Liver is prone to viral infection.Viral hepatitis can be roughly divided into hepatitis A,B,C,D and E.Accurate diagnosis of viral hepatitis is crucial for accurate treatments.Different types of biomarkers,including no...Liver is prone to viral infection.Viral hepatitis can be roughly divided into hepatitis A,B,C,D and E.Accurate diagnosis of viral hepatitis is crucial for accurate treatments.Different types of biomarkers,including non-invasive biomarkers have been explored for the diagnosis of viral hepatitis.With the fast development of multi-omics technology,non-invasive biomarkers can be detected from blood,saliva,urine,stool,and other body fluids.The advantages of non-invasive biomarkers are:1)non-invasive;2)convenient to test and 3)repeatable.The application of non-invasive biomarkers significantly improves the diagnostic accuracy of viral hepatitis.The non-invasive biomarkers can be sugars,proteins,nucleic acids,and even microorganisms.In this review,we summarized recent advances in identifying non-invasive biomarkers using multi-omics technology and discussed their potential diagnostic values for viral hepatitis.展开更多
Background:As a heterogeneous disease,breast cancer requires refined classification frameworks that can effectively guide targeted therapies.However,traditional methods fail to capture the comprehensive molecular insi...Background:As a heterogeneous disease,breast cancer requires refined classification frameworks that can effectively guide targeted therapies.However,traditional methods fail to capture the comprehensive molecular insights needed for this purpose.Methods:To comprehensively capture breast cancer heterogeneity,we employed integrative clustering that incorporates six molecular features from 670 breast cancer samples.Ten distinct clustering algorithms were combined to ensure robust subtype identification,and the identified subtypes were validated in four independent datasets.Subsequently,we constructed a survival support vector machine prognostic model based on key molecular features to enhance survival prediction and clinical applicability.Results:Five novel subtypes were identified:consensus subtypes 1–5(CS1–CS5).CS2 was an aggressive subtype with elevated TP53 mutation rates,high tumor mutational burden,and strong sensitivity to YM-155 and ispinesib.Conversely,CS5 exhibited stable genomics with enhanced nucleotide excision repair and favorable prognoses.CS2 and CS4 showed enriched immune checkpoint expression,indicating potential immunotherapy responsiveness,while CS1 and CS5 exhibited immune-cold profiles.The survival support vector machine model effectively predicted survival outcomes across independent datasets.Conclusions:The refined breast cancer classification framework developed in this research uncovers new insights into molecular heterogeneity,enhances risk stratification,and enables the identification of promising therapeutic targets.The potential of this framework to optimize personalized treatment strategies warrants further clinical validation.展开更多
Bamei pigs,an indigenous Chinese breed,yield meat with a delectable flavor and boast higher carcass fat content compared to commercial breeds,making them a rich food source for humans.However,the differences in lipid ...Bamei pigs,an indigenous Chinese breed,yield meat with a delectable flavor and boast higher carcass fat content compared to commercial breeds,making them a rich food source for humans.However,the differences in lipid and nutrient components between the adipose tissue of Bamei pigs and commercial pigs are still unclear.The study employed UPLC-MS/MS to quantify the composition of lipids and metabolites in the backfat of both Bamei and Large White pigs.A total of 428 lipids and 193 metabolites were significantly different between the 2 groups.Specifically,Bamei pig backfat exhibited altered levels of various lipids and metabolites that may potentially contribute to nutritional and flavor differences,including unsaturated triglycerides,free fatty acids,medium-chain triglycerides,essential amino acids,vitamins and antioxidants,while maintaining reduced cholesterol levels.Furthermore,we delved into the molecular mechanisms underlying these nutritional differences by analyzing significantly different 431 m RNAs and 865 proteins and integrating the regulatory network of protein-metabolite-lipid pathway.Importantly,in the pyruvate metabolic pathway of Bamei pigs,the bioprocess of lactate production was inhibited but the acetyl-Co A production was activated,suggesting the possibility that energy allocation favors the biogenesis of lipid precursors.These findings may contribute to guiding industrial food producers in enhancing the quality of lard at the genetic and molecular levels.展开更多
Background: Cancer-associated fibroblasts (CAFs) play critical roles in tumor progression and immunosuppression;however, their contribution to the functional classification and personalized treatment of gastric cancer...Background: Cancer-associated fibroblasts (CAFs) play critical roles in tumor progression and immunosuppression;however, their contribution to the functional classification and personalized treatment of gastric cancerremains poorly defined. This study aimed to identify effective therapeutic targets to facilitate individualized treatmentstrategies for patients with gastric cancer. Methods: Single-cell and bulk transcriptomic analyses were integrated tocharacterize gastric cancer fibroblasts. “Seurat”, “Slingshot”, and “CellChat” were used for dimensionality reduction,trajectory inference, and cell-cell communication analyses, respectively. Key metastasis-associated fibroblast moduleswere identified using High-dimensional weighted gene co-expression network analysis (hdWGCNA) to construct aprognostic model, which was further evaluated for immune infiltration, therapeutic response, and mutational features.The expression and function of the core gene tripeptidyl peptidase 1 (TPP1) were validated through immunoblotting, PCR, and functional assays. Results: Eight fibroblast subpopulations associated with gastric cancer metastasisexhibited distinct differentiation trajectories and transcriptional heterogeneity. Prognostic analysis indicated thatmetastasis-associated fibroblasts correlated with poor clinical outcomes. The high-risk subgroup showed markedimmunosuppression, resistance to immunotherapy, and reduced mutational burden, with tumor progression-relatedpathways significantly enriched in this group. In vitro experiments further confirmed that TPP1 knockdown suppressedgastric cancer cell metastasis, invasion, and clonogenic capacity while inducing apoptosis. Conclusion: This studycharacterized the heterogeneity of gastric cancer-associated fibroblasts using single-cell transcriptomic analysis andestablished a prognostic model based on metastasis-related fibroblast markers. The model demonstrated strongpredictive performance for patient prognosis, immune landscape, and immunotherapy response. Furthermore, thefindings highlighted the pivotal role of TPP1 in gastric cancer progression and its potential as a therapeutic target.展开更多
Background:Giant cell arteritis(GCA),the most common systemic vasculitis affecting elderly individuals,currently lacks specific therapies.This study aimed to systematically identify therapeutic targets for GCA through...Background:Giant cell arteritis(GCA),the most common systemic vasculitis affecting elderly individuals,currently lacks specific therapies.This study aimed to systematically identify therapeutic targets for GCA through integration of large-scale multi-omics datasets.Methods:We constructed a multi-stage analytical framework encompassing 32 proteomic datasets(covering 2914 unique plasma proteins)and 6 transcriptomic datasets.Multi-omics integration strategies,including two-sample Mendelian randomization,colocalization analysis,and functional enrichment analysis,were employed to identify and validate causal relationships between candidate targets and GCA risk across 4 independent European-ancestry GCA cohorts.Single-cell RNA sequencing analysis of peripheral blood mononuclear cells from untreated GCA patients was performed to characterize hub gene-immune cell relationships.Results:We identified 43 plasma proteins causally associated with GCA[false discovery rate(FDR)<0.05],with 17 representing novel therapeutic targets.Through dual validation using proteome-wide association studies and transcriptome-wide association studies,we identified 13 high-confidence candidate targets with distinct tissue-specific expression patterns.Unc-51 like kinase 3(ULK3)emerged as the strongest protective factor(odds ratio=0.47,95%confidence interval:0.37–0.71)through autophagy regulation,while SLAMF7 represents an immediate drug repositioning opportunity as the target of food and drug administration-approved elotuzumab.Five targets have existing approved drugs(SLAMF7,ICAM1,IL18,IL6ST,CTSS).Single-cell analysis revealed profound disruption of hub gene-immune cell relationships in untreated GCA patients,with cell-type-specific alterations in inflammatory gene expression,and TYMP as the most critical hub gene.Conclusions:This study provides a clinically-actionable atlas of 43 potential therapeutic targets in GCA,identifying novel mechanisms including autophagy modulation and metabolic reprogramming,with immediate drug repositioning opportunities and precision medicine strategies based on tissue-specific and cell-type-specific expression patterns.These findings require experimental validation before clinical translation.展开更多
