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Prognostics and health management (PHM) technology aims to analyze and diagnose the state of equipment using a large amount of data, predict potential failures, and adopt corresponding maintenance and repair strategies to enhance equipment reliability, reduce repair costs, and prevent production interruptions. In this paper, we propose a remaining useful life (RUL) prediction model based on Mamba, which incorporates learnable parameters and a multi-head attention mechanism; to address the issues faced by traditional algorithms, which struggle to efficiently capture dependencies in long sequences and parallelize the processing of these sequences. Firstly, min-max scaling and exponential smoothing techniques are used to preprocess the feature data in order to prevent gradient explosion while speeding up the convergence of the model. Secondly, a learnable scaling parameter is introduced into the Residual block to adjust the output, and a multi-head attention mechanism is innovatively integrated into the Mamba block to operate on the data processed by the convolutional layer, thereby enhancing the expressiveness and accuracy of the model. Lastly, the model is compared with the current state-of-the-art research findings on aero-engine and lithium-ion batteries datasets, and the experimental results demonstrate that the model outperforms the current state-of-the-art methods in RUL prediction tasks, exhibiting better generalization, and can be applied as a general RUL prediction method to other fields.

Deep learning‐based classification algorithms have been used for automatic modulation recognition (AMR). However, most methods only focus on end‐to‐end mapping and neglect the classic key features. In this paper, signals are enforced with key classification features to propose a novel deep learning AMR model by learning the shared latent space of the aligned signals and key features (LLAF); this increases the generalizability of the model and ensures the physical plausibility of the results. To obtain adequate signal representations, an encoder–decoder architecture is proposed to learn the shared latent space, and the architecture is trained to approximate prior label distributions for precise signal classification. Simulation results verify the high recognition accuracy of the proposed LLAF model under different signal‐to‐noise ratios (SNRs).

This article presents the numerical solutions of nonlinear stochastic It –Volterra integral equations by using the basis function method under the global Lipschitz condition. Integral operator matrixes of triangular functions are used to convert the nonlinear stochastic integral equations into a system of algebraic equations. Meanwhile, we gain the error of the current method, and it is demonstrated that the error accuracy of this method is higher than that of the BPFs. In the end, the feasibility, accuracy, and validity of the current method are demonstrated by numerical results.

To solve the technical bottleneck caused by the absence of a feasible method for removing the spermatheca in social insects, we developed a microsurgical technique specifically designed for bumblebee females. In this study, the invention of this technique is based on the anatomical characteristics of the sting chamber of bumblebees and uses a bespoke scalpel to precisely remove the spermatheca, which is small in size and deeply embedded within the body. During the removal operation, a small wound was observed and a small amount of hemolymph flowed out. The wound healed very quickly and the survival rate of treated individuals was high. The results showed that there was no significant impact on the critical life activities of queens and workers, including longevity, mating behavior, oviposition capacity, and overwintering survival rate after the spermatheca was removed using this technique. These findings further confirm the feasibility and applicability of the technique and provide strong technical support for exploring the evolutionary dynamics and potential function of the spermatheca in social insects.

Background Despite research efforts, the causative factors that contribute to esophageal squamous cell carcinoma (ESCC) in high-risk areas have not yet been understood. In this study, we, therefore, aimed to describe the risk factors associated with ESCC and its precursor lesions. Methods We performed an endoscopic examination of 44,857 individuals aged 40–69 years from five high incidence regions of China in 2017–2018. Participants were classified as 4 groups of normal control, esophagitis, low-grade intraepithelial neoplasia (LGIN) and high-grade intraepithelial neoplasia/esophageal squamous cell carcinoma (HGIN/ESCC) using an unconditional logistic regression determine risk factors. Results We identified 4890 esophagitis, 1874 LGIN and 437 HGIN/ESCC cases. Crude odds ratios (ORs) and adjusted odds ratios were calculated using unconditional logistic regression. Drinking well and surface water, salty diet, and positive family history of cancer were the common risk factors for esophagitis, LGIN and HGIN/ESCC. History of chronic hepatitis/cirrhosis was the greatest risk factor of esophagitis (adjusted OR 2.96, 95%CI 2.52–3.47) and HGIN/ESCC (adjusted OR 1.91, 95%CI 1.03–3.22). Pesticide exposure (adjusted OR 1.20, 95%CI 1.05–1.37) was essential risk factor of LGIN. Conclusions Among individuals aged 40–69 years in high incidence regions of upper gastrointestinal cancer, the results provided important epidemiological evidence for the prevention of different precancerous lesions of ESCC.

The emergence of caste-differentiated colonies, which have been defined as ‘superorganisms’, in ants, bees, and wasps represents a major transition in evolution. Lifetime mating commitment by queens, pre-imaginal caste determination and lifetime unmatedness of workers are key features of these animal societies. Workers in superorganismal species like honey bees and many ants have consequently lost, or retain only vestigial spermathecal structures. However, bumble bee workers retain complete spermathecae despite 25-40 million years since their origin of superorganismality, which remains an evolutionary mystery. Here, we show (i) that bumble bee workers retain queen-like reproductive traits, being able to mate and produce colonies, underlain by queen-like gene expression, (ii) the social conditions required for worker mating, and (iii) that these abilities may be selected for by early queen-loss in these annual species. These results challenge the idea of lifetime worker unmatedness in superorganisms, and provide an exciting new tool for the conservation of endangered bumble bee species. Workers in social insects such as honey bees, bumble bees, and ants are expected to spend their lives helping their mother reproduce. Here the authors show that workers of several bumble bee species can in fact mate and lead colonies of their own.

