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Due to the inherent characteristics of accumulation sequence of unbalanced data, the mining results of this kind of data are often affected by a large number of categories, resulting in the decline of mining performance. To solve the above problems, the performance of data cumulative sequence mining is optimized. The algorithm for mining cumulative sequence of unbalanced data based on probability matrix decomposition is studied. The natural nearest neighbor of a few samples in the unbalanced data cumulative sequence is determined, and the few samples in the unbalanced data cumulative sequence are clustered according to the natural nearest neighbor relationship. In the same cluster, new samples are generated from the core points of dense regions and non core points of sparse regions, and then new samples are added to the original data accumulation sequence to balance the data accumulation sequence. The probability matrix decomposition method is used to generate two random number matrices with Gaussian distribution in the cumulative sequence of balanced data, and the linear combination of low dimensional eigenvectors is used to explain the preference of specific users for the data sequence; At the same time, from a global perspective, the AdaBoost idea is used to adaptively adjust the sample weight and optimize the probability matrix decomposition algorithm. Experimental results show that the algorithm can effectively generate new samples, improve the imbalance of data accumulation sequence, and obtain more accurate mining results. Optimizing global errors as well as more efficient single-sample errors. When the decomposition dimension is 5, the minimum RMSE is obtained. The proposed algorithm has good classification performance for the cumulative sequence of balanced data, and the average ranking of index F value, G mean and AUC is the best.

Intelligent-assisted assessment of pilot flight training ability is a method of automating the evaluation of pilots’ flight skills using artificial intelligence. Currently, using AI to assist or replace human instructors in flight skill assessment has become a mainstream research direction in the field of intelligent aviation. Existing flight skill assessment methods suffer from limitations in data types and insufficient assessment accuracy. To address these issues, we evaluate and predict pilot performance in simulated flight missions based on physiological signals. Following the “OODA loop” theory, we established a multimodal dataset including pilot eye movement, electroencephalogram (EEG), electrocardiogram (ECG), electrodermal signaling (EDS), heart rate, respiration, and flight attitude data. This dataset records changes in physiological rhythms and flight behaviors during pilots’ flight training at different difficulty levels. To enhance the signal-to-noise ratio, we propose an enhanced wavelet fuzzy thresholding denoising algorithm utilizing LSTM optimization. We address the problem of isolated features across different time frames in multimodal data modeling by introducing a multi-feature fusion algorithm based on STFT. Furthermore, by combining a high-efficiency sub-attention mechanism with a Transformer network, we construct a multi-classification network for intelligent-assisted assessment of pilot flight training ability, further improving the output accuracy of each category. Experiments show that our designed algorithm can achieve a classification accuracy of up to 85% on the dataset (5-fold cross-validation), which meets the requirements for auxiliary assessment of flight capabilities.

Background Survival analysis is an important part of cancer studies. In addition to the existing Cox proportional hazards model, deep learning models have recently been proposed in survival prediction, which directly integrates multi-omics data of a large number of genes using the fully connected dense deep neural network layers, which are hard to interpret. On the other hand, cancer signaling pathways are important and interpretable concepts that define the signaling cascades regulating cancer development and drug resistance. Thus, it is important to investigate potential associations between patient survival and individual signaling pathways, which can help domain experts to understand deep learning models making specific predictions. Results In this exploratory study, we proposed to investigate the relevance and influence of a set of core cancer signaling pathways in the survival analysis of cancer patients. Specifically, we built a simplified and partially biologically meaningful deep neural network, DeepSigSurvNet, for survival prediction. In the model, the gene expression and copy number data of 1967 genes from 46 major signaling pathways were integrated in the model. We applied the model to four types of cancer and investigated the influence of the 46 signaling pathways in the cancers. Interestingly, the interpretable analysis identified the distinct patterns of these signaling pathways, which are helpful in understanding the relevance of signaling pathways in terms of their application to the prediction of cancer patients’ survival time. These highly relevant signaling pathways, when combined with other essential signaling pathways inhibitors, can be novel targets for drug and drug combination prediction to improve cancer patients’ survival time. Conclusion The proposed DeepSigSurvNet model can facilitate the understanding of the implications of signaling pathways on cancer patients’ survival by integrating multi-omics data and clinical factors.

