Multiple Datasets Research Articles

Multimodal subspace independent vector analysis effectively captures the latent relationships between brain structure and function.

A key challenge in neuroscience is to understand the structural and functional relationships of the brain from high-dimensional, multimodal neuroimaging data. While conventional multivariate approaches often simplify statistical assumptions and estimate one-dimensional independent sources shared across modalities, the relationships between true latent sources are likely more complex - statistical dependence may exist within and between modalities, and span one or more dimensions. Here we present Multimodal Subspace Independent Vector Analysis (MSIVA), a methodology to capture both joint and unique vector sources from multiple data modalities by defining both cross-modal and unimodal subspaces with variable dimensions. In particular, MSIVA enables flexible estimation of varying-size independent subspaces within modalities and their one-to-one linkage to corresponding subspaces across modalities. As we demonstrate, a main benefit of MSIVA is the ability to capture subject-level variability at the voxel level within independent subspaces, contrasting with the rigidity of traditional methods that share the same independent components across subjects. We compared MSIVA to a unimodal initialization baseline and a multimodal initialization baseline, and evaluated all three approaches with five candidate subspace structures on both synthetic and neuroimaging datasets. We show that MSIVA successfully identified the ground-truth subspace structures in multiple synthetic datasets, while the multimodal baseline failed to detect high-dimensional subspaces. We then demonstrate that MSIVA better detected the latent subspace structure in two large multimodal neuroimaging datasets including structural MRI (sMRI) and functional MRI (fMRI), compared with the unimodal baseline. From subsequent subspace-specific canonical correlation analysis, brain-phenotype prediction, and voxelwise brain-age delta analysis, our findings suggest that the estimated sources from MSIVA with optimal subspace structure are strongly associated with various phenotype variables, including age, sex, schizophrenia, lifestyle factors, and cognitive functions. Further, we identified modality- and group-specific brain regions related to multiple phenotype measures such as age (e.g., cerebellum, precentral gyrus, and cingulate gyrus in sMRI; occipital lobe and superior frontal gyrus in fMRI), sex (e.g., cerebellum in sMRI, frontal lobe in fMRI, and precuneus in both sMRI and fMRI), schizophrenia (e.g., cerebellum, temporal pole, and frontal operculum cortex in sMRI; occipital pole, lingual gyrus, and precuneus in fMRI), shedding light on phenotypic and neuropsychiatric biomarkers of linked brain structure and function.

Short-term heating load forecasting model based on SVMD and improved informer

Accurate heat load forecasting can not only effectively reduce energy consumption, but also improve the efficiency of the heating system and user comfort. In order to improve the accuracy of heat load forecasting, we proposed a forecasting method combining SVMD algorithm and improved Informer model and applied it to the heat load forecasting of district heating system. First, SVMD algorithm is used to decompose the heat load data, and then relative position coding is introduced into Informer model, and causal convolution and jump connection mechanisms are adopted to better capture the dependence of sequence data and enhance the ability of local information extraction. In order to verify the model performance, experiments were conducted on multiple data sets. The results show that the R2 evaluation index of the model reaches 98.7 %, which is 10.8 %, 17.1 %, 6.3 %, 4.9 %, 2.3 %, 1.5 % and 16.6 % higher than SVR, RNN, LSTM, Transformer, SH-Informer, iTransformer and DLinear respectively. The validity of the model is further verified by DM test, which provides a reference for further improving the timeliness of intelligent heating.

Quantum classical hybrid convolutional neural networks for breast cancer diagnosis.

