Search Results (777)

For the first time, the interaction between the amino acid leucine (Leu) and thermally denatured single-stranded (ss) DNA has been demonstrated by applying voltammetry. As a result of interaction, the characteristic peak of ssDNA, due to the oxidation of guanine residues, decreased upon interaction time. The interaction behavior between leucine and ssDNA was also studied with UV–vis spectrophotometry; the obtained results are in good agreement with voltammetric ones. The results of the interaction study were exploited in order to develop a SWV method for the determination of leucine at the ssDNA-modified carbon paste electrode (CPE). Different parameters were tested to optimize the conditions of the determination. The peak of guanine was at around +0.86 V. Linearity was observed in the range of 0.213–4.761 μg/L (r = 0.9990) while LOD equals 0.071 μg/L. The method was applied to a spiked soil sample and gave satisfactory results. Full article

Accurate thickness recognition plays a vital role in safeguarding the structural reliability of critical assets. Pulse eddy current testing (PECT), as a non-destructive method that is both non-contact and insensitive to surface coatings, provides an efficient pathway for this purpose. Nevertheless, the complex, nonstationary, and nonlinear characteristics of PECT signals make it difficult for conventional models to jointly capture localized high-frequency patterns and long-range temporal dependencies, thereby constraining their prediction performance. To overcome these issues, we introduce a novel deep learning framework, multi-scale residual dilated convolution, and bidirectional long short-term memory with a squeeze-and-excitation mechanism (MRDC-BiLSE) for PECT time series analysis. The architecture integrates a multi-scale residual dilated convolution block. By combining dilated convolutions with residual connections at different scales, this block captures structural patterns across multiple temporal resolutions, leading to more comprehensive and discriminative feature extraction. Furthermore, to better exploit temporal dependencies, the BiLSTM-SE module combines bidirectional modeling with a squeeze-and-excitation mechanism, resulting in more discriminative feature representations. Experiments on experimental PECT datasets confirm that MRDC-BiLSE surpasses existing methods, showing applicability for real-world thickness recognition. Full article

The regular ore body of the Xianglushan tungsten mine has been completely exploited. The remaining residual ore bodies face numerous hidden dangers, such as large and numerous abandoned mining areas, disorderly and small-scale mining sequences, delayed filling processes, and poor effectiveness. To achieve the zoning warning of ground pressure disasters such as roof caving, caving, and pillar collapse during the mining process of the hidden-danger ore body in the mine, a targeted warning technology system is proposed. We use microseismic monitoring systems to analyze events in the main monitoring areas and summarize specific ground pressure manifestation areas and event characteristics. Based on microseismic monitoring data that identified areas of significant ground pressure, a zoning model was constructed for risk rating and area locking. Based on this model, a safety warning technology for mining residual ore bodies with hidden dangers was established. Summarizing and analyzing, it is found that the disaster warning mode for controlling hidden dangers and residual ore body mining processes through microseismic monitoring is effective and has played a certain demonstration role, providing reference value for other similar mines. Full article

The SETD6 protein lysine methyltransferase monomethylates specific lysine residues in a diverse set of substrates which contain the target lysine residue in a highly variable amino acid sequence context. To investigate the mechanism underlying this multispecificity, we analyzed SETD6 substrate recognition using AlphaFold 3 docking and peptide SPOT array methylation experiments. Structural modeling of the SETD6–E2F1 complex suggested that substrate binding alone is insufficient to restrict SETD6 activity to only one lysine residue, pointing to additional sequence readout at the target site. Methylation of mutational scanning peptide SPOT arrays derived from four different SETD6 substrates (E2F1 K117, H2A.Z K7, RELA K310, and H4 K12) revealed sequence preferences of SETD6 at positions −1, +2, and +3 relative to the target lysine. Notably, glycine or large aliphatic residues were favored at −1, isoleucine/valine at +2, and lysine at +3. These preferences, however, were sequence context dependent and variably exploited among different substrates, indicating conformational variability of the enzyme–substrate interface. Mutation of SETD6 residue L260, which forms a contact with the +2 site in the available SETD6-RELA structure, further demonstrated substrate-specific differences in recognition at the +2/+3 sites. Together, these findings reveal a versatile mode of peptide recognition in which the readout of each substrate position depends on the overall substrate peptide sequence. These findings can explain the multispecificity of SETD6 and similar mechanisms may underlie substrate selection in other protein methyltransferases. Full article

