of the proposed approach to enhance automated fish detection in limited-data environments, supporting aquatic ecosystem sustainability. Full article

Autolysis in sea cucumber has long been a threat to raw material storage and product processing. The involvement of endogenous cysteine protease in sea cucumber autolysis has been proved extendedly. However, as an essential part of the mechanism of autolysis, the role of its endogenous inhibitor has seldom been reported. To investigate the role of cysteine protease inhibitors in the early stage of hypoxic exposure-induced autolysis, a novel cystatin gene (SjCyt) belonging to the subfamily of cystatin C was cloned from Apostichopus japonicus by homology cloning and rapid amplification of cDNA ends. The affinity of SjCyt to cysteine protease (cathepsin L and cathepsin B) was investigated by molecular dynamics simulations. Pertinent metrics, including the root mean square deviation, radius of gyration, Gibbs free energy, binding free energy, and bond-forming frequency, showed that the conformation of SjCyt–SjCL was more stable and confirmed a stronger interaction of SjCyt with cathepsin L than with cathepsin B. Thus, cathepsin L (SjCL) was selected to further study its co-expression with SjCyt over a period of 9 h at an early stage of hypoxic exposure. Quantitative RT-qPCR revealed a ubiquitous transcriptional profile of SjCyt and SjCL in all the tested tissues, with the highest abundance in the dorsal epidermis, tube feet, and coelomocytes. Temporal transcription of them showed an overall up-regulated co-expression in the dorsal epidermis and tube feet. However, up-regulated SjCyt and down-regulated SjCL were observed at the protein level. Further immunofluorescence double labeling also found increased staining of SjCyt and SjCyt–SjCL complexes and decreased SjCL. Additionally, recombinant SjCyt was prepared and demonstrated an evident autolysis-inhibiting effect. The results of this study indicated that the anti-autolytic regulation of SjCyt functions at the very early stage of hypoxic exposure, exerting effects at both the transcriptional and translational levels. The above finding offers new insights into the mechanisms of sea cucumber autolysis. Full article

The primary objective of this study was to identify and characterize pathogen defense proteins in the Nicotiana tabacum L. proteome, focusing on their structural, functional, and evolutionary properties, as well as their interactions with pathogen-derived molecules. Specifically, we aimed to comprehensively analyze the proteome to pinpoint potential uncharacterized defense-related protein that has emerging roles in immune responses and antioxidant activity across plants and animals. Through integrated computational approaches, we determined evolutionary relationships, and structural modeling of the selected protein was performed using different modeling software, followed by validation through multiple metrics, including stereochemical checks (Ramachandran plot), MolProbity analysis, and Z-scores. We further investigated the functional binding regions or interaction sites. We performed molecular docking to investigate the molecular interactions between selected proteins and pathogen-associated molecular patterns (PAMPs), specifically β-glucan and peptidoglycan (PGN), to elucidate their defensive mechanisms. Last, normal mode analysis (NMA), molecular dynamics simulation (MDS), and post-simulation analyses were employed to evaluate the stability and mobility of the protein–ligand complexes. Uncharacterized vitellogenin-like protein (VLP: ID A0A1S4CXB2) with the potential defense domain chosen because of its predicted immune-related features, stress response patterns, and unknown pathogen role at new immunity functions. Phylogenetic analysis revealed significant sequence homology with VLPs from other members of the Solanaceae family. Structural modeling showed a high-quality model, with docking studies indicating a stronger affinity for PGN (−10.16 kcal/mol) and β-glucan (−7.19 kcal/mol), highlighting its potential involvement in pathogen defense. NMA, MDS, and post-simulation analyses revealed that PGN exhibits more substantial binding stability and more extensive interactions with VLP than β-glucan. Our findings confirmed that VLPs in N. tabacum may function as pattern recognition receptors (PRRs), capable of recognizing and responding to pathogens by activating immune signaling pathways. Future experimental validation of these interactions could further elucidate the role of VLPs in plant defense and their potential application in biotechnological approaches for sustainable agriculture. Full article

The polynomial affine model of gravity was proposed as an alternative to metric and metric-affine gravitational models. What, in the beginning, was thought to be a source of unpredictability—the presence of many terms in the action—turned out to be a milestone since it contains all possible combinations of the fields compatible with the covariance under diffeomorphisms. Here, we present a review of the advances in the analysis of the model after 10 years of its proposal and sketch the guidelines for our future perspectives. Full article

Dietary and environmental exposure to aflatoxins via contaminated food items can pose major health challenges to both humans and animals. Studies have reported the coexistence of aflatoxins and other environmental toxins. This emphasizes the urgent need for efficient and effective mitigation strategies for aflatoxins. Previous reports from our laboratory have demonstrated the potency of the green-engineered clays (GECs) on ochratoxin and other toxic chemicals. Therefore, this study sought to investigate the binding and detoxification potential of chlorophyll (CMCH and SMCH) and chlorophyllin (CMCHin and SMCHin)-amended montmorillonite clays for aflatoxin B1 (AFB1). In addition to analyzing binding metrics including affinity, capacity, free energy, and enthalpy, the sorption mechanisms of AFB1 onto the surfaces of engineered clays were also investigated. Computational and experimental studies were performed to validate the efficacy and safety of the clays. CMCH showed the highest binding capacity (Qmax) of 0.43 mol/kg compared to the parent clays CM (0.34 mol/kg) and SM (0.32 mol/kg). Interestingly, there were no significant changes in the binding capacity of the clays at pH2 and pH6, suggesting that the clays can bind to AFB1 throughout the gastrointestinal track. In silico investigations employing molecular dynamics simulations also demonstrated that CMCH enhanced AFB1 binding as compared to parent clay and predicted hydrophobic interactions as the main mode of interaction between the AFB1 and CMCH. This was corroborated by the kinetic results which indicated that the interaction was best defined by chemosorption with favorable thermodynamics and Gibbs free energy (∆G) being negative. In vitro experiments in Hep G2 cells showed that clay treatment mitigated AFB1-induced cytotoxicity, with the exception of 0.5% (w/v) SMCH. Finally, the in vivo results validated the protection of all the clays against AFB1-induced toxicities in Hydra vulgaris. This study showed that these clays significantly detoxified AFB1 (86% to 100%) and provided complete protection at levels as low as 0.1%, suggesting that they may be used as AFB1 binders in feed and food. Full article