Ag+ modified paper-based SERS combined with SiPLS for quantitative detection of soluble As3+ in aqueous realgar solutions.

Development of a highly selective near-infrared fluorescent probe for sensitive detection of Hg2+ in environmental and biological samples.

High-precision and wide-range feature wavelength quantitative analysis (FWQA) method for fluorescent substances based on IFE-induced CDRS.

Colorimetric sensor array based on a bimetallic PtPd nanozyme for simultaneous discrimination of multiple antioxidants.

Oxidative stress reshapes the chemical landscape of cell membranes, yet how specific lipid peroxidation products influence G protein-coupled receptor (GPCR) activation remains poorly understood. Here, we performed microsecond-scale all-atom molecular dynamics simulations of the Angiotensin II type 1 receptor (AT1R) embedded in 1,2-dilinoleoyl-sn-glycero-3-phosphocholine (DUPC) membranes containing nonperoxidized, monohydroperoxidized (O9, O13), and dihydroperoxidized (O9 × 2, O13 × 2) phospholipids. Quantitative analysis of conformational dynamics reveals that the effects of peroxidation depend on both the depth (C9 versus C13) and degree (mono versus di) of oxidation. In these simulations, dihydroperoxidation at the C13 position (O13 × 2) enriches an active-like conformational ensemble that is more similar to that observed for an Angiotensin II-stabilized active reference than in the other membrane environments. In contrast, monohydroperoxidized membranes trap the receptor in restricted conformational subspaces with slower exchange dynamics. Mechanistically, we identify a "polar anchor" effect in O13 × 2 systems, where hydroperoxide groups form persistent hydrogen bonds with Asn5.43 at the TM5-TM6 interface, thereby linking specific lipid-protein interactions to global conformational shifts. These findings suggest that oxidative lipid modification acts as a distinct chemical signal that can modulate AT1R signaling through coupled local chemical interactions and global membrane remodeling.

pH effects on the separation of oligonucleotides by ion-pair reserved phase liquid chromatography mass spectrometry.

ABSTRACT Civilian burial practices in Britain during the Second World War functioned as both performative and narrative acts. This case study examines both the choices and practices surrounding private and communal burial during the Plymouth Blitz. It uses the Portland Square tragedy, the city’s single deadliest civilian incident, as its focal point. Quantitative analysis of mortuary records reveals patterns in burial type: families with greater economic resources or smaller household losses tended to choose private funerals, while members of larger or working-class families, particularly children, were more likely to be buried communally. Qualitative evidence from newspapers and memoirs illustrates how these burial forms communicated distinct messages. Public, state-funded services projected patriotic unity and collective sacrifice, while private rituals preserved individual identity and allowed personalised expressions of grief. The study highlights how families navigated tensions between state narratives and personal mourning, showing that even amid mass fatalities, private remembrance remained a significant cultural practice. A concluding discussion reflects on memorialising large-scale civilian loss.

Competitive effect-derived surface-enhanced Raman scattering for analysis of glutathione based on a gold nanotriangle-modified nanocapillary.

Understanding how motoneuron activity is finely tuned remains an open question. Leeches are a highly suitable organism for studying motor control due to their well-characterized behaviors and relatively simple nervous system. On solid surfaces, leeches display crawling, a rhythmic motor pattern that can be elicited in the isolated nerve cord or even in single ganglia isolated from it. This study aimed to learn how this motor output is shaped by concurrent premotor signals. Specifically, we analyzed how electrophysiological manipulation of a premotor nonspiking (NS) neuron, which forms a recurrent inhibitory circuit (analogous to that formed by vertebrate Renshaw cells), shapes the leech crawling motor pattern. The study included a quantitative analysis of putative motor units active throughout the fictive crawling cycle that shows that the rhythmic motor output in isolated ganglia mirrors the phase relationships observed in vivo. Taken together, the study reveals that the premotor NS neurons, under the control of the segmental pattern generator, modulated the degree of excitation of motoneurons during crawling in a phase-specific manner.

Purpose.Diffusion-weighted imaging (DWI) has significant value in disease diagnosis and treatment response monitoring, but its inherent low signal-to-noise ratio (SNR) severely affects image quality and quantification accuracy. Existing denoising techniques often blur important tissue boundary information when suppressing noise.Methods.This study proposes a band-limited implicit neural representation (BL-INR) framework for DWI denoising. The method introduces BL positional encoding based on the frequency response characteristics of the sinc function to restrict INR models from learning high-frequency noise while maintaining strong signal representation capabilities. Furthermore, multi-b-value DWI and structural MRI from the same patient are integrated as anatomical priors, exploiting the correlation between true signals and the statistical independence of noise to achieve effective denoising.Main Results.In clinical DWI data evaluation across four anatomical regions (brain, head and neck, abdomen, and pelvis), BL-INR's visualization results were superior to existing methods. Under extremely low SNR conditions (SNR = 1) in simulated noise experiments, BL-INR achieved a peak SNR of 35.44 and structural similarity index of 0.933, significantly outperforming other methods. Phantom denoising results showed that BL-INR achieved an average apparent diffusion coefficient value error of only4.57×10-5 mm2 s-1, the smallest among all methods.Significance.BL-INR provides a novel approach for DWI denoising by limiting the frequency of INR input positional encoding. Its self-supervised learning characteristics require no paired training data and allow convenient clinical application. The method enables the derivation of accurate diffusion parameters, providing a reliable foundation for DWI-based quantitative analysis with significant clinical application value.