Cardiac arrhythmia is a leading cause of sudden cardiac death. Its early detection and continuous monitoring hold significant clinical value. Photoplethysmography (PPG) signals, owing to their non-invasive nature, low cost, and convenience, have become a vital information source for monitoring cardiac activity and vascular health. However, the inherent non-stationarity of PPG signals and significant inter-individual variations pose a major challenge in developing highly accurate and efficient arrhythmia classification methods. To address this challenge, we propose a Fusion Deep Multi-domain Attention Network (Fusion-DMA-Net). Within this framework, we innovatively introduce a cross-scale residual attention structure to comprehensively capture discriminative features in both the time and frequency domains. Additionally, to exploit complementary information embedded in PPG signals across these domains, we develop a fusion strategy integrating interactive attention, self-attention, and gating mechanisms. The proposed Fusion-DMA-Net model is evaluated for classifying four major types of cardiac arrhythmias. Experimental results demonstrate its outstanding classification performance, achieving an overall accuracy of 99.05%, precision of 99.06%, and an F1-score of 99.04%. These results demonstrate the feasibility of the Fusion-DMA-Net model in classifying four types of cardiac arrhythmias using single-channel PPG signals, thereby contributing to the early diagnosis and treatment of cardiovascular diseases and supporting the development of future wearable health technologies. Full article

Pleurotus ostreatus is among the most widely cultivated mushroom species on a global scale, valued for its relative ease of cultivation, excellent organoleptic qualities, and notable nutraceutical properties. P. ostreatus could use a wide range of by-products as growth substrates by excreting a potent array of hydrolytic and oxidative enzymes. In this study, a diverse range of agricultural residues and agro-industrial by-products, enriched (or not) with various supplements, was evaluated for the cultivation of five commercial P. ostreatus strains. Key cultivation parameters were assessed, including biological efficiency and productivity. A process analytical technology (PAT) approach, utilizing FTIR spectroscopy in combination with multivariate analysis, was employed to develop a predictive model for biological efficiency based solely on substrate’s spectral profile. Substrates based on wheat and barley straw supplemented with soybean demonstrated superior performance across most strains. The biological efficiency value reached 185% in some cases for a total cultivation period of only 35 days. The resulting model exhibited excellent predictive capability, with a coefficient of determination (R²) of 0.90 and low relative prediction error of just 6%. The developed innovative PAT tool will be beneficial for mushroom growers since it allows the fast and costless evaluation of agro-industrial by-products in respect to their potential exploitation as mushroom substrates. Full article

Intelligent Reflective Surface (IRS)-aided Multiple-Input Multiple-Output (MIMO) systems have emerged as a promising solution to enhance spectral and energy efficiency in future wireless communications. However, accurate channel estimation remains a key challenge due to the passive nature and high dimensionality of IRS channels. This paper proposes a lightweight hybrid framework for cascaded channel estimation by combining a physics-based Bilinear Alternating Least Squares (BALS) algorithm with a deep neural network named ConvTrans-ResNet. The network integrates convolutional embeddings and Transformer modules within a residual learning architecture to exploit both local and global spatial features effectively while ensuring training stability. A series of ablation studies is conducted to optimize architectural components, resulting in a compact configuration with low parameter count and computational complexity. Extensive simulations demonstrate that the proposed method significantly outperforms state-of-the-art neural models such as HA02, ReEsNet, and InterpResNet across a wide range of SNR levels and IRS element sizes in terms of the Normalized Mean Squared Error (NMSE). Compared to existing solutions, our method achieves better estimation accuracy with improved efficiency, making it suitable for practical deployment in IRS-aided systems. Full article

