Search Results (2,638)

A simple and cost-effective graphite paste electrode modified with 5-(4-aminophenyl)-4-amino-1,2,4-triazole-3(2H)-thione was developed for the selective voltammetric determination of Cu(II) ions in environmental water samples. The N,S-donor ligand was incorporated into a graphite–polystyrene matrix without the use of nanomaterials, providing a reproducible and straightforward electrode fabrication route. Scanning electron microscopy revealed a rough, porous surface morphology with an enhanced electroactive surface area of 0.065 cm², approximately twice the geometric area. Electrochemical impedance spectroscopy confirmed diffusion-controlled mass transport, while cyclic voltammetry indicated quasi-reversible behaviour of the Cu(II)/Cu(0) redox system with a linear dependence of peak current on the square root of the scan rate. Differential pulse voltammetry under optimised conditions (0.1 mol·L⁻¹ H₂SO₄, pH 1.0–1.2) yielded a linear analytical response over the concentration range of 0.01–0.4 μmol·L⁻¹ (R² = 0.99507), with a limit of detection of 0.02 μmol·L⁻¹ and a limit of quantification of 0.06 μmol·L⁻¹—well below the WHO guideline for copper in drinking water. The sensing mechanism involves selective N,S-bidentate coordination of Cu(II) at the electrode surface, followed by electrochemical reduction, as supported by FT-IR spectroscopic evidence. The sensor demonstrated good selectivity toward Cu(II) in the presence of common interfering metal ions at up to 20-fold excess. The method was successfully validated against ICP-OES (recovery 99.8%, RSD < 0.33%) and confirmed by spike–recovery experiments (99.0–99.5%), confirming its practical applicability for trace-level environmental monitoring. The modified electrode retained approximately 93% of its initial response after 30 consecutive measurements and 91% after 14 days of storage, demonstrating good operational stability. Full article

Rapid, point-of-care detection of Escherichia coli O157:H7 remains an unmet clinical need, as culture and molecular methods are slow and poorly suited to decentralized or emergency settings. A label-free, monolithic aptasensor biochip was fabricated in a standard 65 nm CMOS process, featuring three aptamer-functionalized gold sensing pads with matched reference pads for differential readout. A 37-mer DNA aptamer targeting the E. coli O157:H7 lipopolysaccharide was immobilized via thiol–gold self-assembled monolayer chemistry. Binding events were transduced into surface-potential shifts, amplified by an on-chip analog front-end (~100 V/V gain, 101.5 µW), and evaluated using calibration standards, patient specimens, and hospital environmental samples, with fluorescence microscopy for validation. The sensor achieved 47.42 mV/decade sensitivity across 1–10,000 CFU/mL, an IUPAC detection limit near 3.74 CFU/mL, and an empirical LOD of about 11 CFU/mL, with outputs tracking bacterial load and ~5.7% matrix-related deviation. Hospital samples were detectable to 28 CFU/mL. Because the patient-derived and hospital-acquired cohorts (n = 10 and n = 6, respectively) were assembled for pilot analytical and matrix-tolerance characterization rather than for diagnostic-accuracy determination, these results establish detectability and matrix robustness in real clinical and environmental specimens rather than clinical diagnostic sensitivity or specificity, which will require a larger, prospectively enrolled cohort in future work. Sensor kinetics followed Langmuir-type adsorption, saturating within 16–25 min for target pathogens versus slower responses for non-target strains. Selectivity tests against six bacterial species showed discrimination, with cross-reactivity decreasing from related E. coli pathotypes to Enterobacteriaceae to Gram-positive species. Inter-pad variability stayed below 1.5 mV, supporting this compact, low-power platform for scalable, enrichment-free point-of-care pathogen detection. Full article