Lysosome, the digestive organelle in eukaryotic cells, plays an important role in the degradation and recirculation of cellular products as well as in maintaining the stability of cellular metabolic microenvironment. Surface‐enhanced Raman scattering (SERS) is a molecular fingerprint technology with high detection sensitivity and photostability, suited for revealing various intracellular molecular information by inducing endocytosis of SERS‐active nanoparticles. However, it remains challenging to selectively extract the molecular information of specific organelles (e.g., lysosomes) from a high‐dimensional spectral set. Herein, we proposed a novel paradigm by combining label‐free SERS spectroscopy with confocal fluorescence imaging to investigate the digestion behavior of lysosomes in cells. The structural similarity algorithm was innovatively introduced and exhibited its effectiveness in screening out the wavenumbers in the SERS spectral set with high correlation with the metabolic behaviors of lysosomes. With comprehensive experiments on HeLa single cells, we captured the intracellular macromolecular digestion phenomenon and discovered the changing pattern of cellular SERS spectra after starvation‐induced autophagy, and analyzed the molecular information within the lysosomes in three‐dimensional space. The authors have developed an approach by combining label‐free surface‐enhanced Raman spectroscopy with confocal fluorescence imaging to decode the digestion behavior of lysosomes in live single cells with the assistance of the structural similarity algorithm.

Metabolic dysregulation is a key driver of cellular senescence, contributing to the progression of systemic aging. The heterogeneity of senescent cells and their metabolic shifts are complex and unexplored. A microfluidic SlipChip integrated with surface‐enhanced Raman spectroscopy (SERS), termed SlipChip‐SERS, is developed for single‐cell metabolism analysis. This SlipChip‐SERS enables compartmentalization of single cells, parallel delivery of saponin and nanoparticles to release intracellular metabolites and to realize SERS detection with simple slipping operations. Analysis of different cancer cell lines using SlipChip‐SERS demonstrated its capability for sensitive and multiplexed metabolic profiling of individual cells. When applied to human primary fibroblasts of different ages, it identified 12 differential metabolites, with spermine validated as a potent inducer of cellular senescence. Prolonged exposure to spermine can induce a classic senescence phenotype, such as increased senescence‐associated β‐glactosidase activity, elevated expression of senescence‐related genes and reduced LMNB1 levels. Additionally, the senescence‐inducing capacity of spermine in HUVECs and WRL‐68 cells is confirmed, and exogenous spermine treatment increased the accumulation and release of H2O2. Overall, a novel SlipChip‐SERS system is developed for single‐cell metabolic analysis, revealing spermine as a potential inducer of senescence across multiple cell types, which may offer new strategies for addressing ageing and ageing‐related diseases. The SlipChip‐SERS system enables label‐free and sensitive profiling of single‐cell metabolism, offering a powerful tool to investigate cellular aging‐related metabolic alterations. Spermine is identified as a potential inducer of cellular senescence through single‐cell analysis, which illuminates metabolic changes in aging and advances the comprehension of the process of senescence mechanisms and therapeutic targets.

Idiopathic membranous nephropathy (IMN) is a leading cause of nephrotic syndrome in middle-aged and elderly populations. Early intervention can delay disease progression and improve patient outcomes. This study aims to identify urinary biomarkers for IMN and investigate their association with disease progression, offering new insights for precise diagnosis and treatment. This study began with RNA sequencing of three urine sample types (first-void morning urine, second-void morning urine, and random urine), combined with single-cell RNA sequencing of renal tissues. Bioinformatics analyses-including differential gene expression screening, machine learning, and molecular function annotation-were employed to identify potential IMN biomarkers. Furthermore, we established both a siRNA-mediated gene silencing model and a lentivirus transfection-mediated gene overexpression model in HK-2 cells. Subsequently, we investigated the functional mechanisms of the candidate biomarkers through qRT-PCR, Western blot, immunohistochemistry, and immunofluorescence assays. SPP1 was identified as a promising biomarker for IMN, demonstrating a critical role in promoting fibrosis and inflammatory responses associated with the disease. These findings suggest its potential as a novel therapeutic target for IMN intervention.

Spectral interference from backgrounds is not negligible for surface-enhanced Raman scattering (SERS) tags and often influences the accuracy and reliability of SERS applications. We report the design and synthesis of orthogonal gap-enhanced Raman tags (O-GERTs) by embedding alkyne and deuterium-based reporters in the interior metallic nanogaps of core–shell nanoparticles and explore their signal orthogonality as optical probes against different backgrounds from common substrates and media (e.g., glass and polymer) to related targets (e.g., bacteria, cancer cells, and tissues). Proof-of-concept experiments show that the O-GERT signals in the fingerprint region (200–1800 cm ) are likely interfered by various backgrounds, leading to difficulty of accurate quantification, while the silent-region (1800–2800 cm ) signals are completely interference-free. Moreover, O-GERTs show much higher photo and biological stability compared to conventional SERS tags. This work not only demonstrates O-GERTs as universal optical tags for accurate and reliable detection onto various substrates and in complex media, but also opens new opportunities in a variety of frontier applications, such as three-dimensional data storage and security labeling.