Malaria persists as a paradigmatic model of co-evolutionary complexity, emerging from the dynamic interplay among a human host, Anopheles vectors, and Plasmodium falciparum parasites. In human populations, centuries of selective pressures have sculpted an intricate and heterogeneous immunogenetic landscape. Classical adaptations, such as hemoglobinopathies, are complemented by a diverse array of genetic polymorphisms that modulate innate and adaptive immune responses. These genetic traits, along with the acquisition of functional immunity following repeated exposures, mitigate disease severity but are continually challenged by the parasite’s highly evolved mechanisms of antigenic variation and immunomodulation. Such host adaptations underscore an evolutionary arms race that perpetually shapes the clinical and epidemiological outcomes. Intermediaries in malaria transmission have evolved robust responses to both natural and anthropogenic pressures. Their vector competence is governed by complex polygenic traits that affect physiological barriers and immune responses during parasite development. Recent studies reveal that these mosquitoes exhibit rapid behavioral and biochemical adaptations, including shifts in host-seeking behavior and the evolution of insecticide resistance. Mechanisms such as enhanced metabolic detoxification and target site insensitivity have emerged in response to the widespread use of insecticides, thereby eroding the efficacy of conventional interventions like insecticide-treated bed nets and indoor residual spraying. These adaptations not only sustain transmission dynamics in intervention saturated landscapes but also challenge current vector control paradigms, necessitating the development of innovative, integrated management strategies. At the molecular level, P. falciparum exemplifies evolutionary ingenuity through extensive genomic streamlining and metabolic reconfiguration. Its compact genome, a result of strategic gene loss and pruning, is optimized for an obligate parasitic lifestyle. The repurposing of the apicoplast for critical anabolic functions including fatty acid, isoprenoid, and haem biosynthesis highlights the parasite’s ability to exploit host derived nutrients efficiently. Moreover, the rapid accumulation of mutations, coupled with an elaborate repertoire for antigenic switching and epigenetic regulation, not only facilitates immune escape but also accelerates the emergence of antimalarial drug resistance. Advanced high throughput sequencing and functional genomics have begun to elucidate the metabolic epigenetic nexus that governs virulence gene expression and antigenic diversity in P. falciparum. By integrating insights from molecular biology, genomics, and evolutionary ecology, this study delineates the multifaceted co-adaptive dynamics that render malaria a recalcitrant global health threat. Our findings provide critical insights into the molecular arms race at the heart of host–pathogen vector interactions and underscore promising avenues for the development of next generation therapeutic and vector management strategies aimed at sustainable malaria elimination. Full article

Hemp (Cannabis sativa L.) is a high-yielding crop cultivated for fiber and seed production, generating substantial lignocellulosic residues such as hurds. These byproducts can be valorized through pyro-gasification, a thermochemical process that offers a sustainable alternative to combustion and produces biochar—a promising soil amendment due to its ability to enhance soil quality and mitigate drought stress. This research explores the viability of utilizing industrial hemp hurds as a direct feedstock for biochar production within the context of agricultural exploitation. The study specifically focuses on assessing the feasibility of converting raw, unprocessed hemp hurds into biochar through pyrolysis. A comprehensive characterization of the resulting biochar is conducted to evaluate its properties and potential applications in agriculture, establishing a foundational understanding for future agronomic use. Specific analysis included proximate and ultimate analysis, thermogravimetric analysis (TGA), SEM-EDS, and phytotoxicity testing. The biochar exhibited an alkaline pH (≥9), a low H/C ratio (0.37), and suitable macro- and micronutrient levels. Microstructural analysis revealed a porous architecture favorable for nutrient retention and water absorption. Germination tests with corn (Zea mays L.) showed a germination index above 90% for substrates containing 0.5–1% biochar. These findings establish a foundation for future research aimed at thoroughly exploring the agricultural potential of this material. Full article