Meat flavor significantly influences consumer preference and market value, particularly for indigenous pig breeds renowned for distinctive sensory characteristics. While traditional research has focused on genetic factors and feeding regimens, emerging evidence suggests that gut microbiota plays a crucial role in meat quality attributes. However, the specific contribution of bacterial–fungal co-occurrence to meat flavor formation remains largely unexplored. This study aimed to characterize the associations between intestinal bacterial–fungal co-occurrence networks and the muscle flavor-related metabolite profiles of CX pigs, using an integrated multi-omics approach. Twenty male pigs (10 CX and 10 LAN, 12 months old) were subjected to comprehensive analyses, including meat quality evaluation, electronic nose analysis, 16S and 18S rRNA sequencing, and untargeted metabolomics. CX pigs exhibited significantly superior meat quality characteristics, including higher moisture content (p < 0.001), fat content (p = 0.008), and meat color scores (p < 0.001). Electronic nose analysis revealed significantly higher response values across all ten aroma sensors in CX pigs (p < 0.001), with the most pronounced differences observed in sensors detecting sulfur compounds and organic compounds. Untargeted metabolomics identified 40 differential metabolites, with 27 up-regulated in CX pigs, including key flavor compounds such as glycocholic acid, isorhamnetin, and pantothenic acid. Microbiome analysis demonstrated significantly higher bacterial alpha diversity in CX pigs (p < 0.05), with enrichment of beneficial bacteria, including Rikenellaceae_RC9_gut_group, Prevotellaceae_UCG_003, and Phascolarctobacterium, while fungal communities showed enrichment of Candida_Lodderomyces_clade. Correlation network analysis revealed that Rikenellaceae_RC9_gut_group demonstrated strong positive correlations with flavor compounds (r = 0.575 for isorhamnetin, r = 0.535 for pantothenic acid, p < 0.001) and all electronic nose responses (r = 0.434–0.691, p < 0.001). Bacterial–fungal co-occurrence networks showed synergistic relationships, with Rikenellaceae_RC9_gut_group positively correlated with Candida_Lodderomyces_clade (r = 0.711, p < 0.001) while exhibiting antagonistic relationships with Piromyces (r = −0.714, p < 0.001). These findings offer novel insights for developing microbiome-targeted strategies to enhance meat quality in pig production systems. Full article

Global Navigation Satellite System Reflectometry (GNSS-R) platforms, such as the CYGNSS constellation, provide unprecedented spatial coverage for monitoring ionospheric scintillation via the $S_4$ index. However, the operational utility of GNSS-R for space weather is substantially degraded by surface-induced signal contamination when sharp land–water boundaries (coastlines) trigger massive, false-positive $S_4$ pseudo-scintillations that imitate true ionospheric plasma irregularities. In this study, a robust machine learning (ML) methodology to autonomously distinguish surface-induced reflections from true atmospheric volumetric scattering was proposed. Using 1 Hz Level 1 continuous Signal-to-Noise Ratio (SNR) time-series data, morphologic features (e.g., maximum amplitude, peak prominence, and standard deviation) were extracted to train a Random Forest (RF) classifier. The model achieves 98% accuracy in differentiating coastal boundaries from ionospheric scintillation, evaluated on a global dataset of over ~450,000 anomalous events. Moreover, a multi-sensor case study of the historic May 2024 G5 geomagnetic storm is presented to validate the geophysical fidelity of the filtered data. The ML-isolated CYGNSS anomalies demonstrate strong spatial correlation with COSMIC-2 Radio Occultation (RO) F2-peak electron density (NmF2) variations and ground-based Rate of TEC Index (ROTI) maps. Furthermore, temporal cross-validation with 1 Hz localized ground magnetometer data in Northwest Mexico reveals positive synchronization between CYGNSS scattering events and localized electrodynamic disturbances. Finally, the results demonstrate that ML-de-aliased GNSS-R data can reliably link the oceanic observational gaps inherent to ground-based networks, offering a powerful new tool for global space weather monitoring. Full article