The World Health Organization states that early diagnosis is essential to increasing the cure rate for breast cancer, which poses a danger to women's health worldwide. However, the efficacy and cost limitations of conventional diagnostic techniques increase the possibility of misdiagnosis. In this work, we present a quantum hybrid classical convolutional neural network (QCCNN) based breast cancer diagnosis approach with the goal of utilizing quantum computing's high-dimensional data processing power and parallelism to increase diagnosis efficiency and accuracy. When working with large-scale and complicated datasets, classical convolutional neural network (CNN) and other machine learning techniques generally demand a large amount of computational resources and time. Their restricted capacity for generalization makes it challenging to maintain consistent performance across multiple data sets. To address these issues, this paper adds a quantum convolutional layer to the classical convolutional neural network to take advantage of quantum computing to improve learning efficiency and processing speed. Simulation experiments on three breast cancer datasets, GBSG, SEER and WDBC, validate the robustness and generalization of QCCNN and significantly outperform CNN and logistic regression models in classification accuracy. This study not only provides a novel method for breast cancer diagnosis but also achieves a breakthrough in breast cancer diagnosis and promotes the development of medical diagnostic technology.

A Low Transformed Tubal Rank Tensor Model Using a Spatial-Tubal Constraint for Sample Clustering with Cancer Multi-omics Data

Background: Since each dimension of a tensor can store different types of genomics data, compared to matrix methods, utilizing tensor structure can provide a deeper understanding of multi-dimensional data while also facilitating the discovery of more useful information related to cancer. However, in reality, there are issues such as insufficient utilization of prior knowledge in multi-omics data and limitations in the recovery of low-tubal-rank tensors. Therefore, the method proposed in this article was developed. Objective: In this paper, we proposed a low transformed tubal rank tensor model (LTTRT) using a spatial-tubal constraint to accurately partition different types of cancer samples and provide reliable theoretical support for the identification, diagnosis, and treatment of cancer. Method: In the LTTRT method, the transformed tensor nuclear norm based on the transformed tensor singular value decomposition is characterized by the low-rank tensor, which can explore the global low-rank property of the tensor, resolving the challenge of the tensor nuclear norm-based method not achieving the lowest tubal rank. Additionally, the introduction of weighted total variation regularization is conducive to extracting more information from sequencing data in both spatial and tubal dimensions, exploring cross-correlation features of multiple genomic data, and addressing the problem of overlooking prior knowledge from various perspectives. In addition, the L1-norm is used to improve sparsity. A symmetric Gauss‒Seidel-based alternating direction method of multipliers (sGS-ADMM) is used to update the LTTRT model iteratively. Results: The experiments of sample clustering on multiple integrated cancer multi-omics datasets show that the proposed LTTRT method is better than existing methods. Experimental results validate the effectiveness of LTTRT in accurately partitioning different types of cancer samples. result: The experiments of sample clustering on multiple integrated cancer multi-omics datasets show that the proposed LTTRT method is better than existing methods. Experimental results validate the effectiveness of LTTRT in accurately partitioning different types of cancer samples. Conclusion: The LTTRT method achieves precise segmentation of different types of cancer samples.

Event Camera Denoising Using Asynchronous Spatio-Temporal Event Denoising Neural Network

Abstract. Compared to conventional cameras, event cameras represent a noteworthy advancement in neuromorphic imaging technology, garnering considerable attention from researchers due to their distinct advantages. However, event cameras are susceptible to significant levels of measurement noise, which can detrimentally affect the performance of algorithms reliant on event stream for tasks such as perception and navigation. In this study, we introduce a novel method for denoising event stream, aiming to filter out events that do not accurately reflect genuine logarithmic intensity changes within the observed scene. Our approach focuses on the asynchronous nature and spatiotemporal properties of events, culminating in the development of a novel Asynchronous Spatio- Temporal Event Denoising neural Network(ASTEDNet). This network operates directly on event streams, circumventing the need to convert event stream into denser formats like image frames, thereby preserving their inherent asynchronous nature. Drawing upon principles from graph encoding and temporal convolutional networks, we incorporate spatiotemporal feature attention mechanisms to capture the temporal and spatial correlations between events. This enables the classification of each active event pixel in the original stream as either representing a genuine intensity change or noise. Comparative evaluations conducted on multiple datasets against state-of-the-art methods demonstrate the remarkable efficacy and robustness of our proposed algorithm in noise removal while retaining meaningful event information within the scene.

