Long pepper is a prominent medicinal plant, extensively utilized as a bioactive constituent in traditional Asian medicine, including in Ayurvedic, Traditional Chinese, and Indonesian herbal medicines. However, the adulteration of long pepper in powdered form not only causes economic losses but also poses a health risk to consumers. This study developed and compared the integration of voltammetric fingerprinting with partial least squares regression (PLSR), Lasso Regression, support vector regression (SVR), random forest (RF), and k-nearest neighbors (kNN) for the quantitative analysis of long pepper, particularly Java long pepper (Piper retrofractum) adulteration. Cyclic voltammetry was performed at a MWCNT-COOH/Chitosan nanofiber-modified glassy carbon electrode to obtain representative voltammetric fingerprints. Principal component analysis (PCA) of CV data was employed for data exploration. Based on the PCA results, the selected optimum dataset comprises the current responses within the potential range of 0 to +1 V and back to 0 V, which contributed most significantly to discrimination between Java long pepper and adulterant. The optimized voltammetric data were subsequently processed using chemometric and machine learning approaches for quantitative analysis. Among all evaluated methods, PLSR consistently achieved the highest predictive performance across all potential regions. This result highlights the suitability of latent-variable chemometric approaches for high-dimensional voltammetric data, which are characterized by strong multicollinearity among neighbouring potentials. Specifically, the PLSR model constructed using the optimized CV dataset outperformed other methods (R 2 = 0.9926, RMSEC = 0.0266, and RMSECV = 0.0293). These findings demonstrate that combining CV with chemometrics and machine learning provides a rapid, effective method for portable in situ monitoring of spice adulteration.
Artemisia argyi Lévl. et Vant. and Artemisia mongolica (Fisch. ex Besser) Nakai are two distinct species of the genus Artemisia (Asteraceae). In some regions of China, Artemisiae Mongolicae Folium is frequently used as a substitute for Artemisiae Argyi Folium. These two herbal materials exhibit marked differences in chemical composition, which result in distinct therapeutic effects. Herein, gas chromatography (GC) fingerprinting and chemometrics were employed to compare their volatile constituents and establish an effective discrimination method. A standard GC fingerprinting procedure for Artemisiae Argyi Folium was established, yielding 21 common characteristic peaks. The similarity values of 20 batches of Artemisiae Argyi Folium were ≥ 0.894, whereas those of 12 batches of Artemisiae Mongolicae Folium were ≤ 0.812. Common peaks were further identified by GC-MS. Cluster analysis (CA), principal component analysis (PCA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were applied to identify differential components. Multivariate analyses clearly separated the two species. Five volatile compounds with VIP > 1.2 were selected as potential chemical markers to distinguish Artemisiae Argyi Folium from its adulterant Artemisiae Mongolicae Folium.
Subtilisin-like serine proteases (family S8) secreted by the nematophagous fungus Purpureocillium lilacinum are widely involved in virulence-related processes during nematode infection and are therefore potential targets for biocontrol enhancement. Here, nine extracellular S8A.139 proteases from P. lilacinum PLFJ-1 were analysed using a unified computational workflow that includes AlphaFold2 structure prediction, docking-based pose initiation, explicit-solvent molecular dynamics (MD) refinement, MM/PBSA ranking and per-residue decomposition, hydrogen-bond persistence analysis, residue interaction network mapping, and in silico mutational scanning with targeted MD validation. A short peptide (VAQGGAAGLA) was used as a standardised probe to compare peptide association across paralogs rather than as a confirmed native substrate. Using a consistent MM/PBSA protocol applied to the equilibrated 300-400 ns window, PL-S8P6 exhibited the most favourable peptide association estimate (ΔG_bind = -52.51 kcal/mol), while PL-S8P7 exhibited the least favourable (ΔG_bind = -17.80 kcal/mol). MM/PBSA estimates were derived from 1000 evenly spaced frames (stride 100 ps) and reported with block-averaged uncertainty to support the robustness of the comparative ranking. Residue-level analyses revealed distinct energetic and contact fingerprints across paralogs; notably, the D658Y substitution in PL-S8P7 increased interfacial hydrogen-bond persistence and improved the binding estimate within the protocol (ΔG_bind = -25.19 kcal/mol) without causing large-scale instability in MD. Collectively, these results offer paralog-resolved structural and energetic hypotheses that prioritise candidate residues for biochemical validation and structure-guided optimisation of P. lilacinum S8 proteases in nematode-related contexts.