Understanding the molecular responses of tea leaves to mechanical stress is crucial for elucidating the mechanisms of post-harvest quality formation during oolong tea processing.This study employed an integrated multi...Understanding the molecular responses of tea leaves to mechanical stress is crucial for elucidating the mechanisms of post-harvest quality formation during oolong tea processing.This study employed an integrated multi-omics strategy to characterize the changes and interactions among metabolomic(MB),transcriptomic(TX),and proteomic(PT)profiles in mechanically stressed tea leaves.Mechanical stress initially activated damage-associated molecular patterns(DAMPs),including Ca^(2+)signaling,jasmonic acid signaling,and glutathione metabolism pathways.These processes subsequently induced quality-related metabolic pathways(QRMPs),particularly α-linolenic acid and phenylalanine metabolism.Upregulated expression of LOX,ADH1,and PAR genes,together with the increased abundance of their encoded proteins,respectively promoted the accumulation of jasmine lactone,benzyl alcohol,and 2-phenylethanol.These findings indicate that mechanical stress influences the metabolite biosynthesis in tea leaves through coordinated molecular responses.This study provides new insights into the molecular mechanisms underlying tea leaf responses to mechanical stress and a foundation for future investigations into how early molecular events may contribute to post-harvest metabolic changes during oolong tea processing.展开更多
The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities...The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.展开更多
AIM:To investigate the clinical features and prognosis of patients with orbital inflammatory myofibroblastic tumor(IMT).METHODS:This retrospective study collected clinical data from 22 patients diagnosed with orbital ...AIM:To investigate the clinical features and prognosis of patients with orbital inflammatory myofibroblastic tumor(IMT).METHODS:This retrospective study collected clinical data from 22 patients diagnosed with orbital IMT based on histopathological examination.The patients were followed up to assess their prognosis.Clinical data from patients,including age,gender,course of disease,past medical history,primary symptoms,ophthalmologic examination findings,general condition,as well as imaging,laboratory,histopathological,and immunohistochemical results from digital records were collected.Orbital magnetic resonance imaging(MRI)and(or)computed tomography(CT)scans were performed to assess bone destruction of the mass,invasion of surrounding tissues,and any inflammatory changes in periorbital areas.RESULTS:The mean age of patients with orbital IMT was 28.24±3.30y,with a male-to-female ratio of 1.2:1.Main clinical manifestations were proptosis,blurred vision,palpable mass,and pain.Bone destruction and surrounding tissue invasion occurred in 72.73%and 54.55%of cases,respectively.Inflammatory changes in the periorbital site were observed in 77.27%of the patients.Hematoxylin and eosin staining showed proliferation of fibroblasts and myofibroblasts,accompanied by infiltration of lymphocytes and plasma cells.Immunohistochemical staining revealed that smooth muscle actin(SMA)and vimentin were positive in 100%of cases,while anaplastic lymphoma kinase(ALK)showed positivity in 47.37%.The recurrence rate of orbital IMT was 27.27%,and sarcomatous degeneration could occur.There were no significant correlations between recurrence and factors such as age,gender,laterality,duration of the disease,periorbital tissue invasion,bone destruction,periorbital inflammation,tumor size,fever,leukocytosis,or treatment(P>0.05).However,lymphadenopathy and a Ki-67 index of 10%or higher may be risk factors for recurrence(P=0.046;P=0.023).CONCLUSION:Orbital IMT is a locally invasive disease that may recur or lead to sarcomatoid degeneration,primarily affecting young and middle-aged patients.The presence of lymphadenopathy and a Ki-67 index of 10%or higher may signify a poor prognosis.展开更多
The detection of steel surface anomalies has become an industrial challenge due to variations in production equipment,processes,and characteristics.To alleviate the problem,this paper proposes a detection and localiza...The detection of steel surface anomalies has become an industrial challenge due to variations in production equipment,processes,and characteristics.To alleviate the problem,this paper proposes a detection and localization method combining 3D depth and 2D RGB features.The framework comprises three stages:defect classification,defect location,an d warpage judgment.The first stage uses a dataefficient image Transformer model,the second stage utilizes reverse knowledge distillation,and the third stage performs feature fusion using3D depth and 2D RGB features.Experimental results show that the proposed algorithm achieves relatively high accuracy and feasibility,and can be effectively used in industrial scenarios.展开更多
Objective To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine(TCM)with machine learning algorithms.The proposed model seeks to establish ...Objective To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine(TCM)with machine learning algorithms.The proposed model seeks to establish a TCM-informed tool for early depression screening,thereby bridging traditional diagnostic principles with modern computational approaches.Methods The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1,2022 to October 1,2023,as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group.Videos of 3–10 s were captured using a Xiaomi Pad 5,and the TCM spirit and expressions were determined by TCM experts(at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions).Basic information,facial images,and interview information were collected through a portable TCM intelligent analysis and diagnosis device,and facial diagnosis features were extracted using the Open CV computer vision library technology.Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data,TCM spirit and expression features,and facial diagnosis feature parameters of the two groups,to compare the differences in TCM spirit and expression and facial features.Five machine learning algorithms,including extreme gradient boosting(XGBoost),decision tree(DT),Bernoulli naive Bayes(BernoulliNB),support vector machine(SVM),and k-nearest neighbor(KNN)classification,were used to construct a depression recognition model based on the fusion of TCM spirit and expression features.The performance of the model was evaluated using metrics such as accuracy,precision,and the area under the receiver operating characteristic(ROC)curve(AUC).The model results were explained using the Shapley Additive exPlanations(SHAP).Results A total of 93 depression patients and 87 healthy individuals were ultimately included in this study.There was no statistically significant difference in the baseline characteristics between the two groups(P>0.05).The differences in the characteristics of the spirit and expressions in TCM and facial features between the two groups were shown as follows.(i)Quantispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls(P<0.05),with characteristic features such as sad expressions,facial erythema,and changes in the lip color ranging from erythematous to cyanotic.(ii)Depressed patients exhibited significantly lower values in facial complexion L,lip L,and a values,and gloss index,but higher values in facial complexion a and b,lip b,low gloss index,and matte index(all P<0.05).(iii)The results of multiple models show that the XGBoost-based depression recognition model,integrating the TCM“spirit-expression”diagnostic framework,achieved an accuracy of 98.61%and significantly outperformed four benchmark algorithms—DT,BernoulliNB,SVM,and KNN(P<0.01).(iv)The SHAP visualization results show that in the recognition model constructed by the XGBoost algorithm,the complexion b value,categories of facial spirit,high gloss index,low gloss index,categories of facial expression and texture features have significant contribution to the model.Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic features with machine learning enables the construction of a high-precision depression detection model,offering a novel paradigm for objective depression diagnosis.展开更多