Comprehensive multi-omics integration uncovers mitochondrial gene signatures for prognosis and personalized therapy in lung adenocarcinoma

The therapeutic efficacy of lung adenocarcinoma (LUAD), the most prevalent histological subtype of primary lung cancer, remains inadequate, with accurate prognostic assessment posing significant challenges. This study sought to elucidate the prognostic significance of mitochondrial-related genes in LUAD through an integrative multi-omics approach, aimed at developing personalized therapeutic strategies. Utilizing transcriptomic and single-cell RNA sequencing (scRNA-seq) data, alongside clinical information from publicly available databases, we first applied dimensionality reduction and clustering techniques to the LUAD single-cell dataset, focusing on the subclassification of fibroblasts, epithelial cells, and T cells. Mitochondrial-related prognostic genes were subsequently identified using TCGA-LUAD data, and LUAD cases were stratified into distinct molecular subtypes through consensus clustering, allowing for the exploration of gene expression profiles and clinical feature distributions across subtypes. By leveraging an ensemble of machine learning algorithms, we developed an Artificial Intelligence-Derived Prognostic Signature (AIDPS) model based on mitochondrial-related genes and validated its prognostic accuracy across multiple independent datasets. The AIDPS model demonstrated robust predictive power for LUAD patient outcomes, revealing significant differences in responses to immunotherapy and chemotherapy, as well as survival outcomes between risk groups. Furthermore, we conducted comprehensive analyses of tumor mutation burden (TMB), immune microenvironment characteristics, and genome-wide association study (GWAS) data, providing additional insights into the mechanistic roles of mitochondrial-related genes in LUAD pathogenesis. This study not only offers a novel approach to improving prognostic assessments in LUAD but also establishes a strong foundation for the development of personalized therapeutic interventions.

MulCPred: Learning Multi-Modal Concepts for Explainable Pedestrian Action Prediction.

Pedestrian action prediction is crucial for many applications such as autonomous driving. However, state-of-the-art methods lack the explainability needed for trustworthy predictions. In this paper, a novel framework called MulCPred is proposed that explains its predictions based on multi-modal concepts represented by training samples. Previous concept-based methods have limitations, including the following: (1) they cannot be directly applied to multi-modal cases; (2) they lack the locality needed to attend to details in the inputs; (3) they are susceptible to mode collapse. These limitations are tackled accordingly through the following approaches: (1) a linear aggregator to integrate the activation results of the concepts into predictions, which associates concepts of different modalities and provides ante hoc explanations of the relevance between the concepts and the predictions; (2) a channel-wise recalibration module that attends to local spatiotemporal regions, which enables the concepts with locality; (3) a feature regularization loss that encourages the concepts to learn diverse patterns. MulCPred is evaluated on multiple datasets and tasks. Both qualitative and quantitative results demonstrate that MulCPred is promising in improving the explainability of pedestrian action prediction without obvious performance degradation. Moreover, by removing unrecognizable concepts, MulCPred shows improved cross-dataset prediction performance, suggesting its potential for further generalization.

HIDIM: A novel framework of network intrusion detection for hierarchical dependency and class imbalance

Deep learning-based network intrusion detection has been extensively explored as a data-driven approach. Therefore, paying attention to the data’s characteristics is essential. By analyzing the attribute dependence and sample distribution of intrusion data, there are the following problems: “hierarchical dependency omission” and “decision boundary discontinuity.” The former means the previous attribute embedding models failed to incorporate network protocol hierarchy. The latter indicates that the small disjuncts distribution leads to sub-concept fragmentation, exacerbating the difficulty in handling class imbalance. To address these problems, we propose a novel detection framework for Hierarchical Dependency and Class Imbalance (HIDIM). First, we treat semantic attributes as words and introduce the protocol hierarchy of attributes into a paragraph embedding model. Second, we design a synthetic oversampling method. It adopts a mutual nearest neighbor approach to determine the boundaries of each disjunct. Then, it synthesizes high-quality samples within those boundary areas by crossing or mutating features based on their importance. The experimental results on multiple real-world datasets demonstrate that the proposed framework is superior to other state-of-the-art models in terms of accuracy, F1-score, and false negative rate by 2.23%, 2.12%, and 1.43% on average, respectively.

