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Background: This study endeavored to develop a nicotinamide adenine dinucleotide (NAD+) metabolism-related biomarkers in gastric cancer (GC), which could provide a theoretical foundation for prognosis and therapy of GC patients. Methods: In this study, differentially expressed genes (DEGs1) between GC and paraneoplastic tissues were overlapped with NAD+ metabolism-related genes (NMRGs) to identify differentially expressed NMRGs (DE-NMRGs). Then, GC patients were divided into high and low score groups by gene set variation analysis (GSVA) algorithm for differential expression analysis to obtain DEGs2, which was overlapped with DEGs1 for identification of intersection genes. These genes were further analyzed using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses to obtain prognostic genes for constructing a risk model. Enrichment and immune infiltration analyses further investigated investigate the different risk groups, and qRT-PCR validated the prognostic genes. Results: Initially, we identified DE-NMRGs involved in NAD biosynthesis, with seven (DNAJB13, CST2, THPO, CIDEA, ONECUT1, UPK1B and SNCG) showing prognostic significance in GC. Subsequent, a prognostic model was constructed in which the risk score, derived from the expression profiles of these genes, along with gender, emerged as robust independent predictors of patient outcomes in GC. Enrichment analysis linked high-risk patients to synaptic membrane pathways and low-risk to the CMG complex pathway. Tumor immune infiltration analysis revealed correlations between risk scores and immune cell abundance, suggesting a relationship between NAD+ metabolism and immune response in GC. The prognostic significance of our identified genes was validated by qRT-PCR, which confirmed their upregulated expression in GC tissue samples. Conclusion: In this study, seven NAD+ metabolism-related markers were established, which is of great significance for the development of prognostic molecular biomarkers and clinical prognosis prediction for gastric cancer patients. [ABSTRACT FROM AUTHOR]

Peak detection is an important step in mass spectrometry as accurately identifying characteristic peaks is key to data analysis. In order to address the issue of false peak detection, while simultaneously ensuring accurate detection of weak and overlapped peaks, this paper introduces an improved algorithm for mass spectrometry integrating weighted continuous wavelet transform with particle swarm optimization-based Otsu (WWTPO). The algorithm applies the weighted continuous wavelet transform (WCWT) to compress the frequency spectrum signal into a smaller scale range, which allows for the acquisition of more distinct and informative peak information. Moreover, the algorithm employs the particle swarm optimization (PSO) algorithm to iteratively evaluate the optimal image segmentation threshold, which addresses the challenge of inaccurate Otsu image segmentation. The method was applied to detect simulated peaks as well as matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) datasets. The performance evaluation was conducted using receiver operating characteristic (ROC) curves, F1 measure and F-scores. Through comparison with continuous wavelet transform (CWT) and genetic algorithm-based threshold segmentation (WSTGA), multi-scale peak detection (MSPD) and CWT and image segmentation (CWT-IS), the results demonstrate that WWTPO exhibits excellent performance in peak detection. The determination of 4-isopropyltoluene also demonstrates that WWTPO has excellent practical application. This method not only maintains a low false peak identification rate but also detects more weak peaks and overlapping peaks, further improving the accuracy and efficiency of peak detection in mass spectrometry. [ABSTRACT FROM AUTHOR]

Mass spectrometry data may be affected by random noise and baseline drift due to experimental instruments and conditions, posing significant challenges for detecting spectral peaks, particularly when identifying weak and separating overlapping peaks. To increase the sensitivity and enhance the resolution, we propose a mass spectral peak detection algorithm that integrates resolution enhancement and image segmentation. Initially, the extended Mexican hat wavelet is proposed by integrating the peak sharpening method with its wavelet. This approach accurately transforms mass spectra into wavelet space using the continuous wavelet transform. Subsequently, the triangular single-peak thresholding method, a more suitable threshold segmentation approach for spectral analysis, is introduced to identify ridges in the two-dimensional wavelet space. Compared to traditional Otsu and its improved variants, long-tailed single-peaked histograms are more effectively processed by this method with lower computational complexity, enabling faster identification of segmentation thresholds and image segmentation. Ultimately, peak positions are determined by utilizing ridge and valley lines in wavelet space along with the original spectrum. To evaluate the performance of the peak recognition algorithm, two metrics are introduced: the receiver operating characteristic (ROC) curve and the balanced F score (F1 score). When compared to multi-scale peak detection (MSPD), continuous wavelet transform and image segmentation (CWT-IS), the developed approach is more suitable for weak and highly overlapping peaks. The robustness and practicality of the method are verified through peak detection using matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectra. [ABSTRACT FROM AUTHOR]