Ubiquitous environmental pollution exerts severe adverse impacts on plant growth, development, and yield, jeopardizing global food security as well as human life and health. Traditional plant health monitoring relies on phenotypic observation and excised tissue testing, suffering from sample destructiveness and time lag in results. Whereas emerging plant wearable sensors mainly provide output signals that are indirect, comprehensive responses to plant physiological processes, failing to specifically capture early endogenous stress signals. Herein, a hydrogel-integrated multiplexed electrochemical sensor (HIMES) with high sensing performance, good flexibility (Young's modulus = 18.27 MPa), and excellent biocompatibility was developed, which enables 24-day in situ monitoring of four core stress-responsive signaling molecules (H2O2, salicylic acid, abscisic acid, indole-3-acetic acid) in tomato stems under exposure to environmentally relevant concentrations of four pollutants (Cd2+, PFOS, atrazine, sulfadiazine). The HIMES achieved ultraearly pollution stress warning within 1-2 days of pollutant exposure, >10 days ahead of visible phenotypic damage, and established a pollutant-specific signal fingerprint library based on the temporal response characteristics of the signaling molecules for rapid pollutant typing. This study provides a powerful platform for the early diagnosis of environmental pollution, pollution source identification, and dynamic assessment of ecological risks for plants.
The growth of unmanned aerial vehicles (UAVs) has generated an increasing need to have an intelligent and efficient radio frequency (RF) fingerprinting system to detect, identify, and classify the mode of operation of the UAV. The paper introduces a computationally efficient transformer-based attention architecture for RF signals analysis in real-world scenario correctly. The architecture possesses a two-stage architecture feature extraction pipeline consisting of the following steps: the correlation filtering feature selection, the ANOVA feature selection, and a multi-headed self-attention encoder to obtain spectral sequences. The evaluation of the model on the large DroneRF dataset indicates that it possesses a high-ranking structure in three hierarchical problems, which are binary UAV detection (DD), four-class UAV classification (DC), and ten-class flight mode classification (FMC). Cross-validation accuracy was 100.00, 99.80, and 99.23; the number of parameters was 39,270 to 152,194, and FLOPs was 2.45 M to 9.81 M, which is appropriate in edge devices with dimensions and block depth customized to task demands. For real-world applicability, the framework was evaluated on the challenging VTI_DroneSET_FFT dataset across 2.4 GHz and 5.8 GHz, in both single- and multi-drone scenarios. A weak baseline (68.09% accuracy) was used to start performance with ablation-guided design, which gave 88.10% accuracy in the 5.8 GHz multi-drone task that was the most challenging. The model scored high in all settings: 98.84%, 96.03%, 89.58%, and 88.09%. The analysis of attention showed that drone detection was effective in various RFs, which were clear to be identified and characterized.
Metal-halide perovskite solar cells combine high power-conversion efficiencies with solution processability, yet scalable fabrication remains limited by incomplete control over crystallization pathways and the resulting film heterogeneity. Under flash infrared annealing (FIRA), millisecond photonic pulses drive strongly non-equilibrium nucleation and growth, producing spherulitic microstructures whose final geometry stores measurable comparative signatures of the underlying crystallization pathway. Here, we establish a segmentation-based framework that converts bright-field microscopy of FIRA-processed films into quantitative comparative descriptors of grain morphology, video-anchored effective kinetics, and spatial microstructural fingerprints, providing a practical route to analyze crystallization under manufacturing-relevant rapid-processing conditions. Using time-resolved crystallization videos of pristine FAPI and FAPI-TEMPO together with a larger static microscopy dataset of roughly 3000 images from about 100 processed films, we quantify how additive chemistry reorganizes crystallization across both dynamic and end-state image populations. The workflow combines semi-supervised instance segmentation and mask-quality classification with grain-level morphology extraction and video-anchored kinetic reconstruction, with the video data providing the kinetic anchor and the static dataset providing the principal statistical support. From a curated library of more than 420 000 validated spherulites (180 545 for FAPI and 241 619 for FAPI-TEMPO), we derive effective growth-rate distributions, transformed-fraction curves, empirical kinetic descriptors, and spatial signatures based on texture entropy, defect loading, shape regularity, radial profiles, and crowding metrics. We find that TEMPO delays and narrows the dominant crystallization burst, reduces grain-size dispersion (median area reduced by 34%, Δ = 357 µm2, Cliff's δ = 0.62), reduces optically defect-like outer-front heterogeneity, and contracts the accessible kinetic landscape while preserving the overall spherulitic growth motif. Sample-level nonparametric statistics further show that area, perimeter, equivalent radius, and the effective growth rate are all larger in pristine FAPI, whereas the whole-grain texture entropy (hm) is comparable between the two compositions, indicating that the additive redistributes intragrain disorder spatially rather than changing its total amount. These results are consistent with additive-mediated narrowing of the accessible crystallization pathway under rapid annealing. More broadly, the workflow shows that bright-field imaging can serve as a scalable probe of non-equilibrium crystallization in solution-processed semiconductors and provides a transferable route for linking processing, crystallization dynamics, and final microstructure in rapidly solidified thin films.