Transfer-learning enabled micro-expression recognition using dense connections and mixed attention

Micro-expression recognition (MER) is a challenging computer vision problem, where the limited amount of available training data and insufficient intensity of the facial expressions are among the main issues adversely affecting the performance of existing recognition models. To address these challenges, this paper explores a transfer–learning enabled MER model using a densely connected feature extraction module with mixed attention. Unlike previous works that utilize transfer learning to facilitate MER and extract local facial-expression information, our model relies on pretraining with three diverse macro-expression datasets and, as a result, can: (i) overcome the problem of insufficient sample size and limited training data availability, (ii) leverage (related) domain-specific information from multiple datasets with diverse characteristics, and (iii) improve the model adaptability to complex scenes. Furthermore, to enhance the intensity of the micro-expressions and improve the discriminability of the extracted features, the Euler video magnification (EVM) method is adopted in the preprocessing stage and then used jointly with a densely connected feature extraction module and a mixed attention mechanism to derive expressive feature representations for the classification procedure. The proposed feature extraction mechanism not only guarantees the integrity of the extracted features but also efficiently captures local texture cues by aggregating the most salient information from the generated feature maps, which is key for the MER task. The experimental results on multiple datasets demonstrate the robustness and effectiveness of our model compared to the state-of-the-art.

Integrative multi-omics analysis reveals the role of tumor-associated endothelial cells and their signature in prognosis of intrahepatic cholangiocarcinoma

This study aims to investigate the interplay between tumor-associated endothelial cells (TECs) and immune cells within the tumor microenvironment (TME) and its impact on tumor prognosis. We conducted single-cell RNA sequencing (scRNA-seq) of tumor, normal, and lymph node tissues obtained from intrahepatic cholangiocarcinoma (ICC) patients to reveal the role of TECs in tumor angiogenesis and their significant heterogeneity. Meanwhile, we identified genes highly expressed in TECs and constructed TEC signatures (TEC.Sig). Next, we calculated TEC scores of samples based on TEC.Sig. Patients with higher TEC scores exhibited a higher frequency of KRAS mutations, which was associated with increased infiltration of neutrophils and immature dendritic cells (iDCs), and decreased numbers of natural killer (NK), CD4 + T, and CD8 + T effector memory (Tem) cells, indicating an inflammation-dominated immunosuppressive phenotype. In contrast, BAP1 mutations and CXCL12 overexpression showed a contrasting trend. Spatial transcriptomics analysis and histological experiments further confirmed that TECs interacted with various tumor-killing immune cells through the CXCL12/CXCR4 axis. Multiple tumor immunotherapy datasets confirmed that the TEC.Sig could predict patient responses to immunotherapy. The TEC score is a promising and reliable biomarker for predicting genetic mutations and prognosis in ICC patients. Enhancing the regulation of the CXCL12/CXCR4 signaling pathway may represent a potential novel therapeutic target for ICC treatment.

The Late Bronze–Early Iron Age transition in Canaan: new insights from Bayesian modelling of radiocarbon dates

Scholars have used the emergence of Philistine material culture and the destruction of Canaanite palatial centres as key anchors to archaeologically define the transition from the Late Bronze to the Early Iron Age in Canaan. They have not always, however, agreed on the absolute chronology of these events and its relationship with the historical record. Various hypotheses have been put forward, placing them before, during, or after, the collapse of Egyptian rule and its withdrawal from the region, potentially around 1170–1130 BC. Traditional ‘historicist’ approaches to establishing a chronology for the theoretically linked archaeological layers, have often relied on the assumption that ceramic styles, such as diagnostic artefacts, developed and declined uniformly across different sites. This synchronicity was presumed to mirror historical events as recorded in textual sources — arrival of ‘Sea Peoples’ and the palatial system’s violent collapse. Such a perspective may, however, over simplify the complex regional variations and temporal nuances inherent in the archaeological data. Analysis of multiple radiocarbon datasets from Levantine sites reveals that destructive events and shifts in material culture were not synchronous across the region. Bayesian statistical modelling of these datasets indicates that the timing of such occurrences was influenced by unique local factors, rather than following a uniform regional pattern. The absence of Late Helladic IIIC Aegean-like wares at many sites indicates a geographically restricted production and distribution. Datasets from places where such ware appears reflect the likelihood that their production and exchange occurred from the 13th century to c. 1050 BC. Conversely, the majority of destructive events at key Levantine centres appear to be concentrated within a narrower timeframe, c. 1150–1100 BC. That is, although manifesting at different times and to different degrees of intensity across the region, Philistine presence in the Levant predated most of the Late Bronze Age III destruction events.