Purpose: This systematic review aims to analyze the literature on the application of AI in predicting patient outcomes and treatment-related toxicity in those undergoing SBRT or SRS across heterogeneous tumor sites. Materials and methods: Our review conformed to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. PubMed, EMBASE and Scopus were systematically searched for English-language human studies evaluating AI for outcome and toxicity prediction in patients undergoing SBRT or SRS for solid tumors. Search terms included (“Stereotactic Body Radiotherapy” OR “SBRT” OR “Stereotactic Radiosurgery” OR “SRS” OR “Stereotactic Ablative Radiotherapy” OR “SABR”) AND (“Artificial Intelligence” OR “AI” OR “Machine Learning” OR “Deep Learning” OR “Radiomics”) AND (“Response Prediction” OR “Response to Treatment” OR “Outcome Prediction”) AND (“Toxicity” OR “Side Effects” OR “Treatment Toxicities” OR “Adverse Events”). Results: The search yielded 29 eligible retrospective studies, published between 2020 and 2025. Eight studies addressed early-stage primary lung cancer, highlighting the potential of AI-based models in predicting radiation-induced pneumonitis, fibrosis and local control. Five studies investigated AI models for predicting hepatobiliary toxicity following SBRT for liver tumors. Sixteen studies involved SRS-treated patients with brain metastases or benign intracranial neoplasms (e.g., arteriovenous malformations, vestibular schwannomas, meningiomas), exploring AI algorithms for predicting treatment response and radiation-induced changes. In the results, AI might have been exploited to both reaffirm already known clinical predictors and to identify novel imaging, dosimetric or biological biomarkers. Examples include predicting radiation pneumonitis in lung cancer, residual liver function in hepatic tumors and local recurrence in brain metastases, thus supporting tailored treatment decisions. Conclusions: Combining AI with SBRT could greatly enhance personalized cancer care by predicting patient-specific outcomes and toxicity. AI models analyze complex datasets, including imaging and clinical data, to identify patterns that traditional methods may miss, thus enabling more accurate risk stratification and reducing variability in treatment planning. With further research and clinical validation, this integration could make radiotherapy safer, more effective and contribute to advancement in precision oncology. Full article

This research introduces a novel de-anonymization technique targeting the Tor network, addressing limitations in prior attack models, particularly concerning router positioning following the introduction of bridge relays. Our method exploits two specific, inherent protocol-level vulnerabilities: the absence of a continuity check for circuit-level cells and anomalous residual values in RELAY_EARLY cell counters, working by manipulating cell headers to embed a covert signal. This signal is composed of reserved fields, start and end delimiters, and a payload that encodes target identifiers. Using this signal, malicious routers can effectively mark data flows for later identification. These routers employ a finite state machine (FSM) to adaptively switch between signal injection and detection. Experimental evaluations, conducted within a controlled environment using attacker-controlled onion routers, demonstrated that the embedded signals are undetectable by standard Tor routers, cause no noticeable performance degradation, and allow reliable correlation of Tor users with public services and deanonymization of hidden service IP addresses. This work reveals a fundamental design trade-off in Tor: the decision to conceal circuit length inadvertently exposes cell transmission characteristics. This creates a bidirectional vector for stealthy, protocol-level de-anonymization attacks, even though Tor payloads remain encrypted. Full article

In the exploitation of ion-adsorption rare earth ores, the environmental effects of leaching agents are key constraints for green mining. Understanding the release behavior of typical heavy metals from soils under leaching conditions is of great significance. Laboratory column leaching experiments were conducted to systematically investigate the effects of three leaching agents—(NH₄)₂SO₄, Al₂(SO₄)₃, and MgSO₄—as well as varying concentrations of Al₂(SO₄)₃ on the release and speciation transformation of heavy metal Pb in mining-affected soils. The results revealed a three-stage pattern in Pb release—characterized by slow release, a sharp increase, and eventual stabilization—with environmental risks predominantly concentrated in the middle to late stages of leaching. Under 3% (NH₄)₂SO₄ and 3% Al₂(SO₄)₃ leaching conditions, Pb concentrations in soil increased significantly, with a higher proportion of labile fractions, indicating pronounced activation and risk accumulation. Due to its relatively weak ion-exchange capacity, MgSO₄ exhibited a lower and more gradual Pb release profile, posing substantially lower pollution risks compared to (NH₄)₂SO₄ and Al₂(SO₄)₃. Pb release under varying Al₂(SO₄)₃ concentrations showed a nonlinear response. At 3% Al₂(SO₄)₃, both the proportion of bioavailable Pb and the Risk Assessment Code (RAC) peaked, while the residual fraction declined sharply, suggesting a threshold effect in risk induction. All three leaching agents promoted the transformation of Pb in soil from stable to more labile forms, including acid-soluble, reducible, and oxidizable fractions, thereby increasing the overall proportion of active Pb (F1 + F2 + F3). A combined analysis of RAC values and the proportion of active Pb provides a comprehensive framework for assessing Pb mobility and ecological risk under different leaching conditions. These findings offer a theoretical basis for the prevention and control of heavy metal risks in the green mining of ion-adsorption rare earth ores. Full article