Compute‐in‐memory (CIM) is a pioneering approach using parallel data processing to eliminate traditional data transmission bottlenecks for faster, energy‐efficient data handling. Crossbar arrays with two‐terminal devices such as memristors and phase‐change memory are commonly employed in CIM, but they encounter challenges such as leakage current and increased power usage. Three‐terminal transistor arrays have potential solutions, yet large‐scale electrolyte‐gated transistors (EGTs) demonstrations are uncommon due to compatibility issues with existing photolithography processes. Herein, a 20 × 20 EGTs array is designed using indium‐gallium‐zinc‐oxide as the semiconductor channel and polyacrylonitrile (PAN) doped with C2F6LiNO4S2 as the electrolyte. Each transistor unit in the array can serve as a synapse, exhibiting a large conductance range, low energy consumption (6.984 fJ) for read–write operations, excellent repeatability, and quasilinear update characteristics. It has been confirmed that the EGTs array not only enables precise device programming but also virtually eliminates signal interference between neighboring devices during the programming process. Using 54 transistors in the EGTs array, unsupervised learning with a winner‐takes‐all neural network is successfully demonstrated. After 50 training iterations, the neural network achieves perfect 100% accuracy in classifying test‐set letters. The work demonstrates the potential of EGTs for constructing large‐scale integration synaptic array toward efficient computing architectures. [ABSTRACT FROM AUTHOR]

To address susceptibility to noise interference in Micro-LED displays, a deep convolutional dictionary learning denoising method based on distributed image patches is proposed in this paper. In the preprocessing stage, the entire image is partitioned into locally consistent image patches, and a dictionary is learned based on the non-local self-similar sparse representation of distributed image patches. Subsequently, a convolutional dictionary learning method is employed for global self-similarity matching. Local constraints and global constraints are combined for effective denoising, and the final denoising optimization algorithm is obtained based on the confidence-weighted fusion technique. The experimental results demonstrate that compared with traditional denoising methods, the proposed denoising method effectively restores fine-edge details and contour information in images. Moreover, it exhibits superior performance in terms of PSNR and SSIM. Particularly noteworthy is its performance on the grayscale dataset Set12. When evaluated with Gaussian noise σ = 50 , it outperforms DCDicL by 3.87 dB in the PSNR and 0.0012 in SSIM. [ABSTRACT FROM AUTHOR]

Bioorthogonal chemistry capable of operating in live animals is needed to investigate biological processes such as cell death and immunity. Recent studies have identified a gasdermin family of pore-forming proteins that executes inflammasome-dependent and -independent pyroptosis.sup.1-5. Pyroptosis is proinflammatory, but its effect on antitumour immunity is unknown. Here we establish a bioorthogonal chemical system, in which a cancer-imaging probe phenylalanine trifluoroborate (Phe-BF.sub.3) that can enter cells desilylates and 'cleaves' a designed linker that contains a silyl ether. This system enabled the controlled release of a drug from an antibody-drug conjugate in mice. When combined with nanoparticle-mediated delivery, desilylation catalysed by Phe-BF.sub.3 could release a client protein--including an active gasdermin--from a nanoparticle conjugate, selectively into tumour cells in mice. We applied this bioorthogonal system to gasdermin, which revealed that pyroptosis of less than 15% of tumour cells was sufficient to clear the entire 4T1 mammary tumour graft. The tumour regression was absent in immune-deficient mice or upon T cell depletion, and was correlated with augmented antitumour immune responses. The injection of a reduced, ineffective dose of nanoparticle-conjugated gasdermin along with Phe-BF.sub.3 sensitized 4T1 tumours to anti-PD1 therapy. Our bioorthogonal system based on Phe-BF.sub.3 desilylation is therefore a powerful tool for chemical biology; our application of this system suggests that pyroptosis-induced inflammation triggers robust antitumour immunity and can synergize with checkpoint blockade. In mouse models of cancer, a biorthogonal chemical system based on desilylation catalysed by phenylalanine trifluoroborate enables the controlled release of gasdermin to induce pyroptosis selectively in tumour cells, Author(s): Qinyang Wang [sup.1] , Yupeng Wang [sup.2] [sup.3] , Jingjin Ding [sup.3] [sup.4] , Chunhong Wang [sup.1] , Xuehan Zhou [sup.1] , Wenqing Gao [sup.3] , Huanwei Huang [sup.3] [...]