Visible light infrared matching algorithm based on improved SuperPoint and linear converter

In order to solve the problems of high difficulty and mismatch rate in heterogeneous image matching of visible light and infrared images, a deep learning matching algorithm based on improved SuperPoint and linear transformer is proposed. The algorithm first introduces the idea of feature pyramid to construct a feature description branch based on the SuperPoint network structure, and trains it based on the hinge loss function, so as to better learn the multi-scale deep features of visible light and infrared images and increase the similarity of image point pair descriptors with the same name; in the feature matching module, the linear transformer is used to improve the SuperGlue matching algorithm and aggregate features to improve matching performance. The proposed algorithm is experimentally verified on multiple data sets. The results show that compared with the existing algorithms, the algorithm achieves better matching results and improves the matching accuracy.

Graph attention automatic encoder based on contrastive learning for domain recognition of spatial transcriptomics

Spatial transcriptomics is an emerging technology that enables the profiling of gene expression in tissues while preserving spatial location information. This innovative approach is anticipated to provide a comprehensive understanding of the spatial distribution of different cells within tissues and facilitate in-depth analysis of tissue structure. To accurately recognize spatial domains from spatial transcriptomics, we have introduced a generalized deep learning method called GAAEST (Graph Attention-based Autoencoder for Spatial Transcriptomics). Our proposed approach effectively integrates both spatial location information and gene expression data from spatial transcriptomics. Specifically, it leverages spatial location details to construct a neighborhood graph and employs a graph attention network-based encoder to embed gene expression information into a spatially informed space. At the same time, to further optimize the learned potential embedding, self-supervised contrastive learning is introduced to capture spatial information at three levels: local, global and contextual feature of spots. Finally, the decoder reconstructs gene expressions, which are then clustered to identify spatial domains with similar expression patterns and spatial proximity. Based on our experiments conducted on multiple datasets, GAAEST consistently outperforms existing state-of-the-art methods. The proposed GAAEST demonstrates excellent capabilities in spatial domain recognition, positioning it as an ideal tool for advancing spatial transcriptomics research.

Improving the industrial defect recognition in radiographic testing by pre-training on medical radiographs

Deep learning methodologies have gained substantial traction for defect recognition in industrial radiographic testing including welds, castings and other fields. Regardless of the deep learning utilized, it has emerged as a standard configuration to use a model pre-trained from ImageNet to accelerate convergence and enhance recognition accuracy. However, there is a significant gap between the domain of natural images and industrial radiographs, raising the question of whether there might be a superior pre-training method than relying on ImageNet pre-training. Fortunately, medical radiographs are more similar to industrial radiographs than natural images because of the same imaging method. In this paper, we initially utilize numerous medical radiographic images from CheXpert dataset to train a pre-trained CNN model. Then, we apply this model to four distinct tasks within two radiographic testing scenarios to validate its advantages and generalization capabilities. Finally, experiments on multiple datasets indicate that our method brings more benefits than ImageNet pre-training or training from scratch, with a F1 score improvement of 3.41 %–13.72 % for defect classification and a mIoU improvement of 1.05 %–6.58 % for defect segmentation. It demonstrates that pre-training from medical radiographs provides a cost-free improvement for all kinds of tasks in industrial defect recognition.

Association of Racial Residential Segregation and Other Social Determinants of Health with HIV Late Presentation.