Increasing numbers of domestic and wild animals in residential environments have contributed to a rise in animal-related incidents. As a result, the forensic identification of animal species from trace blood evidence has become increasingly important. In our previously reported simplified protocol for hemoglobin peptide mass fingerprinting (PMF), applicability was limited to mammals and required comparatively large blood volumes. To address these constraints, we implemented an in-gel digestion PMF workflow and sought to determine the optimal protein content for analysis. Pooled blood from 10 animals was subjected to SDS-PAGE, followed by Mascot-based protein identification. Subsequently, to evaluate species applicability and the suitability of the method for bloodstain analysis, in-gel digestion PMF was performed on blood samples from 21 species and bloodstains from 10 species at the optimized protein load. In nearly all specimens, hemoglobin derived from the target or phylogenetically related species yielded the highest protein scores. Overall, in-gel digestion PMF enables species estimation across a broad range of forensic animal samples while requiring a small blood volume. Given that PMF can be conducted using a relatively low-cost single mass spectrometer, this approach has the potential to advance the adoption of forensic proteomics.
In materials science, the choice of structural descriptors for machine learning protocols strongly influences both predictive performance and model interpretability. High-dimensional descriptors can improve numerical accuracy, but often introduce substantial computational overhead and reduce transparency. To address this, the Dynamic Collision Fingerprint (DCF) framework generates concise descriptors via the dynamical probing of atomic structures. In this work, we benchmark DCF against the widely used Matminer library using a data set of 120 two-dimensional (2D) carbon allotropes. We evaluate performance across three regression algorithms, linear regression, decision trees, and XGBoost, utilizing train-test partitions from 10% to 90%. Our results demonstrate that DCF matches the predictive accuracy of Matminer across all algorithms. Although Matminer can be faster in terms of execution time, DCF accomplishes its predictive performance using descriptors that are significantly lower-dimensional, pointing to manageable computing costs in feature space. Moreover, compared to the rather technical Matminer descriptions, DCF exhibits considerably clearer physical interpretability. These findings suggest that DCF serves as a viable alternative to high-dimensional descriptor libraries for structural representation, since it remains both computationally flexible and physically grounded.
To develop SAFE, a self-calibrated framework to estimate B1 + and B0 field inhomogeneities directly from conventional magnetic resonance fingerprinting (MRF) acquisitions and improve its quantification accuracy. SAFE utilized a two-step approach. First, two physics-informed image markers are extracted from the MRF data to create a magnitude and a phase image that are highly correlated with B1 + and B0 inhomogeneities, respectively. Second, a deep learning (DL) network is applied to map these markers to quantitative field maps. SAFE was tested on 3D Spiral Projection Imaging MRF brain acquisition at 3T, where the network was trained and validated across a multi-site, multi-vendor dataset (N = 358) from healthy volunteers and clinical population. The capability of SAFE to be applied to unseen MRF sequences with different signal preparations and flip-angle trains through adaptively retraining without the need for additional training data was also tested. SAFE achieved normalized-root-mean-square-error within 3% against gold-standard field-calibration scans on both B1 + and B0 maps (N = 32), with high performance remaining on datasets from scanners that the training data were acquired from. T1 and T2 biases were shown to be effectively corrected on healthy volunteers, a patient with brain tumor, and a pediatric subject. Tissue quantification accuracy after SAFE-estimated field correction was validated on a large patient cohort (N = 86). SAFE was also demonstrated to be adaptable to different MRF sequences without acquiring additional training data. By combining physics-informed image markers with DL, the proposed SAFE framework enables calibration-free estimation of B1 + and B0 field inhomogeneities at 3T for whole brain MRF.