We present Dipper, a lightweight 64-bit block cipher with 96-bit and 128-bit key variants, built on a 28-round hybrid SPN–ARX structure. Each round applies a full-state key addition, sixteen parallel 4-bit GIFT S-boxes, four word-wise rotations, two 16-bit modular additions over half of the state, and the GIFT-64 bit permutation, combining the compact substitution layer of GIFT-style designs with the diffusion efficiency of ARX operations. We evaluate Dipper from both hardware and cryptanalytic perspectives under a single, fully open-source methodology. Round-based Verilog implementations were synthesized alongside PRESENT, GIFT, and SIMON variants using an identical Yosys + ABC + Nangate45 flow. Under this flow, Dipper-64/96 and Dipper-64/128 require 2498 and 2824 gate equivalents (GE), respectively, both falling between GIFT-64-128 (2191 GE) and PRESENT-128 (2963 GE); notably, Dipper-64/128 is more compact than PRESENT-128 at the same key size, despite incorporating an additional ARX diffusion layer. A broader comparison re-implements eleven established lightweight ciphers under the same flow, and post-place-and-route FPGA results on Lattice ECP5, measured software timings, and Cortex-M memory footprints support deployment across RFID, sensor-node, and edge-gateway scenarios. For differential resistance, we develop a mixed-integer linear programming (MILP) model that couples the exact GIFT differential distribution table with a Lipmaa–Moriai encoding of modular addition. Predicted and empirical differential probabilities agree tightly for reduced-round variants, while five-round trails reveal differential clustering. The security evaluation further includes proven-optimal linear trail bounds up to ten rounds, an exhaustive impossible-differential search bounding the longest distinguisher at five rounds, and experimental integral distinguishers of at most five rounds, leaving the 28-round cipher a margin close to 3× against the longest identified distinguisher. All RTL, synthesis scripts, reference implementations, and MILP models are released for full reproducibility. Full article

Anthracycline agents, principally doxorubicin and daunorubicin, are widely used in oncology yet carry a narrow therapeutic index and pronounced interindividual pharmacokinetic variability that exposes patients simultaneously to the risk of subtherapeutic dosing and cumulative cardiotoxicity. Conventional therapeutic drug monitoring (TDM) based on periodic venous sampling and offline high-performance liquid chromatography cannot resolve the sub-minute concentration dynamics that determine organ-specific drug exposure. Electrochemical aptamer-based (EAB) sensors couple nucleic-acid aptamers, self-assembled monolayers, and methylene blue redox reporters on gold microelectrodes to convert binding-induced conformational changes into real-time, reagent-free electrochemical signals. Recent advances in this field fall into five areas: signal interrogation strategies, from kinetic differential measurement to calibration-free Fourier-transform impedance spectroscopy (FFT-EIS); interface engineering including nanostructured electrodes and AI-guided aptamer design; in vivo multi-compartment pharmacokinetic monitoring and closed-loop feedback drug delivery; the mechanisms of in vivo signal drift alongside antifouling countermeasures spanning hydrogel barriers, zwitterionic brushes, and xenonucleic acid backbone substitution; and FDA premarket pathways and clinical translation, including Premarket Approval requirements and the emerging Real-Time Clinical Trial (RTCT) framework. In live rodents, dual-compartment monitoring has resolved a reproducible 30–60 min plasma-to-ISF lag for doxorubicin at 12 s temporal resolution; calibration-free FFT-EIS interrogation achieves inter-animal coefficients of variation below 12% without individual pre-calibration; and xenonucleic acid backbone substitution has extended continuous in vivo operation to seven consecutive days. Unlike prior EAB reviews that survey general molecular targets or benchtop aptasensors, this review uniquely integrates anthracycline-specific in vivo pharmacokinetics, multi-compartment plasma–ISF monitoring, calibration-free interrogation, XNA-enabled long-term stability, and FDA/RTCT regulatory translation into a single clinical roadmap. Three gaps still separate rodent proof-of-concept work from chemotherapy patients: clinical-context validation, tumor microenvironment calibration, and anthracycline-specific XNA aptamer design. Full article