Rationale: Ion mobility spectrometry (IMS), as a promising analytical tool, has been widely employed in the structural characterization of biomolecules. Nevertheless, the inherent limitation in the structural resolution of IMS frequently results in peak overlap during the analysis of isomers exhibiting comparable structures. Methods: The radial basis function (RBF) neural network optimization algorithm based on dynamic inertial weight particle swarm optimization (DIWPSO) was proposed for separating overlapping peaks in IMS. The RBF network structure and parameters were optimized using the DIWPSO algorithm. By extensively training using a large dataset, an adaptive model was developed to effectively separate overlapping peaks in IMS data. This approach successfully overcomes issues related to local optima, ensuring efficient and precise separation of overlapping peaks. Results: The method's performance was evaluated using experimental validation and analysis of overlapping peaks in the IMS spectra of two sets of isomers: 3′/6′‐sialyllactose; fructose‐6‐phosphate, glucose‐1‐phosphate, and glucose‐6‐phosphate. A comparative analysis was conducted using other algorithms, including the sparrow search algorithm, DIWPSO algorithm, and multi‐objective dynamic teaching‐learning‐based optimization algorithm. The comparison results show that the DIWPSO‐RBF algorithm achieved remarkably low maximum relative errors of only 0.42%, 0.092%, and 0.41% for ion height, mobility, and half peak width, respectively. These error rates are significantly lower than those obtained using the other three algorithms. Conclusions: The experimental results convincingly demonstrate that this method can adaptively, rapidly, and accurately separate overlapping peaks of multiple components, improving the structural resolution of IMS. [ABSTRACT FROM AUTHOR]

N- and O-glycosylation modifications of proteins are closely linked to the onset and development of many diseases and have gained widespread attention as potential targets for therapy and diagnosis. However, the low abundance and low ionization efficiency of glycopeptides as well as the high heterogeneity make glycosylation analysis challenging. Here, an enrichment strategy, using Knoevenagel copolymers modified with polydopamine–adenosine (denoted as PDA–ADE@KCP), was firstly proposed for simultaneous enrichment of N- and O-glycopeptides through the synergistic effects of hydrophilic and electrostatic interactions. The adjustable charged surface and hydrophilic properties endow the material with the capability to achieve effective enrichment of intact N- and O-glycopeptides. The experimental results exhibited excellent selectivity (1 : 5000) and sensitivity (0.1 fmol μL−1) of the prepared material for N-glycopeptides from standard protein digest samples. Moreover, it was further applied to simultaneous capturing of N- and O-glycopeptides from mouse liver protein digests. Compared to the commercially available zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC) material, the number of glycoproteins corresponding to all N- and O-glycopeptides enriched with PDA–ADE@KCP was much more than that with ZIC-HILIC. Furthermore, PDA–ADE@KCP captured more O-glycopeptides than ZIC-HILIC, revealing its superior performance in O-glycopeptide enrichment. All these results indicated that the strategy holds immense potential in characterizing N- and O-intact glycopeptides in the field of proteomics. [ABSTRACT FROM AUTHOR]

Despite the popularity of ion mobility spectrometry (IMS) for glycan analysis, its limited structural resolution hinders the effective separation of many glycan isomers. This leads to the overlap of IMS peaks, consequently impacting the accurate identification of glycan compositions. To this end, an improved algorithm, namely second-order differentiation combined with a simulated annealing particle swarm optimization algorithm based on sine adaptive weights (DWSA-PSO), was proposed for the separation of overlapping IMS peaks formed by glycan isomers. DWSA-PSO first performed second-order differentiation to automatically determine the number of components in overlapping peaks and exclude impossible single-peak combinations. It then introduced sinusoidal adaptive weights and a simulated annealing mechanism to improve the algorithm's search capability and global optimization performance, thereby enabling accurate and efficient separation of individual peaks. To evaluate the performance of DWSA-PSO and its application to the separation of glycan isomers, multiple sets of overlapping peaks with different degrees of overlap were simulated, and various types of multi-component overlapping peaks were formed using six disaccharide and four trisaccharide isomers. The experimental results consistently demonstrated that the DWSA-PSO algorithm outperformed both the improved particle swarm optimization (IPSO) algorithm and the dynamic inertia weight particle swarm optimization (DIWPSO) algorithm in terms of separation accuracy, running time, and fitness values. In addition, the DWSA-PSO algorithm was successfully applied to the separation of glycan isomers in malt milk beverage. All these results reveal the capability of the DWSA-PSO algorithm to facilitate the accurate identification of glycan isomers. [ABSTRACT FROM AUTHOR]