Conventional crop height measurements performed using aerial drone images require 3D reconstruction results of several aerial images obtained through structure from motion. Therefore, they require extensive computation time and their measurement accuracy is not high; if the 3D reconstruction result fails, several aerial photos must be captured again. To overcome these challenges, this study proposes a high-precision measurement method that uses a drone equipped with a monocular camera and real-time kinematic global navigation satellite system (RTK-GNSS) for real-time processing. This method performs high-precision stereo matching based on long-baseline lengths (approximately 1 m) during the flight by linking the RTK-GNSS and aerial image capture points. As the baseline length of a typical stereo camera is fixed, once the camera is calibrated on the ground, it does not need to be calibrated again during the flight. However, the proposed system requires quick calibration in flight because the baseline length is not fixed. A new calibration method that is based on zero-mean normalized cross-correlation and two stages least square method, is proposed to further improve the accuracy and stereo matching speed. The proposed method was compared with two conventional methods in natural world environments. It was observed that error rates reduced by 62.2% and 69.4%, for flight altitudes between 10 and 20 m respectively. Moreover, a depth resolution of 1.6 mm and reduction of 44.4% and 63.0% in the error rates were achieved at an altitude of 4.1 m, and the execution time was 88 ms for images with a size of 5472 × 3468 pixels, which is sufficiently fast for real-time measurement.

As the core method of cooperative navigation, relative positioning plays a key role in realizing intelligent vehicle driving and vehicle self-assembling network collaboration algorithms. However, when the contamination rate of measurement noise is high, the performance of filtering will be seriously affected. To better address the filtering performance degradation problem due to noise contamination, this paper proposes a vehicular cooperative localization method based on the Maximum Correentropy Robust Square-root Cubature Kalman Filter (MCSCKF). The algorithm not only retains the advantages of Square-root Cubature Kalman Filter (SCKF) but also has strong robustness to non-Gaussian noise. The experimental results of tightly integrated vehicular cooperative navigation show that compared with the Extended Kalman Filter (EKF) and Cubature Kalman Filter (CKF), the localization accuracy of MCSCKF is improved by 35.08% and 31.83%, respectively, which verified the effectiveness in improving the accuracy and robustness of the relative position estimation.

Background Reliable identification of protein‑protein interactions (PPIs) is crucial for deciphering cellular functional networks. Current research models still face limitations in aligning heterogeneous features and handling sparse supervision signals in graph learning. To address these issues, this study proposes a prediction framework named DSS‑PPI. This framework aims to enhance prediction performance by integrating multimodal sequence semantics with self‑supervised graph learning, thereby transforming static protein sequence embeddings into dynamic, topology‑aware representations. Results DSS‑PPI employs a dual‑stream architecture that synergistically integrates ProTrek’s cross‑modal aligned embeddings with ProtT5’s deep sequence features. The study innovatively constructs a context encoder that leverages Smith‑Waterman sequence similarity as quantitative edge features to guide graph attention weights, and incorporates Deep Graph Infomax (DGI) for self‑supervised pretraining. Furthermore, a gated fusion mechanism enables the model to adaptively integrate sequence semantics with network topological information. Experimental results indicate that the model achieves competitive performance compared to existing state‑of‑the‑art algorithms on both human and multi‑species benchmark datasets, with an accuracy of 0.73 on the rigorously designed Bernett test set. Conclusions This study demonstrates the synergistic effect of multimodal embeddings and self‑supervised graph learning in PPI prediction. Ablation experiments and SHAP interpretability analysis further confirm that DSS‑PPI can effectively capture genuine physical interaction patterns. The framework provides a reliable computational tool for understanding complex biological networks and holds broad potential for biomedical applications.

Background Individuals with low socioeconomic status (SES) are at a higher risk of developing depression. However, evidence on the role of cardiovascular health (CVH) in this chain is sparse and limited. The purpose of this research was to assess the mediating role of Life’s Essential 8 (LE8), a recently updated measurement of CVH, in the association between SES and depression according to a nationally representative sample of adults. Methods Data was drawn from the National Health and Nutrition Examination Survey (NHANES) in 2013–2018. Multivariate logistic regression analysis was applied to analyze the association of SES (measured via the ratio of family income to poverty (FIPR), occupation, educational level, and health insurance) and LE8 with clinically relevant depression (CRD) (evaluated using the Patient Health Questionnaire (PHQ-9)). Multiple linear regression analysis was performed to analyze the correlation between SES and LE8. Mediation analysis was carried out to explore the mediating effect of LE8 on the association between SES and CRD. Moreover, these associations were still analyzed by sex, age, and race. Results A total of 4745 participants with complete PHQ-9 surveys and values to calculated LE8 and SES were included. In the fully adjusted model, individuals with high SES had a significantly higher risk of CRD (odds ratio = 0.21; 95% confidence interval: 0.136 to 0.325, P  < 0.01) compared with those with low SES. Moreover, LE8 was estimated to mediate 22.13% of the total association between SES and CRD, and the mediating effect of LE8 varied in different sex and age groups. However, the mediating effect of LE8 in this chain was significant in different sex, age, and racial subgroups except for Mexican American (MA) individuals. Conclusion The results of our study suggest that LE8 could mediate the association between SES and CRD. Additionally, the mediating effect of LE8 in this chain could be influenced by the race of participants.