The accurate discrimination of structurally analogous neurotransmitters remains a formidable challenge due to their high structural similarity and overlapping chemical properties. To address the limitations of low specificity in single-probe sensors and the fabrication complexity of multi-component arrays, we developed a simplified fluorescence sensing array based on a single pyrene-functionalized MOF, In-TBAPy. This strategy leverages the distinctive monomer-to-excimer luminescence transition of In-TBAPy, triggered by the tunable π-π stacking of pyrene units within the crystalline framework. The results demonstrate that the array, integrated with Linear Discriminant Analysis (LDA) across four optimized emission channels, achieves a classification accuracy of 93.75% in identifying four highly similar neurotransmitters: serotonin (5-HT), dopamine (DA), adrenaline (A), and norepinephrine (NA). Notably, the sensing platform exhibits exceptional robustness in simulated physiological environments and complex multi-analyte mixtures, enabling reliable quantitative analysis: 0–100 μM for 5-HT and adrenaline (A), 0–40 μM for dopamine (DA), and 0–80 μM for norepinephrine. Mechanistic studies suggest that the differential quenching of monomer and excimer peaks stems from the synergistic effect of competitive absorption and host–guest interactions. This work effectively overcomes the cross-interference issues of traditional sensors and validates a high-efficiency solution for high-throughput neurotransmitter analysis using a single-material-based array strategy, significantly reducing operational costs and preparation time. Full article

Ensuring the reliability and operational efficiency of critical scientific equipment is a central challenge in high-stakes research environments such as nuclear physics laboratories and particle accelerator facilities. Unexpected failures entail significant financial cost and prolonged interruptions to experimental programmes. We present a predictive maintenance (PdM) framework built around a two-layer Vanilla Long Short-Term Memory (LSTM) network trained on multivariate sensor streams collected at NRF-iThemba LABS between January 2021 and December 2023. Four channels, namely supply voltage, vibration velocity, differential pressure, and rotational speed, were recorded at 5 $\min$ intervals using a suite of industrial-grade transducers (power quality analyser, IEPE accelerometers, differential pressure transmitters, and proximity encoders) feeding a multi-channel data-acquisition chassis via OPC-UA, yielding a time-synchronised dataset of 315,360 observations. A normalised failure score converts the binary classifier output into a continuous, interpretable health indicator that supports tiered scheduling of maintenance. The Vanilla LSTM achieved a test-set F1-score of $75\%$ and an area under the receiver-operating-characteristic curve (AUC) of $0.856$, outperforming five competing architectures (PCA/$T^2$, Random Forest, Deep Neural Network, LSTM Autoencoder, and Bidirectional LSTM Autoencoder), and delivered a mean failure lead time of $(42.3\pm 7.2)$$\mathrm{h}$, exceeding the 36 $\mathrm{h}$ engineering requirement for proactive maintenance scheduling. Full article

In this work, an electrochemical sensor based on a Fe₃O₄/STAB/chitosan nanocomposite was developed for the detection of nitrite. The nanocomposite exhibited positive charges on its surface, high conductivity, favorable biocompatibility, and strong antifouling ability, enabling electrostatic adsorption of nitrite and thereby enhancing detection sensitivity. A glassy carbon electrode (GCE) modified with this material was employed for nitrite measurement using differential pulse voltammetry (DPV). Under optimized conditions, the Fe₃O₄/STAB/chitosan/GCE showed excellent analytical performance for nitrite detection, with a detection limit of 0.151 mg/L. Recovery rates of nitrite in real samples ranged from 95.02% to 107.93%, with relative standard deviations between 0.91% and 5.23%. Moreover, the modified GCE displayed excellent selectivity, reproducibility, and repeatability for nitrite detection. Therefore, this work provides a novel strategy for nitrite monitoring in aquaculture. Full article