This study investigates the relationship between quorum sensing genes (lasR), biofilm-related genes (algD and pslD), and the virulence gene (toxA) in Pseudomonas aeruginosa, a major opportunistic pathogen known for its increasing antibiotic resistance. Our study was conducted on 100 P. aeruginosa strains isolated from 4 selected hospitals in Tehran, Iran. The antimicrobial test was determined using the disk diffusion method. Biofilm formation was tested on all isolates using the phenotypic method. The presence of lasR, algD, pslD and toxA genes was detected using PCR and confirmed through sequencing. The expression levels of the genes were measured using the real-time PCR. Isolates were typed by Rep-PCR at an 80% similarity level. Antimicrobial susceptibility testing revealed that 34% of isolates were classified as multidrug-resistant (MDR). Biofilm formation was observed in 78% of isolates. PCR analysis showed a high prevalence of lasR (80%), algD (88%), and toxA (83%), while pslD was detected in 42% of isolates. The co-occurrence of all genes was noted in 23 isolates, 22 of which were biofilm producers. Real-time PCR confirmed elevated expression of these genes in biofilm-producing isolates. Finally, Rep-PCR fingerprinting analysis revealed preliminary clonal relatedness patterns among 22 selected isolates, which were categorized into 4 common types (CT) and 14 single types (ST). These findings highlight the relationship between Las quorum‑sensing system (specifically lasR), biofilm formation, and exotoxin A production in P. aeruginosa, underscoring the need for targeted strategies to combat MDR mediated bacterial infections.
This study presents an integrated framework combining multi-elemental profiling and 87Sr/86Sr isotopic fingerprinting with machine learning and explainable artificial intelligence (XAI) for the geographical authentication of Italian wheat. A total of 122 samples collected from Northern, Central and Southern Italy over two harvest years (2023-2024) were analysed by ICP-MS and MC-ICP-MS. Elemental composition exhibited pronounced interannual variability, whereas the 87Sr/86Sr ratio showed greater temporal stability and a consistent link to geological background. Random Forest models achieved three-class classification accuracies of 0.75 ± 0.08 for 2023 data and 0.81 ± 0.06 for 2024. SHAP analysis identified the isotopic ratio, together with Zn, Ni, Mn and Cu, as the main contributors to classification. Results demonstrate that wheat geographical origin is reliably characterised by an integrated elemental-isotopic signature interpreted through explainable machine learning, supporting provenance assessment across the major Italian macro-areas over different harvest years.
The persistent migration of pesticides into aquatic ecosystems poses severe risks to environmental and public health. Monitoring pesticide residues along these migration pathways is therefore critical. Herein, we report the first application of a three-dimensional (3D) photonic microsphere surface-enhanced Raman spectroscopy (SERS) substrate (ZIF-8 PCMs-NH2@AuNPs) for the targeted adsorption and trace detection of the pesticide thiram. Synthesized via microfluidics, these hierarchically porous metal-organic framework microspheres overcome the poor reproducibility of conventional substrates by yielding highly uniform, stable "Raman hotspots" through surface-modified AuNPs. This structurally reproducible substrate achieved an enhancement factor of 8.4 × 107. For thiram analysis, it demonstrated a broad linear range (0.05-10 μg/mL) and a low limit of detection (0.0137 μg/mL). Validated in real water samples, this platform provides a robust analytical tool for monitoring pesticide residues of the migration pathways (soil water-river water-tap water).
