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Spatial transcriptomics technologies have revolutionized our understanding of tissue heterogeneity by providing gene expression data with spatial context. However, due to the limited resolution of some of these techniques, multiple cell types are often captured at each spatial location. Several spatial deconvolution methods have been developed in recent years to infer cell type compositions from these mixed spots, but their results can be noisy and spatially inconsistent. In this study, we introduce a spatial smoothing method, Spatialsmooth based on convolutional autoencoder, in which several spatial deconvolution tools are integrated to obtain the deconvolution results, the spatial location information is fully utilized using positional encoding, and the deconvolved cell type compositions are smoothed using a convolutional autoencoder to optimize the distribution of different cell type compositions in space. This method integrates multiple spatial deconvolutions tools and fully utilizes spatial location information and cell type composition inferred by multiple deconvolution algorithms to produce smooth, biologically plausible cell type distributions. The smoothing effect was measured by using three spatial metrics, namely, Moran's I, Geary's C, and Total Variation, on multiple pairs of datasets. pancreatic ductal adenocarcinomas (PDAC) dataset, Spatialsmooth reached 0.52 on the Moran's I metric (92% higher than the Redeconve method), the Geary's C metric was reduced to 0.47 (a 45% improvement compared to RCTD), and Total Variation was reduced by 22%. Applied to benchmark data, multiple cell types and molecular markers with different spatial localizations were identified, closer to the spatial projection of marker genes than other deconvolution tools. ( https://github.com/njjyxl/Spatialsmooth ).

Cancer classification using gene expression data presents significant challenges due to high dimensionality and complex biological patterns. While integrated learning approaches show promise, current methods often fail to capture complex model interactions and lack automated optimization frameworks. We developed a novel hybrid machine learning framework combining traditional machine learning models with a Transformer-based stacked architecture for cancer classification. The framework employs a parallel optimization strategy for multiple base models (Random Forest, Gradient Boosting, eXtreme Gradient Boosting, and support vector machines) and utilizes a Transformer-based meta-learner for ensemble prediction. We evaluated the framework on a comprehensive cancer gene expression dataset containing 3,181 samples across five cancer types. The proposed framework achieved superior performance with over 99.7% accuracy across all cancer types. The Transformer-based meta-learner demonstrated particular effectiveness in handling complex cases, showing significant improvements in classification accuracy for rare cancer subtypes compared to traditional methods. Our hybrid framework successfully leverages the strengths of traditional machine learning algorithms and the advanced pattern recognition capabilities of Transformer architecture to provide a robust and interpretable solution for cancer classification. The research results indicate that this framework has potential application value in the fields of cancer diagnosis and model interpretability, marking an important advancement in the integration of learning technologies in the field of cancer classification.

Tertiary lymphoid structures (TLSs) are ectopic lymphoid formations that arise in non-lymphoid tissues due to chronic inflammation. The pivotal function of TLSs in regulating tumor invasion and metastasis has been established across several cancers, such as lung cancer, liver cancer, and melanoma, with a positive correlation between increased TLS presence and improved prognosis. Nevertheless, the current research about the clinical significance of TLSs in breast cancer remains limited. In our investigation, we discovered TLS-critical genes that may impact the prognosis of breast cancer patients, and categorized breast cancer into three distinct subtypes based on critical gene expression profiles, each exhibiting substantial differences in prognosis (p = 0.0046, log-rank test), with Cluster 1 having the best prognosis, followed by Cluster 2, and Cluster 3 having the worst prognosis. We explored the impact of the heterogeneity of these subtypes on patient prognosis, the differences in the molecular mechanism, and their responses to drug therapy and immunotherapy. In addition, we designed a machine learning-based classification model, unveiling highly consistent prognostic distinctions in several externally independent cohorts. A notable marker gene CXCL13 was identified in Cluster 3, potentially pivotal in enhancing patient prognosis. At the single-cell resolution, we delved into the adverse prognosis of Cluster 3, observing an enhanced interaction between fibroblasts, myeloid cells, and basal cells, influencing patient prognosis. Furthermore, we identified several significantly upregulated genes (CD46, JAG1, IL6, and IL6R) that may positively correlate with cancer cells' survival and invasive capabilities in this subtype. Our study is a robust foundation for precision medicine and personalized therapy, presenting a novel perspective for the contemporary classification of breast cancer.

Single-cell annotation plays a crucial role in the analysis of single-cell genomics data. Despite the existence of numerous single-cell annotation algorithms, a comprehensive tool for integrating and comparing these algorithms is also lacking. This study meticulously investigated a plethora of widely adopted single-cell annotation algorithms. Ten single-cell annotation algorithms were selected based on the classification of either reference dataset-dependent or marker gene-dependent approaches. These algorithms included SingleR, Seurat, sciBet, scmap, CHETAH, scSorter, sc.type, cellID, scCATCH, and SCINA. Building upon these algorithms, we developed an R package named scAnnoX for the integration and comparative analysis of single-cell annotation algorithms. The development of the scAnnoX software package provides a cohesive framework for annotating cells in scRNA-seq data, enabling researchers to more efficiently perform comparative analyses among the cell type annotations contained in scRNA-seq datasets. The integrated environment of scAnnoX streamlines the testing, evaluation, and comparison processes among various algorithms. Among the ten annotation tools evaluated, SingleR, Seurat, sciBet, and scSorter emerged as top-performing algorithms in terms of prediction accuracy, with SingleR and sciBet demonstrating particularly superior performance, offering guidance for users. Interested parties can access the scAnnoX package at https://github.com/XQ-hub/scAnnoX.