BACKGROUND SMARCB1/INI1-deficient pancreatic undifferentiated rhabdoid carcinoma is a highly aggressive tumor,and spontaneous splenic rupture(SSR)as its presenting manifestation is rarely reported among pancreatic mal...BACKGROUND SMARCB1/INI1-deficient pancreatic undifferentiated rhabdoid carcinoma is a highly aggressive tumor,and spontaneous splenic rupture(SSR)as its presenting manifestation is rarely reported among pancreatic malignancies.CASE SUMMARY We herein report a rare case of a 59-year-old female who presented with acute left upper quadrant abdominal pain without any history of trauma.Abdominal imaging demonstrated a heterogeneous splenic lesion with hemoperitoneum,raising clinical suspicion of SSR.Emergency laparotomy revealed a pancreatic tumor invading the spleen and left kidney,with associated splenic rupture and dense adhesions,necessitating en bloc resection of the distal pancreas,spleen,and left kidney.Histopathology revealed a biphasic malignancy composed of moderately differentiated pancreatic ductal adenocarcinoma and an undifferentiated carcinoma with rhabdoid morphology and loss of SMARCB1 expression.Immunohistochemical analysis confirmed complete loss of SMARCB1/INI1 in the undifferentiated component,along with a high Ki-67 index(approximately 80%)and CD10 positivity.The ductal adenocarcinoma component retained SMARCB1/INI1 expression and was positive for CK7 and CK-pan.Transitional zones between the two tumor components suggested progressive dedifferentiation and underlying genomic instability.The patient received adjuvant chemotherapy with gemcitabine and nab-paclitaxel and maintained a satisfactory quality of life at the 6-month follow-up.CONCLUSION This study reports a rare case of SMARCB1/INI1-deficient undifferentiated rhabdoid carcinoma of the pancreas combined with ductal adenocarcinoma,presenting as SSR-an exceptionally uncommon initial manifestation of pancreatic malignancy.展开更多
Because the pathogenesis of Alzheimer’s disease is multifactorial and complex,integrated multi-level omics analysis is essential to comprehensively elucidate its molecular alterations.We therefore utilized the well-e...Because the pathogenesis of Alzheimer’s disease is multifactorial and complex,integrated multi-level omics analysis is essential to comprehensively elucidate its molecular alterations.We therefore utilized the well-established amyloid precursor protein/presenilin 1 mouse model to carry out an integrated multi-omics study using transcriptomic,proteomic,N^(6)-methyladenosine epitranscriptomic,and phosphoproteomic analyses.The results revealed substantial molecular alterations across multiple biological dimensions and the alteration in the expression of several key genes,such as GFAP,APP,and RTN4,in a mouse model of Alzheimer’s disease.The pronounced elevation of RTN4 in reactive astrocytes is indicative of its involvement in Alzheimer’s disease pathogenesis.Furthermore,we identified dysregulation of pathways related to endocytosis,highlighting the critical role of this process in disease progression.Our findings underscore the significant impact of post-transcriptional(N^(6)-methyladenosine methylation)and post-translational(phosphorylation)protein modifications,which have been underrepresented in Alzheimer’s disease research.The significant contribution made by this study is the integrated,multi-level omics analysis that we carried out to investigate the complex biological changes that occur in Alzheimer’s disease.Our findings provide novel insights into Alzheimer’s disease pathogenesis and suggest potential therapeutic targets,such as RTN4.展开更多
This study proposes a multimodal deep learning framework for joint prediction of the state of health(SOH)and remaining useful life(RUL)of lithium-ion batteries.Twelve representative impedance features-covering charge-...This study proposes a multimodal deep learning framework for joint prediction of the state of health(SOH)and remaining useful life(RUL)of lithium-ion batteries.Twelve representative impedance features-covering charge-transfer resistance,solid electrolyte interface(SEI)layer impedance,and ion diffusion-are extracted from electrochemical impedance spectroscopy(EIS)and combined with short voltage/current segments to form a compact,interpretable feature set.A residual multi-layer perceptron(ResMLP)is employed for SOH regression,and a temporal convolutional network with attention(TCNAttention)is used for RUL estimation.Lifetime experiments on two battery types with different chemistries and form factors,evaluated through three rounds of paired cross-validation,validate the approach.Results show that the proposed features significantly reduce dimensionality and computational cost while substantially lowering SOH error,achieving an average normalized root mean square error of 2.3%.The RUL prediction reaches an average error of 14.8%.Overall,the framework balances interpretability,robustness,and feasibility,providing a practical solution for battery management systems(BMS)monitoring and life prediction.展开更多
Improved delay detached eddy simulation is performed to explore the flow features and aero-optical effects of turrets with different bottom cylinder height at a freestream Mach number Ma=0.7.Analysis of both the time-...Improved delay detached eddy simulation is performed to explore the flow features and aero-optical effects of turrets with different bottom cylinder height at a freestream Mach number Ma=0.7.Analysis of both the time-averaged and instantaneous flow features demonstrate that the shock motion causes the oscillation of separated shear layer.In flow analysis,two unsteady shock-wake-correlated modes are discerned:the asymmetric shifting mode and the symmetric breathing mode.With the increase of cylinder height,the relative energy of shock gradually increases,which goes from 26%to 59%.The proper orthogonal decomposition analysis yields the single frequency peak for the two dominant modes.The frequency peaks of shifting mode are generally at StD<0.23,while the frequency peaks of breathing mode are generally at StD>0.26.The dynamic mode decomposition analysis gives range of frequency peak.The frequency peaks of shifting mode are in the range of StD=0.11-0.23,and the frequency peaks of breathing mode are in range of StD=0.26-0.41.Optical distortion analysis indicates that the distortion calculated in five cases is linked to the breathing mode.When the beam passes through the turbulent wake,it exhibits the high-frequency and high-amplitude characteristics.展开更多
Hard disk drives(HDDs)serve as the primary storage devices in modern data centers.Once a failure occurs,it often leads to severe data loss,significantly degrading the reliability of storage systems.Numerous studies ha...Hard disk drives(HDDs)serve as the primary storage devices in modern data centers.Once a failure occurs,it often leads to severe data loss,significantly degrading the reliability of storage systems.Numerous studies have proposed machine learning-based HDD failure prediction models.However,the Self-Monitoring,Analysis,and Reporting Technology(SMART)attributes differ across HDD manufacturers.We define hard drives of the same brand and model as homogeneous HDD groups,and those from different brands or models as heterogeneous HDD groups.In practical engineering scenarios,a data center is often composed of a heterogeneous population of HDDs,spanning multiple vendors and models.Existing research predominantly focuses on homogeneous datasets,ignoring the model’s generalization capability across heterogeneous HDDs.As a result,HDD models with limited samples often suffer from poor training effectiveness and prediction performance.To address this issue,we investigate generalizable SMART predictors across heterogeneous HDD groups.By extracting time-series features within a fixed sliding time window,we propose a Heterogeneous Disk Failure Prediction Method based on Time Series Features(HDFPM)framework.This method is adaptable to HDD models with limited sample sizes,thereby enhancing its applicability and robustness across diverse drive populations.Experimental results show that the proposed model achieves an F1-score of 0.9518 when applied to two different Seagate HDD models,while maintaining the False Positive Rate(FPR)below 1%.After incorporating the Complexity-Ratio Dynamic Time Warping(CDTW)based feature enhancement method,the best prediction model achieves a True Positive Rate(TPR)of up to 0.93 between the two models.For next-day failure prediction across various Seagate models,the model achieves an F1-score of up to 0.8792.Moreover,the experimental results also show that within the same brand,the higher the proportion of shared SMART attributes across different models,the better the prediction performance.In addition,HDFPMdemonstrates the best stability andmost significant performance in heterogeneous environments.展开更多