Grass carp (Ctenopharyngodon idella) is an important freshwater aquaculture species in China. However, high-density farming often leads to significant growth differentiation among individuals, seriously affecting yield and product quality. Here, we conducted an integrated transcriptomic and data-independent acquisition (DIA) proteomic analysis across the brain, liver, and muscle tissues of fast-growing (FG) and slow-growing (SG) grass carp after 9 months of high-density culture. Our analysis revealed that the core mechanism driving growth differentiation is a deep decoupling of transcription and translation, where enhanced muscle translational efficiency dictates the fast-growth phenotype, while slow growth is constrained by central stress and hepatic energy depletion. In the slow growth group, the brain exhibited translational arrest and neuroinflammation, and the liver entered a state of hypermetabolism mediated by AMPK, evidenced by the post-transcriptional up-regulation of key sensors such as CAB39 (PRM ratio = 1.634) and CAMKK2 (PRM ratio = 1.295). Conversely, in the fast-growing group, the mTOR–ribosome signaling axis was strongly activated at the post-transcriptional level in the muscle (GSEA NES = −1.852), triggering myofibrillar protein deposition despite transcriptional silence in classical growth pathways. These findings elucidate the systemic molecular mechanisms of growth divergence and provide high-confidence molecular targets for developing fast-growing, stress-resilient grass carp strains for precision aquaculture breeding. Full article

1,2,3-Triazole units with their structural and photophysical properties are well-suited for the development of chemosensors for ion sensing. Synthetic approaches make it extremely easy to modify this core with just a few steps to control ion selectivity and response-signal output. The current study examines how 8-(4-phenyl-1H-1,2,3-triazol-1-yl)quinoline, a quinoline–triazole–phenyl (QTP) construct, responds differentially to Cu²⁺ and Al³⁺ ions. QTP provides distinct fluorescent signals in acetonitrile in the presence of Cu²⁺ versus Al³⁺, a turn-off response with Cu²⁺ and blue-to-green output with Al³⁺. Spectroscopic studies quantify the selectivity of the sensor for these species with respect to other ions and reveal a stoichiometric ratio of 1:1 for sensor:Cu²⁺ and 2:1 for sensor:Al³⁺. NMR titration studies suggest that Cu²⁺ is detected via coordination of the quinolinyl and triazolyl nitrogens, while Al³⁺ is detected through coordination of the quinoline nitrogen. Overall, QTP displays a selectivity for Al³⁺ relative to Cu²⁺ over other cations in this investigation. Full article

Exosomes and small extracellular vesicles are promising liquid-biopsy biomarkers because they carry molecular information from their cells of origin and can be accessed from minimally invasive biofluids. Reliable separation and detection are made more difficult by their small size, low abundance, diverse composition, and co-occurrence with lipoproteins, protein aggregates, and other extracellular particles. To improve exosome enrichment, capture, and sensing, nanomaterial-assisted techniques have become crucial. Using a mechanism-based approach that differentiates between non-gravimetric signal amplification and genuine physical mass loading, this study offers an organized comparison of nanomaterial-enabled exosome sensing techniques. This distinction is helpful because different transducers measure different physical quantities: while optical, electrochemical, fluorescent, catalytic, and nucleic acid-based platforms typically benefit from enhanced signal generation rather than increased mass, resonant and gravimetric sensors benefit from increased inertial or surface-bound mass. In terms of amplification mechanism, transducer compatibility, sample-matrix tolerance, workflow complexity, and translational maturity, the review contrasts metallic nanoparticles, magnetic systems, metal–organic frameworks, carbon and two-dimensional materials, quantum dots, upconversion nanomaterials, DNA nanostructures, and polymer-based platforms. The gap between analytical sensitivity and clinical utility, including separation purity, recovery, biological heterogeneity, pre-analytical variability, interference from complex biofluids, and the need for uniform validation, is given special focus. The review concludes that no single nanomaterial or amplification method is universally optimal; instead, platform-aware, application-specific integration of isolation, amplification, and validation techniques is necessary for clinically meaningful exosome sensing. Full article