Abstract A novel method for 3D image registration of neutron/X-ray imaging for cylindrical lithium batteries is proposed for addressing inconsistencies in image size, grayscale distribution, and spatial alignment caused by geometric and parameter differences in heterogeneous imaging systems. Considering the complex internal structure and multi-material distribution of lithium batteries, a multi-stage registration process is designed, including verticality adjustment, slice height mapping, scale transformation, translation and rotation calibration. The process is enhanced by incorporating image super-resolution reconstruction techniques to improve image clarity and detail representation. Comparative and extended experiments are conducted using cylindrical lithium batteries and micro DC motors to evaluate the performance of the registration method in terms of spatial alignment accuracy, multi-modal information fusion, and detail fidelity, thereby validating the effectiveness of the method. Results indicate that the proposed approach effectively corrects geometric and modal differences between heterogeneous images, achieving precise alignment of metallic and non-metallic regions. Quantitative analysis based on metrics such as Dice Similarity Coefficient (DSC), Normalized Mutual Information (NMI), and Gradient Similarity (GS) demonstrates the significant advantages of the method in registration accuracy, edge preservation, and modal feature alignment. The method exhibits high adaptability and stability in characterizing the complex structures and multi-modal characteristics of cylindrical lithium batteries.

Abstract To investigate the improvement of surface textured on the noise and vibration characteristics of the tensioner wheels in a belt drive system, this study, for the first time, combines acoustic array noise source identification with laser Doppler vibrometry technology. This enables synchronous and precise measurement and correlation analysis of the noise and vibration generated by the textured tensioner wheels during operation. Experimental results indicate that the tension pulley-back of belt interaction is the primary noise source of the system, and its noise level increases significantly with the relative sliding speed: at 3.4 m/s, the sound pressure level reaches 94 dB, which is 44.3% higher than that at 0.34 m/s. Quantitative analysis confirms that surface textured effectively suppresses vibration, reducing the average absolute amplitude of vibration acceleration by 17.1% to 20.5% within the speed range of 0.34–3.4 m/s. Frequency domain analysis further reveals the underlying mechanism: the textured significantly suppresses high-frequency vibration energy in the 2000–5000 Hz range, but has limited impact on low-frequency vibrations near the system's natural frequency. This study demonstrates that the combined measurement method based on acoustic array and laser Doppler vibrometry can effectively diagnose vibration and noise in belt drive systems. Furthermore, the surface textured design of the tensioner wheels provides an effective solution for controlling vibration and noise in belt drive systems.

Thrombosis is a leading cause of cardiovascular morbidity and mortality, driven by platelet-mediated mechanisms common across distinct vascular environments. However, the dynamical behavior of platelets during thrombogenesis remains poorly understood due to the lack of a comprehensive analytical framework. Here we present a physiologically relevant, imaging-integrated thrombosis-on-a-chip platform that enables real-time quantitative analysis of platelet dynamics during thrombogenesis under arterial, venous, and cancer-associated conditions. The system incorporates endothelialized 3D-printed vascular geometries into a closed-loop whole-blood perfusion circuit that replicates native hemodynamic and cellular microenvironments. Multimodal imaging captures the spatiotemporal evolution of thrombus formation and shows how hydrodynamic forces, endothelial dysfunction, and tumor-derived factors drive distinct thrombotic signatures. Notably, platelet-endothelium adhesion and circulating platelet aggregation are identified as mechanistically distinct yet closely linked processes, each uniquely modulated by vascular context. This platform offers a robust framework for dissecting thrombogenesis and advancing antithrombotic and cancer-associated thrombosis research.

Abstract The Demkov-Kunike (DK) model, characterized by a hyperbolic tangent detuning and a hyperbolic secant coupling, serves as a universal framework for describing driven quantum systems. However, existing research has primarily focused on two-level systems, some gaps still persist in extending the model to three-level configurations to achieve coherent population transfer (CPT). This study constructs exact solutions for both two- and three-level DK models via a canonical transformation, providing a basis for the quantitative analysis of population transfer, and achieves effective regulation of CPT via a finite-time adiabatic driving mechanism under specific parameter constraints. Furthermore, we analyze the impact of pulse truncation—a common experimental constraint—as well as environmental dissipation on quantum state evolution. The results show that the proposed scheme achieves rapid, high-fidelity coherent population transfer not only under ideal conditions, but also in the presence of realistic pulse truncation and even when decoherence induced by system‑environment coupling is included. This work achieves CPT in the three-level DK model, offering a promising approach for advancing quantum state control in practical applications.