Enterovirus A71 (EV-A71) causes hand, foot, and mouth disease and can trigger life-threatening neurological complications, yet the sequence-level physicochemical correlates of CNS involvement across globally circulating lineages remain incompletely defined. Here we screened 15,247 EV-A71 genomic entries spanning 1998-2024, retaining 267 full-length sequences (≥7,000 bp) with confirmed clinical outcomes (7 central nervous system [CNS]-involved, 260 non-CNS). This extreme 7:260 class imbalance, reflecting the scarcity of publicly available full-length CNS-associated EV-A71 genomes, is the principal limitation and interpretive premise of the study. Each polyprotein position was encoded by three Z-scale descriptors-hydrophobicity (Z1), molecular volume (Z2), and electrostatic polarity (Z3)-converting discrete residue identities into a continuous biophysical feature space. A two-stage statistical pipeline (Mann-Whitney U screening followed by odds-ratio ranking) distilled 20 significant loci down to five core positions: P2124_Z1, P997_Z2, P1246_Z3, P1743_Z2, and P1711_Z1 (all P<0.001). Leave-one-out cross-validated logistic regression achieved the highest area under the receiver operating characteristic curve (AUC = 0.889) among eight algorithms benchmarked. Because this AUC is estimated from only seven positive samples, it should be regarded as an exploratory internal performance signal rather than definitive evidence of generalisable accuracy. SHapley Additive exPlanations (SHAP) assigned the largest model contribution to P2124_Z1 (OR = 4.28; 95% CI 1.47-12.51), while P1246_Z3 was statistically associated with lower CNS odds (OR = 0.50); these model-derived quantities do not establish causal mechanisms. Reference-strain mapping linked the five polyprotein coordinates to mature-protein residues in 3D RdRp, 3C protease, 2C helicase, and 2A, thereby providing structural context for cautious biochemical hypotheses rather than confirmed mechanisms. Phylogenetic dispersion of CNS-associated strains was compatible with convergent evolution, but this inference remains limited by the seven available CNS genomes. We therefore present the five-position physicochemical signature and nomogram as hypothesis-generating tools for prioritising candidate neurovirulence markers, requiring prospective validation in larger and more balanced independent cohorts before clinical or field deployment.
Drug-drug interactions (DDIs) are a critical safety issue in clinical practice, as they can lead to severe and often unpredictable adverse effects. This risk becomes significantly higher in multi-drug therapies, which are increasingly used in the treatment of complex and chronic diseases such as cancer, cardiovascular disorders, and diabetes. However, identifying DDIs through in vivo studies is costly and time-consuming. In this study, a novel DDI prediction model, Mol2Image, has been proposed that utilizes chemical structure features derived from Simplified Molecular Input Line Entry System (SMILES) representations, including molecular property descriptors and structural fingerprints. The proposed model combines chemical structure information with automated feature learning. Molecular descriptors and structural fingerprints extracted from SMILES representations are converted into visual patterns that capture key chemical characteristics of each drug. These images are then processed by a Convolutional Neural Network (CNN) to learn high-level structural features associated with drug-drug interactions. The model is trained and evaluated using two benchmark DDI datasets: the Drugbank dataset, which consists of 443,046 interactions, and ChCh-Miner, which consists of 48,514 DDIs. Experimental results demonstrate that the proposed model (Mol2Image) achieves competitive performance compared with several state-of-the-art methods. Experimental results demonstrate that the proposed model consistently outperforms existing approaches, achieving accuracies of 0.9608 and 0.9683 using the Drugbank dataset and ChCh-Miner dataset, respectively. Ultimately, Mol2Image provides a highly scalable, strictly structure-centric framework that ensures superior predictive accuracy with minimal computational overhead, operating entirely independently of clinical data.
Cybister chinensis Motschulsky is a traditional Chinese medicinal insect traditionally used to tonify the kidney, and clinically applied for the management of chronic kidney disease (CKD). The present work was dedicated to identifying bioactive ingredients in the ethyl acetate fraction of C. chinensis and elucidating their mechanisms underlying the treatment of CKD. Compounds were isolated and used to establish fingerprints. Anti-inflammatory and anti-injury activities in vitro and grey relational analysis were applied to establish spectrum-effect relationships. Network pharmacology combined with molecular docking was employed to identify the candidate bioactive components. The effects of ethyl acetate fraction of C. chinensis were explored in doxorubicin-induced CKD rat model via Western blot, RT-PCR, transcriptomics, and metabolomics. Molecular dynamics simulation was applied to assess binding affinity of bioactive components. Cell experiments verified effects of components on inflammation, oxidative stress, and fibrosis. The ethyl acetate fraction of C. chinensis was confirmed as the active fraction. Compounds 6, 10, 22, 23 and 27 were screened as the potential active components. Ethyl acetate fraction improved the condition of CKD rats, while multi-omics identified a triple-functional network against inflammation, oxidative stress, and fibrosis, with PI3K-AKT/MAPK/HIF-1 pathways verified. Cell experiments and molecular dynamics simulation identified compound 6 as the key active component, with its regulatory effects validated through PI3K-AKT/MAPK/HIF-1 pathways. Ethyl acetate fraction of C. chinensis and its active component exerted renoprotective effects by suppressing inflammation, oxidative stress, and fibrosis in CKD via regulating PI3K-Akt, MAPK, and HIF-1 pathways.