Understanding social determinants of HIV late presentation with advanced disease (LPWA) beyond individual-level factors could help decrease LPWA and improve population-level HIV outcomes. This study aimed to examine county-level social determinants of health (SDOH) with HIV late presentation. We aggregated datasets for analysis by linking statewide HIV diagnosis data from the South Carolina (SC) Enhanced HIV/AIDS Reporting System and multiple social contextual datasets (e.g., the American Community Survey). All adult (18 years and older) people with HIV diagnosed from 2014 to 2019 in SC were included. Linear mixed models with forward selection were employed to explore the association of county-level SDOH with the county-level three-year moving average percentage of LPWA and average delay time from HIV infection to diagnosis. Around 30% of new HIV diagnoses were LPWA in SC, and the mean delay time for people with LPWA was approximately 13 years. Counties with more racial residential segregation had longer average delay time (Adjusted beta = 5.079, 95% CI: 0.268 ~ 9.889). Regarding other SDOH, the increased percentage of LPWA was associated with fewer Ryan White centers per 100,000 population (Adjusted beta = -0.006, 95% CI: -0.011~-0.001) and higher percentages of the population with less than a high school education (Adjusted beta = 0.008, 95% CI: 0 ~ 0.015). Reducing county-level disparities in LPWA requires multifaceted interventions addressing multiple dimensions of SDOH. Targeted interventions are needed for counties with more Black residential segregation, fewer Ryan White centers, and higher percentages of less than high school education.

High-temperature stress in strawberry: understanding physiological, biochemical and molecular responses.

Heat stress reduces strawberry growth and fruit quality by impairing photosynthesis, disrupting hormone regulation, and altering mineral nutrition. Multi-omics studies show extensive transcriptional, post-transcriptional, proteomic and metabolomic under high temperatures. Garden strawberry is a globally cultivated, economically important fruit crop highly susceptible to episodic heat waves and chronically rising temperatures associated with climate change. Heat stress negatively affects the growth, development, and quality of strawberries. Elevated temperatures affect photosynthesis, respiration, water balance, hormone signaling, and carbohydrate metabolism in strawberries. Heat stress reduces the size and number of leaves, the number of crowns, the differentiation of flower buds, and the viability of pollen and fruit set, ultimately leading to a lower yield. On a physiological level, heat stress reduces membrane stability, increases the production of reactive oxygen species, and reduces the antioxidant capacity of strawberries. Heat-tolerant varieties have better physiological and biochemical adaptation mechanisms compared to heat-sensitive varieties. Breeding heat-tolerant strawberry cultivars involves selection for traits such as increased leaf temperature, membrane thermostability, and chlorophyll content. Multi-omics studies show extensive transcriptional, post-transcriptional, proteomic, metabolomic, and ionomic reprogramming at high temperatures. Integrative-omics approaches combine multiple omics datasets to obtain a systemic understanding of the responses to heat stress in strawberries. This article summarizes the deciphering of strawberry responses to heat stress using physiological, biochemical, and molecular approaches that will enable the development of resilient adaptation strategies that sustain strawberry production under global climate change.

Border Gateway Protocol Route Leak Detection Technique Based on Graph Features and Machine Learning

In the Internet, ASs are interconnected using BGP. However, due to a lack of security considerations in the design of BGP, a series of security issues arise during the propagation of routing information, such as prefix hijacking, route leakage, and AS path tampering. Therefore, this paper conducts research on the detection of route leakage. By analyzing BGP routing information, we abstract the routing propagation relationship between ASs into a network topology graph, and extract graph features from the graph abstracted from routing data at certain time intervals. Based on the structural robustness features and centrality measurement features of the graph, we determine whether a route leakage has occurred during the current time period. To this end, we use machine learning methods and propose a weighted voting model. This model trains multiple single models and assigns weights to them, and through the weighted analysis of the results of multiple models, it can determine whether a route leakage has occurred. In addition, to determine the corresponding weights, we use genetic algorithms for identifying route leaks. The experimental results show that the method used in this paper has a high accuracy rate, and compared with a single model, it performs better on multiple datasets.

Integrating bulk and single-cell transcriptomics to elucidate the role and potential mechanisms of autophagy in aging tissue.