Dual-specificity protein phosphatases (DUSPs) are a family of proteins that dephosphorylate both phospho-serine/threonine and phospho-tyrosine residues of Mitogen-Activated Protein Kinases (MAPKs). MAPKs are involved in a large number of cellular processes, including proliferation, differentiation, apoptosis, and stress responses. Therefore, dysregulation or improper functioning of the MAPK signalling is involved in the onset and progression of several diseases, including cancer. Likewise, dysregulation of DUSPs markedly affects cancer biology. The importance of MAPKs in the modulation of tumour development has been known for a long time, and MAPKs are consistently used as molecular targets for cancer therapy. However, in the last decade, DUSPs have acquired a greater interest as possible therapeutic targets to regulate MAPK activity and to prevent resistance mechanisms to MAPK-targeting therapies. Moreover, the possibility of exploiting DUSPs as biomarkers for the diagnosis and prognosis of specific types of cancer is also emerging. In this review, we report what is known in the literature on the role of DUSPs in cancer onset and progression, focusing on those targeting the extracellular signal-regulated kinases (ERKs), in particular ERK1/2 and ERK5 conventional MAPKs. The specific role of each ERK-targeting DUSP in supporting or hampering cancer progression in the context of different types of cancer is also discussed. Full article

The integer precise point positioning (IPPP) technique significantly improves the accuracy of positioning and time and frequency transfer by restoring the integer nature of carrier-phase ambiguities. However, in practical applications, IPPP performance is often degraded by day-boundary discontinuities and instances of incorrect ambiguity resolution, which can compromise the reliability of time transfer. To address these challenges and enable continuous, robust, and stable IPPP time transfer, this study proposes an effective approach that utilizes narrow-lane ambiguities to absorb receiver clock jumps, combined with a robust sliding-window weighting strategy that fully exploits multi-epoch information. This method effectively mitigates day-boundary discontinuities and employs adaptive thresholding to enhance error detection and mitigate the impact of incorrect ambiguity resolution. Experimental results show that at an averaging time of 76,800 s, the frequency stabilities of GPS, Galileo, and BDS IPPP reach 4.838 × 10⁻¹⁶, 4.707 × 10⁻¹⁶, and 5.403 × 10⁻¹⁶, respectively. In the simulation scenario, the carrier-phase residual under the IGIII scheme is 6.7 cm, whereas the robust sliding-window weighting method yields a lower residual of 5.2 cm, demonstrating improved performance. In the zero-baseline time link, GPS IPPP achieves stability at the 10⁻¹⁷ level. Compared to optical fiber time transfer, the GPS IPPP solution demonstrates superior long-term performance in differential analysis. For both short- and long-baseline links, IPPP consistently outperforms the PPP float solution and IGS final products. Specifically, at an averaging time of 307,200 s, IPPP improves average frequency stability by approximately 29.3% over PPP and 32.6% over the IGS final products. Full article

The primary contradiction in mature oilfields during the high water-cut stage is the uneven vertical water drive, which prevents the effective utilization of residual oil in the upper part of thick sand bodies at small scales. To address this issue, ultra-short-radius horizontal wells are employed to establish large-diameter oil flow channels within the reservoir, thereby achieving precise exploitation of this type of residual oil. Optimizing the length of the horizontal section is a critical issue in the development of small-scale residual oil, but conventional methods for optimizing the length of horizontal sections cannot be directly applied to ultra-short-radius horizontal wells (USRHWs). Therefore, utilizing reservoir seepage mechanics theory, the reservoir numerical simulation method was employed to investigate variations in daily and cumulative oil production for different horizontal section lengths. The theoretical upper limit of the optimal horizontal section length for actual injection and production well patterns was determined. Considering the coupled flow characteristics in the bottom water drive reservoir formation and wellbore, as well as the impact of friction losses caused by the relative roughness of the pipe wall under turbulent flow conditions on productivity, a mathematical model was established for the optimal length of the horizontal section of USRHWs, and the technological optimal value was determined. On this basis, fully accounting for the influence of drilling costs and oil prices on the optimization of the horizontal section length, an economic model for optimizing horizontal section length was established, and we comprehensively determine the optimal length of horizontal sections from multiple perspectives, including simulation, technology, and economics. The effectiveness of this method was validated by the processing results of actual reservoir parameters and the production performance after drilling. Full article