Structural brain abnormalities in psychosis are well-replicated but heterogenous posing a barrier to uncovering the pathophysiology, etiology, and treatment of psychosis. To parse neurostructural heterogeneity and assess for the presence of anatomically-derived subtypes, we applied a data-driven method, similarity network fusion (SNF), to structural neuroimaging data in a broad cohort of individuals with psychosis (schizophrenia spectrum disorders (SSD) n = 280; bipolar disorder with psychotic features (BD) n = 101). SNF identified two transdiagnostic subtypes in psychosis (subtype 1: n = 158 SSD, n = 75 BD; subtype 2: n = 122 SSD, n = 26 BD) that exhibited divergent patterns of abnormal cortical surface area and subcortical volumes. Compared to controls (n = 243), subtype 1 showed moderate enlargement of surface area in frontal and parietal areas and larger dorsal striatal volumes, whereas subtype 2 demonstrated markedly smaller surface areas in frontal and temporal areas and subcortical volumes, including hippocampus, amygdala, thalamus and ventral striatum. When comparing subtypes on clinical characteristics, subtype 2 demonstrated more severe negative symptoms, greater neuropsychological impairment, and lower estimated premorbid intellectual functioning. Integrating cell-type data imputed from gene expression in the Allen Human Brain Atlas revealed an association between interregional glial cell and parvalbumin-expressing interneuron abundance and surface area expansion in subtype 1, yet cortical thinning in subtype 2. The largest deviations from controls were present in subtype 2 surface area that spatially coupled with layer 5 glutamatergic neuron abundance, critical for corticostriatal connectivity. These outcomes indicate early diverging neurodevelopmental trajectories of psychosis subtypes, as evidenced by opposing patterns in surface area, cellular fingerprints, and subcortical volumes.
Liquid biopsy has become a revolutionary method for the early detection of cancer as a non-invasive technology that can assess circulating tumor material in biofluids. Liquid biopsy allows dynamic monitoring of tumor evolution, genetic changes and treatment responses, which is different from traditional tissue biopsy which offers a static and potentially narrow view of tumor biology. This mini-review will summarize the rapidly evolving future of circulating biomarkers (circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), microRNAs, proteins, exosomes and epigenetic fingerprints), with their potential for early multi-cancer biomarkers, and their integration into early detection. The analytical sensitivity of liquid biopsy has expanded dramatically through technologies such as next-generation sequencing (NGS), digital PCR, and advanced proteomics. In addition, data collection has been enhanced through machine learning for increased predictive performance and the identification of new biomarkers. This review discusses clinical performance between liquid and tissue biopsy and the value of combined biomarker methods to improve accuracy for the detection of early disease. Finally, future directions will be presented to identify new methods of integration, improved costs and the establishment of early detection programs at the population scale.
A major challenge in energetic materials research is the development of compounds that simultaneously exhibit high density, high heats of detonation, excellent detonation performance, and acceptable safety characteristics through efficient incorporation of energetic functionalities into compact frameworks. In this regard, reaction conditions play a critical role in governing the self-assembly of energetic ligands with metal ions and thereby influencing the architecture and properties. Herein, two nitrate-containing energetic coordination compounds, [Ni3(TATa)6(H2O)6]·6NO3 (Ni-ECC) and [Cu(TATa)2(NO3)3]·NO3 (Cu-ECC), were synthesized and characterized by infrared spectroscopy, TGA-DSC, elemental analysis, PXRD, and single-crystal X-ray diffraction. Evaluation of their properties revealed detonation velocities of 7222 and 8634 m s-1, respectively. Notably, Cu-ECC possesses a solvent-free three-dimensional dense supramolecular framework with a crystal density of 2.04 g cm-3 and reduced impact and friction sensitivities of 20 J and 360 N. Its detonation performance (VOD = 8634 m s-1; DP = 36.4 GPa) approaches that of RDX, placing it among the promising ECCs reported to date. Hirshfeld surface and fingerprint analyses indicate that noncovalent interactions stabilize nitrate ions and reinforce structural integrity. Furthermore, Cu-ECC produced a 3.4 cm dent in a 5 mm lead plate in a detonation test, demonstrating potential as a secondary explosive.