Free of noble-metal and high in unit internal quantum efficiency of electroluminescence, organic molecules with thermally activated delayed fluorescence (TADF) features pose the potential to substitute metal-based phosphorescence materials and serve as the new-generation emitters for the mass production of organic light emitting diodes (OLEDs) display. Predicting the function of TADF emitters beyond classic chemical synthesis and material characterization experiments remains a great challenge. The advances in deep learning (DL) based artificial intelligence (AI) offer an exciting opportunity for screening high-performance TADF materials through efficiency evaluation. However, data-driven material screening approaches with the capacity to access the excited state properties of TADF emitters remain extremely difficult and largely unaddressed. Inspired by the fundamental principle that the excited state properties of TADF molecules are strongly dependent on their D-A geometric and electronic structures, we developed the Electronic Structure-Infused Network (ESIN) for TADF emitter screening. Designed with capacities of accurate prediction of the photoluminescence quantum yields (PLQYs) of TADF molecules based on elemental molecular geometry and orbital information and integrated with frontier molecular orbitals (FMOs) weight-based representation and modeling features, ESIN is a promising interpretable tool for emission efficiency evaluation and molecular design of TADF emitters. An electronic structure-infused deep-learning neural network based on frontier molecular orbitals representation is constructed to predict the emission efficiencies of thermally activated delayed fluorescence (TADF) emitters.

The aim of this study is to analyse the association of socioeconomic status (SES) with cognitive performance, and the mediation effect of periodontal status in this relationship in the National Health and Nutrition Examination Survey (NHANES) database from 2011-2014. The SES was evaluated based on poverty-income ratio (PIR), occupation, educational level, and health insurance using latent class analysis. Multivariable logistic regressions were used to determine the association of cognitive performance, examined by Consortium to Establish a Registry for Alzheimer's Disease (CERAD) test, animal fluency test (AFT), and digit symbol substitution test (DSST), with SES, attachment loss (AL) and probing depth (PD). Multivariable linear regressions were used to explore the association of mean AL and mean PD with SES. A mediation analysis was conducted to examine the impact of mean AL and mean PD on the relationship between SES and cognitive performance. The study included 1,812 participants aged 60 years or older. In the fully adjusted model, SES showed a positive correlation with all three cognitive tests. Meanwhile, mean AL [odds ratio (OR) = 1.61; 95% confidence interval (CI): 1.33 to 1.95] and mean PD (OR = 2.14; 95% CI: 1.54 to 2.96) were inversely related to the DSST scores, accounting for 12.17 and 6.91% of the relationship between SES and DSST, respectively. The mediation effect of periodontal status in this association was significant only in non-HSB participants or in younger participants. SES was negatively associated with periodontal status in older adults in the United States. Furthermore, the link between SES and cognitive performance can be partially explained by periodontal status.

Immune checkpoint molecules, also known as cosignaling molecules, are pivotal cell-surface molecules that control immune cell responses by either promoting (costimulatory molecules) or inhibiting (coinhibitory molecules) a signal. These molecules have been studied for many years. The application of immune checkpoint drugs in the clinic provides hope for cancer patients. Recently, the poliovirus receptor (PVR)-like protein cosignaling network, which involves several immune checkpoint receptors, i.e., DNAM-1 (DNAX accessory molecule-1, CD226), TIGIT (T-cell immunoglobulin (Ig) and immunoreceptor tyrosine-based inhibitory motif (ITIM)), CD96 (T cell activation, increased late expression (TACLILE)), and CD112R (PVRIG), which interact with their ligands CD155 (PVR/Necl-5), CD112 (PVRL2/nectin-2), CD111 (PVRL1/nectin-1), CD113 (PVRL3/nectin-3), and Nectin4, was discovered. As important components of the immune system, natural killer (NK) and T cells play a vital role in eliminating and killing foreign pathogens and abnormal cells in the body. Recently, increasing evidence has suggested that this novel cosignaling network axis costimulates and coinhibits NK and T cell activation to eliminate cancer cells after engaging with ligands, and this activity may be effectively targeted for cancer immunotherapy. In this article, we review recent advances in research on this novel cosignaling network. We also briefly outline the structure of this cosignaling network, the signaling cascades and mechanisms involved after receptors engage with ligands, and how this novel cosignaling network costimulates and coinhibits NK cell and T cell activation for cancer immunotherapy. Additionally, this review comprehensively summarizes the application of this new network in preclinical trials and clinical trials. This review provides a new immunotherapeutic strategy for cancer treatment.