In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,...In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,existing methods face two major challenges:traditional feature engineering suffers from insufficient effective dimensionality in the feature space due to kinematic coupling,making it difficult to distinguish essential differences between maneuvers,while end-to-end deep learning models lack controllability in implicit feature learning and fail to model high-order long-range temporal dependencies.This paper proposes a trajectory feature pre-extraction method based on a Long-range Masked Autoencoder(LMAE),incorporating three key innovations:(1)Random Fragment High-ratio Masking(RFH-Mask),which enforces the model to learn long-range temporal correlations by masking 80%of trajectory data while retaining continuous fragments;(2)Kalman Filter-Guided Objective Function(KFG-OF),integrating trajectory continuity constraints to align the feature space with kinematic principles;and(3)Two-stage Decoupled Architecture,enabling efficient and controllable feature learning through unsupervised pre-training and frozen-feature transfer.Experimental results demonstrate that LMAE significantly improves the average recognition accuracy for 20-class maneuvers compared to traditional end-to-end models,while significantly accelerating convergence speed.The contributions of this work lie in:introducing high-masking-rate autoencoders into low-informationdensity trajectory analysis,proposing a feature engineering framework with enhanced controllability and efficiency,and providing a novel technical pathway for intelligent air combat decision-making systems.展开更多
OBJECTIVE:To explore the objective biological evidence for the classification and diagnosis of Traditional Chinese Medicine(TCM)syndromes in ankylosing spondylitis(AS)using multiomics analysis.METHODS:Patients with AS...OBJECTIVE:To explore the objective biological evidence for the classification and diagnosis of Traditional Chinese Medicine(TCM)syndromes in ankylosing spondylitis(AS)using multiomics analysis.METHODS:Patients with AS were categorized into kidney deficiency and blood stasis syndrome(SX group)and damp-heat stasis syndrome(SR group).Transcriptomic sequencing and quantitative plasma proteomics were performed on patients with AS and healthy volunteers.Multiomics integration was used to characterize the biological basis of AS with renal deficiency and blood stasis syndrome.Specific proteins were validated by quantitative reverse transcriptionpolymerase chain reaction(RT-q PCR)and enzymelinked immunosorbent assay(ELISA).RESULTS:Transcriptomic sequencing identified 31 significantly upregulated genes in patients with AS compared to healthy controls.These genes were primarily involved in tumor necrosis factor,interleukin-17,and nuclear factor kappa-B signaling pathways,as well as osteoblast differentiation and various viral infection pathways.Differentially expressed genes,including intercellular adhesion molecule 1(ICAM1),6-phosphofructo-2-kinase,cyclin-dependent kinase inhibitor 1A,interleukin 1 receptor antagonist,integrin alpha IIb,and myosin light chain 9 were more upregulated in the SX group than in the SR group.Quantitative proteomics identified 723 differential proteins associated with the disease and 788 differential proteins between the SX and SR groups.Notable proteins such as myeloperoxidase,cluster of differentiation 14,macrophage simulating 1(MST1),and Ras homolog enriched in brain may serve as characteristic proteins of the SX group.By integrating transcriptomic and proteomic data,45 associated differential molecules involved in platelet activation,pathogenic intestinal flora infection,glycolysis/gluconeogenesis,and T-cell receptor signaling pathways were identified in patients with AS compared to healthy controls.Additionally,ICAM1,MST1,C-X-C motif chemokine ligand 8(CXCL8),suppressor of cytokine signaling 3(SOCS3),and insulin-like growth factor binding protein 1(IGFBP1)were detected in TCM syndromes by RT-q PCR and ELISA,showing upregulation in AS renal deficiency and blood stasis syndromes,which is consistent with the proteomic and transcriptomic results.CONCLUSIONS:ICAM1,MST1,CXCL8,SOCS3,and IGFBP1 were identified as biomarkers of renal deficiency and blood stasis syndrome in AS.This study provides a biological basis for the differential diagnosis of TCM syndromes in AS,offering new insights into Chinese medicine evidence and more precise Chinese medicine treatments for AS.展开更多
Phishing email detection represents a critical research challenge in cybersecurity.To address this,this paper proposes a novel Double-S(statistical-semantic)feature model based on three core entities involved in email...Phishing email detection represents a critical research challenge in cybersecurity.To address this,this paper proposes a novel Double-S(statistical-semantic)feature model based on three core entities involved in email communication:the sender,recipient,and email content.We employ strategic game theory to analyze the offensive strategies of phishing attackers and defensive strategies of protectors,extracting statistical features from these entities.We also leverage the Qwen large language model to excavate implicit semantic features(e.g.,emotional manipulation and social engineering tactics)from email content.By integrating statistical and semantic features,our model achieves a robust representation of phishing emails.We introduce a hybrid detection model that integrates a convolutional neural network(CNN)module with the XGBoost(Extreme Gradient Boosting)classifier,effectively capturing local correlations in high-dimensional features.Experimental results on real-world phishing email datasets demonstrate the superiority of our approach,achieving an F1-score of 0.9587,precision of 0.9591,and recall of 0.9583,representing improvements of 1.3%–10.6%compared to state-of-the-art methods.展开更多
By integrating self-localization,environment mapping,and dynamic object tracking into a unified framework,visual simultaneous localization and mapping with multiple object tracking(SLAMMOT)enhances decision-making and...By integrating self-localization,environment mapping,and dynamic object tracking into a unified framework,visual simultaneous localization and mapping with multiple object tracking(SLAMMOT)enhances decision-making and interaction capabilities in applications such as autonomous driving,robotic navigation,and augmented reality.While numerous outstanding visual SLAMMOT methods have been proposed,the majority rely only on point features,overlooking the abundant and stable planar features in artificial objects that can provide valuable constraints.To address this limitation,we propose OP(object planar)-SLAM,an RGB-D SLAMMOT system that leverages planar features to improve object pose estimation and reconstruction accuracy.Specifically,we introduce an accurate object planar feature extraction and association method using normal images,alongside a novel object bundle adjustment framework that incorporates planar constraints for enhanced optimization.The proposed system is evaluated on both synthetic and public real-world datasets,including Oxford multimotion dataset(OMD)and KITTI tracking dataset.Especially on the OMD,where planar features are prominent,our method improves object pose estimation accuracy by approximately 60%.Extensive experiments demonstrate its effectiveness in enhancing object pose estimation and reconstruction,achieving notable performance compared with existing methods.Furthermore,OP-SLAM runs in real time,making it suitable for practical robots and augmented reality applications.展开更多
Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single ...Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single target”presents significant challenges due to its holistic approach.Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks,overcoming the limitations of reductionist research models and showing considerable value in TCM research.Recent integration of network target computational and experimental methods with artificial intelligence(AI)and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology.The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles.This review,centered on network targets,examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships,alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae,syndromes,and toxicity.Looking forward,network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics,potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.展开更多
文摘Liver is prone to viral infection.Viral hepatitis can be roughly divided into hepatitis A,B,C,D and E.Accurate diagnosis of viral hepatitis is crucial for accurate treatments.Different types of biomarkers,including non-invasive biomarkers have been explored for the diagnosis of viral hepatitis.With the fast development of multi-omics technology,non-invasive biomarkers can be detected from blood,saliva,urine,stool,and other body fluids.The advantages of non-invasive biomarkers are:1)non-invasive;2)convenient to test and 3)repeatable.The application of non-invasive biomarkers significantly improves the diagnostic accuracy of viral hepatitis.The non-invasive biomarkers can be sugars,proteins,nucleic acids,and even microorganisms.In this review,we summarized recent advances in identifying non-invasive biomarkers using multi-omics technology and discussed their potential diagnostic values for viral hepatitis.