DNA methylation plays a fundamental role in maintaining intestinal homeostasis, immune tolerance, and inflammatory balance. Active DNA demethylation, mediated by the ten-eleven translocation family of dioxygenases (TET1, TET2, and TET3), has emerged as an important epigenetic mechanism linking environmental and metabolic cues to gene regulatory programs in the gut. In the intestinal epithelium, TET-dependent DNA hydroxymethylation contributes to intestinal stem cell maintenance, epithelial differentiation, regeneration, and barrier integrity. Perturbations in TET activity are associated with epithelial dysfunction, chronic inflammation, and increased susceptibility to colorectal tumorigenesis. Within the immune compartment, TET-mediated demethylation is required for the epigenetic stabilization of gut-associated immune cells. Altered TET function has been implicated in immune imbalance in inflammatory bowel disease, Hirschsprung’s disease, and colitis-associated colorectal cancer. Emerging evidence further indicates that intestinal microbiota-derived metabolites, including short-chain fatty acids and aryl hydrocarbon receptor ligands, modulate TET activity, positioning TET enzymes as epigenetic sensors of microbial and metabolic signals. In turn, TET-dependent programs shape immune responses to commensal microbes and pathogens, establishing a bidirectional microbiota–epigenetic axis that influences both intestinal and systemic immunity. In this review, we summarize and critically evaluate current evidence on the roles of TET enzymes in intestinal epithelial biology, immune cell regulation, and host–microbiota interactions in colorectal inflammation and disease. Full article

The detection of RASSF1A (Ras-associated domain family 1 isoform A) promoter methylation in body fluids can offer a powerful tool for the early diagnosis of bladder cancer. Zinc finger proteins (ZFPs) serve as sequence-specific recognition elements for targeting double-stranded DNA sequences. Here, we report a cyclic olefin copolymer (COC) chip-integrated ZFP array-based molecular sensor that bypasses the need for bisulfite conversion and PCR amplification to recognize the specific site of DNA methylation in the RASSF1A promoter. Building upon the SEER-LAC (SEquence-Enabled Reassembly of β-Lactamase) framework, we engineered a dual-recognition split-enzyme system in which a COC chip-immobilized ZFP array confers sequence specificity while a co-recruited methyl-binding domain (MBD) enforces methylation-dependent gating, together driving the proximity-induced reconstitution of functional β-lactamase at methylated target loci. Accordingly, this sensor specifically reassembles and restores enzymatic activity only in the presence of specific methylated DNA in the RASSF1A promoter region. We demonstrate that this dual-component array effectively differentiates methylation status with high specificity. Given its rapid turnaround and non-PCR-based mechanism, this system can be well-suited for developing diagnostic assays for bladder cancer, offering a potential alternative to conventional epigenetic screening methods. Full article

Long-term wildfire susceptibility mapping represents a significant component of disaster prevention and the protection of human communities, public health, and local ecosystems. In this study, a wildfire inventory was developed through multi-sensor fusion of satellite data (MODIS and VIIRS), comprising 153,305 fire events across China for the period 2001–2024. In addition to historical incidents, 14 predictive variables were processed, representing geomorphological, climatological, hydrological, vegetative, and anthropogenic conditions. This study evaluates long-term spatial wildfire susceptibility based on long-term mean environmental and climatic conditions. Methodologically, the research applies six models from machine learning (ML), deep learning (DL), and transformer-based approaches: Random Forest (RF), Extreme Gradient Boosting (XGBoost), Deep Neural Network (DNN), Fourier Multi-Layer Perceptron (F-MLP), Kolmogorov–Arnold Network (KAN), and Feature Tokenizer (FT) Transformer. The results were integrated into an ensemble susceptibility map with a spatial resolution of 500 m using Geographic Information Systems (GIS), indicating that 7.4% of China’s territory is classified as having a very high wildfire susceptibility. In addition to the national-scale assessment, a local differentiation was conducted across 34 province-level divisions, revealing that Fujian Province (86.8%) and the Guangxi Zhuang Autonomous Region (82.9%) had the largest shares of areas classified as high and very high wildfire susceptibility. Performance evaluation under spatial block-based validation demonstrated that the Random Forest model achieved the highest predictive power, with an area under the curve (AUC) of 87.8%, followed by XGBoost (87.3%) and Fourier MLP (86.6%). Based on the combined SHAP (Shapley additive explanations) analysis of all applied models, soil moisture, elevation, and terrain slope were identified as the most influential factors affecting wildfire occurrence in China. Overall, the findings contribute to more effective wildfire prevention and risk management strategies at both the local and national levels. Full article