Excited states of systems composed of linked fragments or stacked molecules are important for understanding their optoelectronic properties. These states, when projected to individual fragments, are either local (LEs) or charge transfer excitons (CTEs). However, the canonical molecular orbitals (CMOs) obtained from a typical calculation tend to delocalize, which makes the subsequent analysis of excited states cumbersome. In this work, we report a simple approach to address this problem by employing localized molecular orbitals (LMOs) as linear combinations of the CMOs in the occupied and virtual subspaces separately after a self-consistent field calculation. This separated linear combination ensures that configuration interaction singles (CIS), random phase approximation (RPA), and their corresponding density functional theory (DFT) counterparts [Tamm-Dancoff approximation time-dependent DFT (TDA-TDDFT) and TDDFT] calculations with LMOs are mathematically equivalent to those performed with CMOs. We performed tests on simple symmetric and asymmetric dimer systems and found that the excited states are numerically identical in excitation energies and transition moments for both LMOs and CMOs, except for very few states that are only found in either LMO or CMO (in symmetric cases). The LMO basis makes both qualitative and quantitative analyses of the excited states much more accessible, as the extent of LE and CTE contributions can be easily defined. Consequently, this simple yet robust approach can be useful for characterizing excitons in multichromophoric systems and in condensed phases, which is useful when studying problems pertaining to electron/excitation energy transfer processes.

PPP1R1A (protein phosphatase 1 regulatory inhibitor subunit 1A) is a cAMP/PKA-responsive inhibitor of protein phosphatase 1 (PP1) with a pivotal role in pancreatic β-cell physiology. To investigate its functional impact, Ppp1r1a was silenced in INS-1 (832/13) rat β-cells, and proteomic alterations were profiled using label-free DIA mass spectrometry (Orbitrap Exploris 480) with a rat spectral library. Quantitative analysis (n = 4/group) identified ∼2846 proteins with >2-fold change, revealing extensive proteome reprogramming. Key biological processes affected included vesicle trafficking and exocytosis, insulin biosynthesis and processing, organelle organization, mRNA processing, and autophagy. Pathway enrichment highlighted disruptions in insulin secretion, insulin resistance, and mTOR signaling. Crucial β-cell proteins, including INS2, Cacna1a, CPEB2, PCSK2, SNAP25, SYT5, and VAMP7, were significantly downregulated. Validation confirmed reduced phosphorylated AKT levels and p-AKT/T-AKT ratio, consistent with impaired mTOR signaling. Collectively, these findings demonstrate that PPP1R1A is essential for maintaining β-cell function and insulin secretion, and its depletion triggers broad proteomic and signaling alterations. Thus, PPP1R1A emerges as a regulatory node with potential therapeutic relevance in modulating β-cell activity and insulin dynamics in diabetes.

Routine EEG with quantitative analysis to detect delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage.

Individual micron-sized coal aerosol particle for quantitative analysis based on hollow laser trapping-LIBS and machine learning.

Integrated screening of quality markers from Paris polyphylla var. yunnanensis using UHPLC-Q/TOF-HRMS, spectrum-effect relationship analysis, network pharmacology, and quantitative analysis.

Introduction: Deformable image registration is an essential task in medical image analysis. The UNet model, or the model with the U-shaped structure, has been popularly proposed in deep learning-based registration methods. However, they easily lose the important similarity information in the up-sampling stage, and these methods usually ignore the inherent inverse consistency of the transformation between a pair of images. Furthermore, the traditional smoothing constraints used in the existing methods can only partially ensure the folding of the deformation field. Method: An inverse consistent deformable medical image registration network(ICSANet) based on the inverse consistency constraint and the similarity-based local attention is developed. A new UNet network is constructed by introducing similarity-based local attention to focus on the spatial correspondence in the high-similarity space. A novel inverse consistency constraint is proposed, and the objective function of the new form is presented with the combination of the traditional constraint conditions. Experiment: The performance of the proposed method is compared with the typical registration models, such as the VoxelMorph, PVT, nnFormer, and TransMorph-diff model, on the brain IXI and OASIS datasets. Result: Experimental results on the brain MRI datasets show that the images can be deformed symmetrically until two distorted images are well matched. The quantitative comparison and visual analysis indicate that the proposed method performs better, and the Dice index can be improved by at least 12% with only 10% parameters. Conclusion: This paper presents a new medical image registration network, ICSANet. By introducing a similarity attention gate, it accurately captures high-similarity spatial correspondences between source and target images, resulting in better registration performance.