基金supported by the National Natural Science Foundation of China(Grant No.:82560497,82260502,82272656)Guizhou Provincial Basic Research Program(Grant No.:Natural Science,MS[2025]-495)Talent Fund of Guizhou Provincial People’s Hospital(Grant No.:2022-33).
文摘Background:As a heterogeneous disease,breast cancer requires refined classification frameworks that can effectively guide targeted therapies.However,traditional methods fail to capture the comprehensive molecular insights needed for this purpose.Methods:To comprehensively capture breast cancer heterogeneity,we employed integrative clustering that incorporates six molecular features from 670 breast cancer samples.Ten distinct clustering algorithms were combined to ensure robust subtype identification,and the identified subtypes were validated in four independent datasets.Subsequently,we constructed a survival support vector machine prognostic model based on key molecular features to enhance survival prediction and clinical applicability.Results:Five novel subtypes were identified:consensus subtypes 1–5(CS1–CS5).CS2 was an aggressive subtype with elevated TP53 mutation rates,high tumor mutational burden,and strong sensitivity to YM-155 and ispinesib.Conversely,CS5 exhibited stable genomics with enhanced nucleotide excision repair and favorable prognoses.CS2 and CS4 showed enriched immune checkpoint expression,indicating potential immunotherapy responsiveness,while CS1 and CS5 exhibited immune-cold profiles.The survival support vector machine model effectively predicted survival outcomes across independent datasets.Conclusions:The refined breast cancer classification framework developed in this research uncovers new insights into molecular heterogeneity,enhances risk stratification,and enables the identification of promising therapeutic targets.The potential of this framework to optimize personalized treatment strategies warrants further clinical validation.
基金supported by the National Key Research and Development Program of China(2021YFF1000602)the National Natural Science Foundations(32202642)the earmarked fund for CARS-35-PIG.
文摘Bamei pigs,an indigenous Chinese breed,yield meat with a delectable flavor and boast higher carcass fat content compared to commercial breeds,making them a rich food source for humans.However,the differences in lipid and nutrient components between the adipose tissue of Bamei pigs and commercial pigs are still unclear.The study employed UPLC-MS/MS to quantify the composition of lipids and metabolites in the backfat of both Bamei and Large White pigs.A total of 428 lipids and 193 metabolites were significantly different between the 2 groups.Specifically,Bamei pig backfat exhibited altered levels of various lipids and metabolites that may potentially contribute to nutritional and flavor differences,including unsaturated triglycerides,free fatty acids,medium-chain triglycerides,essential amino acids,vitamins and antioxidants,while maintaining reduced cholesterol levels.Furthermore,we delved into the molecular mechanisms underlying these nutritional differences by analyzing significantly different 431 m RNAs and 865 proteins and integrating the regulatory network of protein-metabolite-lipid pathway.Importantly,in the pyruvate metabolic pathway of Bamei pigs,the bioprocess of lactate production was inhibited but the acetyl-Co A production was activated,suggesting the possibility that energy allocation favors the biogenesis of lipid precursors.These findings may contribute to guiding industrial food producers in enhancing the quality of lard at the genetic and molecular levels.
基金funded by the Key Research and Development and Promotion Project of Henan Province(Grant No.232102310130)。
文摘Background: Cancer-associated fibroblasts (CAFs) play critical roles in tumor progression and immunosuppression;however, their contribution to the functional classification and personalized treatment of gastric cancerremains poorly defined. This study aimed to identify effective therapeutic targets to facilitate individualized treatmentstrategies for patients with gastric cancer. Methods: Single-cell and bulk transcriptomic analyses were integrated tocharacterize gastric cancer fibroblasts. “Seurat”, “Slingshot”, and “CellChat” were used for dimensionality reduction,trajectory inference, and cell-cell communication analyses, respectively. Key metastasis-associated fibroblast moduleswere identified using High-dimensional weighted gene co-expression network analysis (hdWGCNA) to construct aprognostic model, which was further evaluated for immune infiltration, therapeutic response, and mutational features.The expression and function of the core gene tripeptidyl peptidase 1 (TPP1) were validated through immunoblotting, PCR, and functional assays. Results: Eight fibroblast subpopulations associated with gastric cancer metastasisexhibited distinct differentiation trajectories and transcriptional heterogeneity. Prognostic analysis indicated thatmetastasis-associated fibroblasts correlated with poor clinical outcomes. The high-risk subgroup showed markedimmunosuppression, resistance to immunotherapy, and reduced mutational burden, with tumor progression-relatedpathways significantly enriched in this group. In vitro experiments further confirmed that TPP1 knockdown suppressedgastric cancer cell metastasis, invasion, and clonogenic capacity while inducing apoptosis. Conclusion: This studycharacterized the heterogeneity of gastric cancer-associated fibroblasts using single-cell transcriptomic analysis andestablished a prognostic model based on metastasis-related fibroblast markers. The model demonstrated strongpredictive performance for patient prognosis, immune landscape, and immunotherapy response. Furthermore, thefindings highlighted the pivotal role of TPP1 in gastric cancer progression and its potential as a therapeutic target.
基金supported by grants from the Fundamental Research Funds for the Central Universities(No.2025ZFJH03)the Central Guidance Fund for Local Science and Technology Development(No.2024ZY01054)the CAMS Innovation Fund for Medical Sciences(No.2019-I2M-5-045).
文摘Background:Giant cell arteritis(GCA),the most common systemic vasculitis affecting elderly individuals,currently lacks specific therapies.This study aimed to systematically identify therapeutic targets for GCA through integration of large-scale multi-omics datasets.Methods:We constructed a multi-stage analytical framework encompassing 32 proteomic datasets(covering 2914 unique plasma proteins)and 6 transcriptomic datasets.Multi-omics integration strategies,including two-sample Mendelian randomization,colocalization analysis,and functional enrichment analysis,were employed to identify and validate causal relationships between candidate targets and GCA risk across 4 independent European-ancestry GCA cohorts.Single-cell RNA sequencing analysis of peripheral blood mononuclear cells from untreated GCA patients was performed to characterize hub gene-immune cell relationships.Results:We identified 43 plasma proteins causally associated with GCA[false discovery rate(FDR)<0.05],with 17 representing novel therapeutic targets.Through dual validation using proteome-wide association studies and transcriptome-wide association studies,we identified 13 high-confidence candidate targets with distinct tissue-specific expression patterns.Unc-51 like kinase 3(ULK3)emerged as the strongest protective factor(odds ratio=0.47,95%confidence interval:0.37–0.71)through autophagy regulation,while SLAMF7 represents an immediate drug repositioning opportunity as the target of food and drug administration-approved elotuzumab.Five targets have existing approved drugs(SLAMF7,ICAM1,IL18,IL6ST,CTSS).Single-cell analysis revealed profound disruption of hub gene-immune cell relationships in untreated GCA patients,with cell-type-specific alterations in inflammatory gene expression,and TYMP as the most critical hub gene.Conclusions:This study provides a clinically-actionable atlas of 43 potential therapeutic targets in GCA,identifying novel mechanisms including autophagy modulation and metabolic reprogramming,with immediate drug repositioning opportunities and precision medicine strategies based on tissue-specific and cell-type-specific expression patterns.These findings require experimental validation before clinical translation.