In order to comprehend the effects of the duration of pesticide residual effectiveness on successful pest control, a stochastic integrated pest management (IPM) model with pesticides which have residual effects is proposed. Firstly, we show that our model has a global and positive solution and give its explicit expression when pest goes extinct. Then the sufficient conditions for pest extinction combined with the ones for the global attractivity of the pest solution only chemical control are established. Moreover, we also derive sufficient conditions for weak persistence which show that the solution of stochastic IPM models is stochastically ultimately bounded under some conditions.

This paper provides a new delay-dependent stabilization criterion for systems with two additive time-varying delays. The novel functional is constructed, a tighter upper boundof the derivative of the Lyapunov functional is obtained. These results have advantages over some existing ones because the combination of the delay decomposition technique and the reciprocally convex approach. Two examples are provided to demonstrate the less conservatism and effectiveness of the results in this paper.

This paper aims to examine the influence of body shape on income, which varies with gender and occupational structure in China. The data were obtained from the CGSS (Chinese General Social Survey) 2010–2017 Survey. The overall finding in this paper is that women and men face different body shape–income effects. For females, the obesity penalty is significant and is reinforced with increasing occupational rank. For men, the thinness penalty (or weight premium) is enhanced as the occupational class decreases. Body shape–income gaps are mainly caused by the occupational structure. Twenty-nine percent of the income gap between overweight and average weight women can be explained by the obesity penalty, 37% of the income gap between overweight and average weight men can be interpreted by the weight premium, and 11% of the gap between underweight and normal weight men can be explained by the thinness penalty. The findings also suggest that the effect of body shape on income consists of two pathways: body shape affects health capital and socialization, and therefore income. Healthy lifestyles and scientific employment concepts should be promoted, and measures to close the gender gap should be implemented.

Accurately forecasting the trajectory of stock prices holds crucial significance for investors in mitigating investment risks and making informed decisions. Candlestick charts visually depict price information and the trends in stocks, harboring valuable insights for predicting stock price movements. Therefore, the challenge lies in efficiently harnessing candlestick patterns to forecast stock prices. Furthermore, the selection of hyperparameters in network models has a profound impact on the forecasting outcomes. Building upon this foundation, we propose a stock price prediction model SSA-CPBiGRU that integrates candlestick patterns and a sparrow search algorithm (SSA). The incorporation of candlestick patterns endows the input data with structural characteristics and time series relationships. Moreover, the hyperparameters of the CPBiGRU model are optimized using an SSA. Subsequently, the optimized hyperparameters are employed within the network model to conduct predictions. We selected six stocks from different industries in the Chinese stock market for experimentation. The experimental results demonstrate that the model proposed in this paper can effectively enhance the prediction accuracy and has universal applicability. In comparison to the LSTM model, the proposed model produces an average of 31.13%, 24.92%, and 30.42% less test loss in terms of MAPE, RMSE and MAE, respectively. Moreover, it achieves an average improvement of 2.05% in R2.

A novel “Bernoulli experiment model” of constant false alarm rate (CFAR) is presented in order to analyze CFAR performance in jamming condition. In this model, target detection can be treated as a sequence of independent subevents, and the detection process equals to pick up target energy from an energy pool diluted with noise, interference, and jamming pulses. In a multiple-jammer case, each jammer contributes independently, and the content of the energy pool only relates to the corresponding jammer. Impacts of jammer factors, such as signal-to-interference ratio (SIR), jamming-to-interference ratio (JIR), the number of false targets in reference cells, and the number of false targets in a cell under test (CUT), are analyzed and compared in detail, and two jamming operation principles are deduced as an application. The deduction procedure and conclusions drawn from the model can also be utilized on similar occasions.

Accurately measuring the cutting temperature in micro cutting zone is crucial for characterizing and optimizing the cutting status during ultra-precision machining. This work proposes an innovative method for self-sensing of cutting temperature using a locally boron-doped diamond tool. A longitudinal layered deposition synthesis methodology, instead of the traditional growth method under high temperature and high pressure conditions (HTHP), was developed to enable the fabrication of the locally boron-doped diamond tool. The doping contents, lattice integrity, and electrical properties of the diamond were characterized. Owing to the inherently low thermal capacity and quick carrier migration induced by the thin-layer structure for sensing temperature, the diamond tool has the advantages of rapid response and enhanced sensitivity, compared with traditional cutting temperature measurement technologies. An insulated diamond tool edge without boron doping enables to accurately measure cutting temperature for various conductive materials in ultra-precision cutting processes. The locally boron-doped diamond tool was employed for in-process monitoring of the temperature in micro cutting zone during ultra-precision machining processes. The experimental results demonstrated the capabilities of in-process cutting temperature monitoring of conductive materials using the diamond tool, as well as the high-sensitivity identification of micro/nano morphologies and defects on machined surface based on the measured temperature. It provides a potential approach for advanced status analysis and diagnosis in the process of ultra-precision machining.