Autophagy is frequently observed in tissues during the aging process, yet the tissues most strongly correlated with autophagy during aging and the underlying regulatory mechanisms remain inadequately understood. The purpose of this study is to identify the tissues with the highest correlation between autophagy and aging, and to explore the functions and mechanisms of autophagy in the aging tissue microenvironment. Integrated bulk RNA-seq from over 7000 normal tissue samples, single-cell sequencing data from blood samples of different ages, more than 2000 acute myeloid leukemia (AML) bulk RNA-seq, and multiple sets of AML single-cell data. The datasets were analysed using various bioinformatic approaches. Blood tissue exhibited the highest positive correlation between autophagy and aging among healthy tissues. Single-cell resolution analysis revealed that in aged blood, classical monocytes (C. monocytes) are most closely associated with elevated autophagy levels. Increased autophagy in these monocytes correlated with a higher proportion of C. monocytes, with hypoxia identified as a crucial contributing factor. In AML, a representative myeloid blood disease, enhanced autophagy was accompanied by an increased proportionof C. monocytes. High autophagy levels in monocytes are associated with pro-inflammatory gene upregulation and Reactive Oxygen Species (ROS) accumulation, contributing to tissue aging. This study revealed that autophagy is most strongly correlated with aging in blood tissue. Enhanced autophagy levels in C. monocytes demonstrate a positive correlation with increased secretion of pro-inflammatory factors and elevated production of ROS, which may contribute to a more rapid aging process. This discovery underscores the critical role of autophagy in blood aging and suggests potential therapeutic targets to mitigate aging-related health issues.

Comprehensive analysis of lactylation-related gene sets and mitochondrial functions in gastric adenocarcinoma: implications for prognosis and therapeutic strategies.

Gastric adenocarcinoma (STAD) is characterized by high heterogeneity and aggressiveness, leading to poor prognostic outcomes worldwide. This study explored the prognostic significance of lactylation-related gene sets and mitochondrial functions in STAD by integrating large-scale genomic datasets, including TCGA and several GEO datasets. We utilized Spatial transcriptomics and single-cell RNA sequencing to delineate the tumor microenvironment and assess the heterogeneity of cellular responses within the tumor. Additionally, the study identified distinct molecular subtypes within STAD that correspond with unique survival outcomes and immune profiles, enhancing the molecular classification beyond current paradigms. Prognostic models incorporating these molecular markers demonstrated superior predictive capabilities over existing models across multiple validation datasets. Furthermore, our analysis of immune landscapes revealed that variations in lactylation could influence immune cell infiltration and responsiveness, pointing towards novel avenues for tailored immunotherapy approaches. These comprehensive insights provide a foundation for targeted therapeutic strategies and underscore the potential of metabolic and immune modulation in improving STAD treatment outcomes.

Tri-training algorithm based nuclear power systems semi-supervised fault diagnosis under multiple restricted data conditions

An improved tri-training semi-supervised classification method was proposed as a solution to the restricted data conditions of supervised classification models relying on labeled data, semi-supervised models not considering imbalanced proportion of faults data and the presence of faults data with similar feature or varying severity in existing data-driven nuclear power system fault diagnosis studies. Based on the structure of three identical sub-classifiers in the original tri-training algorithm, data update paths have been added, the updated data selection has been strengthened, and the initial information and permissions of sub-models have been differentiated. The novel method only requires the dataset to meet the Smoothness Assumption, and through multiple strict data update paths, it improves the utilization of unlabeled training data while reducing Pseudo-labeling process errors. The difference in permissions and initial information between sub-classifiers is utilized to improve the training rigor for fault types with smaller sample sizes, in order to alleviate the problem of imbalanced data. Based on a marine nuclear power system secondary circuit model and a widely recognized nuclear power plant dataset, multiple nuclear power system common restricted fault diagnosis datasets faced in ships and power plants were obtained to verify the novel method's advantages and generalization. The conclusion is that under the same conditions, compared with the original tri-training method and other semi-supervised learning methods, the Accuracy, AUC, Precision Rate and Recall Rate of the novel method have improved by about 21 %, 10 %, 20 %, and 21 %, respectively, and can reduce about 25 % of misjudgments between similar feature faults.