基金supported by the National Key Research and Development Program of China(2022YFD2101101)the Earmarked Fund for CARS-19+2 种基金the National Natural Science Foundation of China(32402634)the Modern Agricultural(Tea)Industry Technology System of Fujian Province,China(2025 No.593)the Special Fund for Science and Technology Innovation of Fujian Zhang Tianfu Tea Development Foundation,China(FJZTF01)。
文摘Understanding the molecular responses of tea leaves to mechanical stress is crucial for elucidating the mechanisms of post-harvest quality formation during oolong tea processing.This study employed an integrated multi-omics strategy to characterize the changes and interactions among metabolomic(MB),transcriptomic(TX),and proteomic(PT)profiles in mechanically stressed tea leaves.Mechanical stress initially activated damage-associated molecular patterns(DAMPs),including Ca^(2+)signaling,jasmonic acid signaling,and glutathione metabolism pathways.These processes subsequently induced quality-related metabolic pathways(QRMPs),particularly α-linolenic acid and phenylalanine metabolism.Upregulated expression of LOX,ADH1,and PAR genes,together with the increased abundance of their encoded proteins,respectively promoted the accumulation of jasmine lactone,benzyl alcohol,and 2-phenylethanol.These findings indicate that mechanical stress influences the metabolite biosynthesis in tea leaves through coordinated molecular responses.This study provides new insights into the molecular mechanisms underlying tea leaf responses to mechanical stress and a foundation for future investigations into how early molecular events may contribute to post-harvest metabolic changes during oolong tea processing.
文摘The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.
基金Supported by the National Key R&D Program of China(No.2023YFC2410203)Beijing Hospitals Authority Clinical Medicine Development of Special Funding Support(No.ZLRK202503).
文摘AIM:To investigate the clinical features and prognosis of patients with orbital inflammatory myofibroblastic tumor(IMT).METHODS:This retrospective study collected clinical data from 22 patients diagnosed with orbital IMT based on histopathological examination.The patients were followed up to assess their prognosis.Clinical data from patients,including age,gender,course of disease,past medical history,primary symptoms,ophthalmologic examination findings,general condition,as well as imaging,laboratory,histopathological,and immunohistochemical results from digital records were collected.Orbital magnetic resonance imaging(MRI)and(or)computed tomography(CT)scans were performed to assess bone destruction of the mass,invasion of surrounding tissues,and any inflammatory changes in periorbital areas.RESULTS:The mean age of patients with orbital IMT was 28.24±3.30y,with a male-to-female ratio of 1.2:1.Main clinical manifestations were proptosis,blurred vision,palpable mass,and pain.Bone destruction and surrounding tissue invasion occurred in 72.73%and 54.55%of cases,respectively.Inflammatory changes in the periorbital site were observed in 77.27%of the patients.Hematoxylin and eosin staining showed proliferation of fibroblasts and myofibroblasts,accompanied by infiltration of lymphocytes and plasma cells.Immunohistochemical staining revealed that smooth muscle actin(SMA)and vimentin were positive in 100%of cases,while anaplastic lymphoma kinase(ALK)showed positivity in 47.37%.The recurrence rate of orbital IMT was 27.27%,and sarcomatous degeneration could occur.There were no significant correlations between recurrence and factors such as age,gender,laterality,duration of the disease,periorbital tissue invasion,bone destruction,periorbital inflammation,tumor size,fever,leukocytosis,or treatment(P>0.05).However,lymphadenopathy and a Ki-67 index of 10%or higher may be risk factors for recurrence(P=0.046;P=0.023).CONCLUSION:Orbital IMT is a locally invasive disease that may recur or lead to sarcomatoid degeneration,primarily affecting young and middle-aged patients.The presence of lymphadenopathy and a Ki-67 index of 10%or higher may signify a poor prognosis.
基金supported by ZTE Industry-University-Institute Cooperation Funds under Grant No. HC-CN-20221107001。
文摘The detection of steel surface anomalies has become an industrial challenge due to variations in production equipment,processes,and characteristics.To alleviate the problem,this paper proposes a detection and localization method combining 3D depth and 2D RGB features.The framework comprises three stages:defect classification,defect location,an d warpage judgment.The first stage uses a dataefficient image Transformer model,the second stage utilizes reverse knowledge distillation,and the third stage performs feature fusion using3D depth and 2D RGB features.Experimental results show that the proposed algorithm achieves relatively high accuracy and feasibility,and can be effectively used in industrial scenarios.
基金General Program of National Natural Science Foundation of China(82474390)Construction Project of Pudong New Area Famous TCM Studios(National Pilot Zone for TCM Development,Shanghai)(PDZY-2025-0716)Shanghai Municipal Science and Technology Program Project Shanghai Key Laboratory of Health Identification and Assessment(21DZ2271000).
文摘Objective To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine(TCM)with machine learning algorithms.The proposed model seeks to establish a TCM-informed tool for early depression screening,thereby bridging traditional diagnostic principles with modern computational approaches.Methods The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1,2022 to October 1,2023,as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group.Videos of 3–10 s were captured using a Xiaomi Pad 5,and the TCM spirit and expressions were determined by TCM experts(at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions).Basic information,facial images,and interview information were collected through a portable TCM intelligent analysis and diagnosis device,and facial diagnosis features were extracted using the Open CV computer vision library technology.Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data,TCM spirit and expression features,and facial diagnosis feature parameters of the two groups,to compare the differences in TCM spirit and expression and facial features.Five machine learning algorithms,including extreme gradient boosting(XGBoost),decision tree(DT),Bernoulli naive Bayes(BernoulliNB),support vector machine(SVM),and k-nearest neighbor(KNN)classification,were used to construct a depression recognition model based on the fusion of TCM spirit and expression features.The performance of the model was evaluated using metrics such as accuracy,precision,and the area under the receiver operating characteristic(ROC)curve(AUC).The model results were explained using the Shapley Additive exPlanations(SHAP).Results A total of 93 depression patients and 87 healthy individuals were ultimately included in this study.There was no statistically significant difference in the baseline characteristics between the two groups(P>0.05).The differences in the characteristics of the spirit and expressions in TCM and facial features between the two groups were shown as follows.(i)Quantispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls(P<0.05),with characteristic features such as sad expressions,facial erythema,and changes in the lip color ranging from erythematous to cyanotic.(ii)Depressed patients exhibited significantly lower values in facial complexion L,lip L,and a values,and gloss index,but higher values in facial complexion a and b,lip b,low gloss index,and matte index(all P<0.05).(iii)The results of multiple models show that the XGBoost-based depression recognition model,integrating the TCM“spirit-expression”diagnostic framework,achieved an accuracy of 98.61%and significantly outperformed four benchmark algorithms—DT,BernoulliNB,SVM,and KNN(P<0.01).(iv)The SHAP visualization results show that in the recognition model constructed by the XGBoost algorithm,the complexion b value,categories of facial spirit,high gloss index,low gloss index,categories of facial expression and texture features have significant contribution to the model.Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic features with machine learning enables the construction of a high-precision depression detection model,offering a novel paradigm for objective depression diagnosis.