Salinity is a major abiotic stress that negatively affects nearly all plant species at all stages of growth. Drought and poor-quality irrigation cause high soil salinity and salt accumulation via evaporation, reducing crop productivity. Despite its critical importance, the spatial localization of salt ions and associated biochemical changes within plants experiencing high salinity remains largely unknown. In this study, we developed a multimodal imaging pipeline to understand the impact of salinity on the pistachio rootstock UCB-1 (Pistacia atlantica x Pistacia integerrima). We directly link biochemical fingerprints in stem tissue architecture with salt ion localization to provide insights into the strategies pistachio uses to tolerate salinity. We observed that Pistacia spp. exposed to high salt conditions accumulated Ca, Si, Cl, Al and Mg as hotspots within the pith, compared to the control (of which only Ca and Al co-locate). In contrast, there was a decrease in K between the control and salinity treatment. Hotspots of amide I and II were present in the cortex and pith of the salinity treated sample. Additionally, the salinity treatment resulted in an increased abundance of pectin and carbohydrates within the pith compared to the control, and the abundance of esters/carboxylic acid was greater in the salinity treatment. We determined that Cl and K, S and P, and biochemical components polysaccharide and pectin, esters and carboxylic acid, amide I and cellulose are the strongest drivers of salinity-treatment induced variability. In the cortex and phloem/xylem, a negative K-Ca correlation decreases in the salinity treatment. Several hotspots of elements and amide I (proteins) appear under salinity treatment, particularly in the cortex, suggesting an increase in the production of stress-related proteins (in response to high Cl) and/or structural proteins (i.e. Ca). Together, these results indicate that pistachio responds to salinity through ion compartmentalization coupled with a targeted biochemical adjustment, rather than a broadscale tissue-wide response. Overall, these novel, spatially resolved pixel-registered multimodal imaging data provide an enabling platform to understand the mechanisms of salinity tolerance in Pistacia spp and can be broadly applied to studying stress-related phenotype response in various plant tissues.
Sulfur-containing metal salts (SCMs) are widely used as food additives, but their excess residues or chemical interconversion during processing may generate more toxic species, posing significant health risks. However, conventional analytical methods for SCMs suffer from high cost, time-consuming procedures, and reliance on sophisticated instrumentation. Herein, we report a nanozyme-based colorimetric sensor array constructed from a microbial synthesized Prussian blue nanozyme (Bio-PB) with intrinsically enhanced peroxidase-like activity. The array integrates Bio-PB with three chromogenic substrates (TMB, OPD, ABTS) to generate a three-channel cross-reactive sensing platform. Upon exposure to SCMs, competitive consumption of reactive oxygen species (ROS) by SCMs with different sulfur oxidation states produces unique colorimetric fingerprints. Critically, machine learning (linear discriminant analysis, hierarchical cluster analysis, and principal component analysis) is employed to decode the multi-channel response patterns, transforming subtle cross-reactive differences into statistically robust classification. This machine learning-assisted approach achieves 100% discrimination accuracy of five SCMs (Na2S, Na2S2O3, Na2S2O4, Na2S2O5, Na2S2O8) and correctly identifies 98.78% of 82 unknown samples. It further enables semi-quantification over 10-180 μM, distinguishes binary to quinary SCM mixtures, and maintains excellent performance in complex food matrices (milk, red wine, and eggs). Notably, unlike conventional nanozyme relying solely on redox interaction, Bio-PB hybrid leverages specific binding of SCMs to bacterial membrane components via electrostatic/hydrophobic interactions, endowing high selectivity and robust stability in real-world matrices. With its simplicity, rapidity and cost-effectiveness, coupled with machine learning-powered pattern recognition, this bio-PB based sensor array offers a promising platform for on-site SCMs monitoring and food safety screening.