文摘BACKGROUND SMARCB1/INI1-deficient pancreatic undifferentiated rhabdoid carcinoma is a highly aggressive tumor,and spontaneous splenic rupture(SSR)as its presenting manifestation is rarely reported among pancreatic malignancies.CASE SUMMARY We herein report a rare case of a 59-year-old female who presented with acute left upper quadrant abdominal pain without any history of trauma.Abdominal imaging demonstrated a heterogeneous splenic lesion with hemoperitoneum,raising clinical suspicion of SSR.Emergency laparotomy revealed a pancreatic tumor invading the spleen and left kidney,with associated splenic rupture and dense adhesions,necessitating en bloc resection of the distal pancreas,spleen,and left kidney.Histopathology revealed a biphasic malignancy composed of moderately differentiated pancreatic ductal adenocarcinoma and an undifferentiated carcinoma with rhabdoid morphology and loss of SMARCB1 expression.Immunohistochemical analysis confirmed complete loss of SMARCB1/INI1 in the undifferentiated component,along with a high Ki-67 index(approximately 80%)and CD10 positivity.The ductal adenocarcinoma component retained SMARCB1/INI1 expression and was positive for CK7 and CK-pan.Transitional zones between the two tumor components suggested progressive dedifferentiation and underlying genomic instability.The patient received adjuvant chemotherapy with gemcitabine and nab-paclitaxel and maintained a satisfactory quality of life at the 6-month follow-up.CONCLUSION This study reports a rare case of SMARCB1/INI1-deficient undifferentiated rhabdoid carcinoma of the pancreas combined with ductal adenocarcinoma,presenting as SSR-an exceptionally uncommon initial manifestation of pancreatic malignancy.
基金supported by the National Natural Science Foundation of China,No.82374552(to WP)the Natural Science Foundation of Hunan Province,Nos.2024JJ2086,2024JJ6597(to JK)+1 种基金the Science and Technology Innovation Program of Hunan Province,No.2022RC1220(to WP)Support Plan for High-Level Health and Medical Talents in Hunan Province,No.20240304076(to WP).
文摘Because the pathogenesis of Alzheimer’s disease is multifactorial and complex,integrated multi-level omics analysis is essential to comprehensively elucidate its molecular alterations.We therefore utilized the well-established amyloid precursor protein/presenilin 1 mouse model to carry out an integrated multi-omics study using transcriptomic,proteomic,N^(6)-methyladenosine epitranscriptomic,and phosphoproteomic analyses.The results revealed substantial molecular alterations across multiple biological dimensions and the alteration in the expression of several key genes,such as GFAP,APP,and RTN4,in a mouse model of Alzheimer’s disease.The pronounced elevation of RTN4 in reactive astrocytes is indicative of its involvement in Alzheimer’s disease pathogenesis.Furthermore,we identified dysregulation of pathways related to endocytosis,highlighting the critical role of this process in disease progression.Our findings underscore the significant impact of post-transcriptional(N^(6)-methyladenosine methylation)and post-translational(phosphorylation)protein modifications,which have been underrepresented in Alzheimer’s disease research.The significant contribution made by this study is the integrated,multi-level omics analysis that we carried out to investigate the complex biological changes that occur in Alzheimer’s disease.Our findings provide novel insights into Alzheimer’s disease pathogenesis and suggest potential therapeutic targets,such as RTN4.
基金financially supported by the National Natural Science Foundation of China(No.U22A20439)the Shenzhen Fundamental Research Program(No.JCYJ20220818100418040)+2 种基金the Guangdong-Hong Kong-Macao Joint Innovation Fund(No.2024A0505040001)the Guangdong Basic and Applied Basic Research Foundation(2023A1515011122)the Shenzhen ShowMac Network Technology Co.,Ltd.
文摘This study proposes a multimodal deep learning framework for joint prediction of the state of health(SOH)and remaining useful life(RUL)of lithium-ion batteries.Twelve representative impedance features-covering charge-transfer resistance,solid electrolyte interface(SEI)layer impedance,and ion diffusion-are extracted from electrochemical impedance spectroscopy(EIS)and combined with short voltage/current segments to form a compact,interpretable feature set.A residual multi-layer perceptron(ResMLP)is employed for SOH regression,and a temporal convolutional network with attention(TCNAttention)is used for RUL estimation.Lifetime experiments on two battery types with different chemistries and form factors,evaluated through three rounds of paired cross-validation,validate the approach.Results show that the proposed features significantly reduce dimensionality and computational cost while substantially lowering SOH error,achieving an average normalized root mean square error of 2.3%.The RUL prediction reaches an average error of 14.8%.Overall,the framework balances interpretability,robustness,and feasibility,providing a practical solution for battery management systems(BMS)monitoring and life prediction.
基金funded by the National Key Lab Foundation,China(No.2020KLF030101)the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University,China(No.CX2025031)Shaanxi Innovative Research Team of Artificial Intelligence for Fluid Mechanics,China(No.2024RS-CXTD-16)。
文摘Improved delay detached eddy simulation is performed to explore the flow features and aero-optical effects of turrets with different bottom cylinder height at a freestream Mach number Ma=0.7.Analysis of both the time-averaged and instantaneous flow features demonstrate that the shock motion causes the oscillation of separated shear layer.In flow analysis,two unsteady shock-wake-correlated modes are discerned:the asymmetric shifting mode and the symmetric breathing mode.With the increase of cylinder height,the relative energy of shock gradually increases,which goes from 26%to 59%.The proper orthogonal decomposition analysis yields the single frequency peak for the two dominant modes.The frequency peaks of shifting mode are generally at StD<0.23,while the frequency peaks of breathing mode are generally at StD>0.26.The dynamic mode decomposition analysis gives range of frequency peak.The frequency peaks of shifting mode are in the range of StD=0.11-0.23,and the frequency peaks of breathing mode are in range of StD=0.26-0.41.Optical distortion analysis indicates that the distortion calculated in five cases is linked to the breathing mode.When the beam passes through the turbulent wake,it exhibits the high-frequency and high-amplitude characteristics.
基金supported by the Tianjin Manufacturing High Quality Development Special Foundation(No.20232185)the Roycom Foundation(No.70306901).
文摘Hard disk drives(HDDs)serve as the primary storage devices in modern data centers.Once a failure occurs,it often leads to severe data loss,significantly degrading the reliability of storage systems.Numerous studies have proposed machine learning-based HDD failure prediction models.However,the Self-Monitoring,Analysis,and Reporting Technology(SMART)attributes differ across HDD manufacturers.We define hard drives of the same brand and model as homogeneous HDD groups,and those from different brands or models as heterogeneous HDD groups.In practical engineering scenarios,a data center is often composed of a heterogeneous population of HDDs,spanning multiple vendors and models.Existing research predominantly focuses on homogeneous datasets,ignoring the model’s generalization capability across heterogeneous HDDs.As a result,HDD models with limited samples often suffer from poor training effectiveness and prediction performance.To address this issue,we investigate generalizable SMART predictors across heterogeneous HDD groups.By extracting time-series features within a fixed sliding time window,we propose a Heterogeneous Disk Failure Prediction Method based on Time Series Features(HDFPM)framework.This method is adaptable to HDD models with limited sample sizes,thereby enhancing its applicability and robustness across diverse drive populations.Experimental results show that the proposed model achieves an F1-score of 0.9518 when applied to two different Seagate HDD models,while maintaining the False Positive Rate(FPR)below 1%.After incorporating the Complexity-Ratio Dynamic Time Warping(CDTW)based feature enhancement method,the best prediction model achieves a True Positive Rate(TPR)of up to 0.93 between the two models.For next-day failure prediction across various Seagate models,the model achieves an F1-score of up to 0.8792.Moreover,the experimental results also show that within the same brand,the higher the proportion of shared SMART attributes across different models,the better the prediction performance.In addition,HDFPMdemonstrates the best stability andmost significant performance in heterogeneous environments.
文摘In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,existing methods face two major challenges:traditional feature engineering suffers from insufficient effective dimensionality in the feature space due to kinematic coupling,making it difficult to distinguish essential differences between maneuvers,while end-to-end deep learning models lack controllability in implicit feature learning and fail to model high-order long-range temporal dependencies.This paper proposes a trajectory feature pre-extraction method based on a Long-range Masked Autoencoder(LMAE),incorporating three key innovations:(1)Random Fragment High-ratio Masking(RFH-Mask),which enforces the model to learn long-range temporal correlations by masking 80%of trajectory data while retaining continuous fragments;(2)Kalman Filter-Guided Objective Function(KFG-OF),integrating trajectory continuity constraints to align the feature space with kinematic principles;and(3)Two-stage Decoupled Architecture,enabling efficient and controllable feature learning through unsupervised pre-training and frozen-feature transfer.Experimental results demonstrate that LMAE significantly improves the average recognition accuracy for 20-class maneuvers compared to traditional end-to-end models,while significantly accelerating convergence speed.The contributions of this work lie in:introducing high-masking-rate autoencoders into low-informationdensity trajectory analysis,proposing a feature engineering framework with enhanced controllability and efficiency,and providing a novel technical pathway for intelligent air combat decision-making systems.
基金Supported by National Natural Science Foundation of China:to Explore the Molecular Mechanism of Treating Ankylosing Spondylitis by Invigorating Kidney and Activating Blood from the Regulation of T helper 17 Cells Differentiation and Migration by Histone Histone H3 Lysine 27 Trimethylation(No.81873292)Science and Technology Innovation Project of China Academy of Chinese Medical Sciences:Evaluation of Curative Effect and Molecular Mechanism of Shenqiangji Decoction in the Treatment of Ankylosing Spondylitis by Standard Control and Intervention in the Imaging Progress of Spinal Spondylitis(No.CI2021A01506)High Level Chinese Medical Hospital Promotion Project:Research and Development of Traditional Chinese Medicine Preparation for Treating Ankylosing Spondylitis with Danxian Bushen Qiangji Granules(No.HLCMHPP2023049)。
文摘OBJECTIVE:To explore the objective biological evidence for the classification and diagnosis of Traditional Chinese Medicine(TCM)syndromes in ankylosing spondylitis(AS)using multiomics analysis.METHODS:Patients with AS were categorized into kidney deficiency and blood stasis syndrome(SX group)and damp-heat stasis syndrome(SR group).Transcriptomic sequencing and quantitative plasma proteomics were performed on patients with AS and healthy volunteers.Multiomics integration was used to characterize the biological basis of AS with renal deficiency and blood stasis syndrome.Specific proteins were validated by quantitative reverse transcriptionpolymerase chain reaction(RT-q PCR)and enzymelinked immunosorbent assay(ELISA).RESULTS:Transcriptomic sequencing identified 31 significantly upregulated genes in patients with AS compared to healthy controls.These genes were primarily involved in tumor necrosis factor,interleukin-17,and nuclear factor kappa-B signaling pathways,as well as osteoblast differentiation and various viral infection pathways.Differentially expressed genes,including intercellular adhesion molecule 1(ICAM1),6-phosphofructo-2-kinase,cyclin-dependent kinase inhibitor 1A,interleukin 1 receptor antagonist,integrin alpha IIb,and myosin light chain 9 were more upregulated in the SX group than in the SR group.Quantitative proteomics identified 723 differential proteins associated with the disease and 788 differential proteins between the SX and SR groups.Notable proteins such as myeloperoxidase,cluster of differentiation 14,macrophage simulating 1(MST1),and Ras homolog enriched in brain may serve as characteristic proteins of the SX group.By integrating transcriptomic and proteomic data,45 associated differential molecules involved in platelet activation,pathogenic intestinal flora infection,glycolysis/gluconeogenesis,and T-cell receptor signaling pathways were identified in patients with AS compared to healthy controls.Additionally,ICAM1,MST1,C-X-C motif chemokine ligand 8(CXCL8),suppressor of cytokine signaling 3(SOCS3),and insulin-like growth factor binding protein 1(IGFBP1)were detected in TCM syndromes by RT-q PCR and ELISA,showing upregulation in AS renal deficiency and blood stasis syndromes,which is consistent with the proteomic and transcriptomic results.CONCLUSIONS:ICAM1,MST1,CXCL8,SOCS3,and IGFBP1 were identified as biomarkers of renal deficiency and blood stasis syndrome in AS.This study provides a biological basis for the differential diagnosis of TCM syndromes in AS,offering new insights into Chinese medicine evidence and more precise Chinese medicine treatments for AS.
基金supported by the National Key Research and Development Program of China(No.2023YFB3105700).
文摘Phishing email detection represents a critical research challenge in cybersecurity.To address this,this paper proposes a novel Double-S(statistical-semantic)feature model based on three core entities involved in email communication:the sender,recipient,and email content.We employ strategic game theory to analyze the offensive strategies of phishing attackers and defensive strategies of protectors,extracting statistical features from these entities.We also leverage the Qwen large language model to excavate implicit semantic features(e.g.,emotional manipulation and social engineering tactics)from email content.By integrating statistical and semantic features,our model achieves a robust representation of phishing emails.We introduce a hybrid detection model that integrates a convolutional neural network(CNN)module with the XGBoost(Extreme Gradient Boosting)classifier,effectively capturing local correlations in high-dimensional features.Experimental results on real-world phishing email datasets demonstrate the superiority of our approach,achieving an F1-score of 0.9587,precision of 0.9591,and recall of 0.9583,representing improvements of 1.3%–10.6%compared to state-of-the-art methods.
基金Supported by Major Science and Technology Project of Hubei Province(2022AAA009)。
文摘By integrating self-localization,environment mapping,and dynamic object tracking into a unified framework,visual simultaneous localization and mapping with multiple object tracking(SLAMMOT)enhances decision-making and interaction capabilities in applications such as autonomous driving,robotic navigation,and augmented reality.While numerous outstanding visual SLAMMOT methods have been proposed,the majority rely only on point features,overlooking the abundant and stable planar features in artificial objects that can provide valuable constraints.To address this limitation,we propose OP(object planar)-SLAM,an RGB-D SLAMMOT system that leverages planar features to improve object pose estimation and reconstruction accuracy.Specifically,we introduce an accurate object planar feature extraction and association method using normal images,alongside a novel object bundle adjustment framework that incorporates planar constraints for enhanced optimization.The proposed system is evaluated on both synthetic and public real-world datasets,including Oxford multimotion dataset(OMD)and KITTI tracking dataset.Especially on the OMD,where planar features are prominent,our method improves object pose estimation accuracy by approximately 60%.Extensive experiments demonstrate its effectiveness in enhancing object pose estimation and reconstruction,achieving notable performance compared with existing methods.Furthermore,OP-SLAM runs in real time,making it suitable for practical robots and augmented reality applications.
文摘Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single target”presents significant challenges due to its holistic approach.Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks,overcoming the limitations of reductionist research models and showing considerable value in TCM research.Recent integration of network target computational and experimental methods with artificial intelligence(AI)and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology.The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles.This review,centered on network targets,examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships,alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae,syndromes,and toxicity.Looking forward,network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics,potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.
