This paper presents an approach of singular value de- composition plus digital phase lock loop to solve the difficult problem of blind pseudo-noise （PN） sequence estimation in low signal to noise ratios （SNR） dire...This paper presents an approach of singular value de- composition plus digital phase lock loop to solve the difficult problem of blind pseudo-noise （PN） sequence estimation in low signal to noise ratios （SNR） direct sequence spread spectrum （DS-SS） signals with residual carrier. This approach needs some given parameters, such as the period and code rate of PN sequence. The received signal is firstly sampled and divided into non-overlapping signal vectors according to a temporal window, whose duration is two periods of PN sequence. An autocorrelation matrix is then computed and accumulated by those signal vectors one by one. The PN sequence with residual carrier can be estimated by the principal eigenvector of the autocorrelation matrix. Further more, a digital phase lock loop is used to process the estimated PN sequence, it estimates and tracks the residual carrier and removes the residual carrier in the end. Theory analysis and computer simulation results show that this approach can effectively realize the PN sequence blind estimation from the input DS-SS signals with residual carrier in lower SNR.展开更多
In order to solve the problem of coherent signal subspace method(CSSM) depending on the estimated accuracy of signal subspace, a new direction of arrival(DOA) estimation method of wideband source, which is based on it...In order to solve the problem of coherent signal subspace method(CSSM) depending on the estimated accuracy of signal subspace, a new direction of arrival(DOA) estimation method of wideband source, which is based on iterative adaptive spectral reconstruction, is proposed. Firstly, the wideband signals are divided into several narrowband signals of different frequency bins by discrete Fourier transformation(DFT). Then, the signal matched power spectrum in referenced frequency bins is computed, which can form the initial covariance matrix. Finally, the linear restrained minimum variance spectral(Capon spectral) of signals in other frequency bins are reconstructed using sequential iterative means, so the DOA can be estimated by the locations of spectral peaks. Theoretical analysis and simulation results show the proposed method based on the iterative spectral reconstruction for the covariance matrices of all sub-bands can avoid the problem of determining the signal subspace accurately with the coherent signal subspace method under the conditions of small samples and low signal to noise ratio(SNR), and it can also realize full dimensional focusing of different sub-band data, which can be applied to coherent sources and can significantly improve the accuracy of DOA estimation.展开更多
A system that allows computer interaction by disabled people with very low mobility and who cannot use the standard procedure based on keyboard and mouse is presented. The development device uses the patient’s volunt...A system that allows computer interaction by disabled people with very low mobility and who cannot use the standard procedure based on keyboard and mouse is presented. The development device uses the patient’s voluntary biomechanical signals, specifically, winks—which constitute an ability that generally remains in this kind of patients—, as interface to control the computer. A prototype based on robust and low-cost elements has been built and its performance has been validated through real trials by 16 users without previous training. The system can be optimized after a learning period in order to be adapted to every user. Also, good results were obtained in a subjective satisfaction survey that was completed by the users after carrying out the test trials.展开更多
In many domains of science and technology, as the need for secured transmission of information has grown over the years, a variety of methods have been studied and devised to achieve this goal. In this paper, we prese...In many domains of science and technology, as the need for secured transmission of information has grown over the years, a variety of methods have been studied and devised to achieve this goal. In this paper, we present an information securing method using chaos encryption. Our proposal uses only one chaotic oscillator both for signal encryption and decryption for?avoiding the delicate synchronisation step. We carried out numerical and electronic simulations of the proposed circuit using electrocardiographic signals as input. Results obtained from both simulations were compared and exhibited a good agreement proving the suitability of our system for signal encryption and decryption.展开更多
The topic of this paper is the utilization of time for optical information processing. As clock rates in computing and communication systems increase and reach the THz border, optical techniques for signal filtering, ...The topic of this paper is the utilization of time for optical information processing. As clock rates in computing and communication systems increase and reach the THz border, optical techniques for signal filtering, shaping and clock distribution become attractive. We discuss the use of optics in temporal processing and consider in particular diffractive solutions. In part one of this paper, we discuss the basic concepts of temporal optics.展开更多
The topic of this presentation is the utilization of time for optical information processing. As clock rates in computing and communication systems increase and reach the THz border, optical techniques for signal filt...The topic of this presentation is the utilization of time for optical information processing. As clock rates in computing and communication systems increase and reach the THz border, optical techniques for signal filtering, shaping and clock distribution become attractive. We discuss the use of optics in temporal processing and consider in particular diffractive solutions, in this paper, we describe the use of double diffraction for implementing an ultrafast tapped-delay line.展开更多
The strawberry crimp nematode(Aphelenchoides fragariae) is a serious pathogen of ornamental crops and a significant quarantine concern in approximately 50 countries and regions,including China.A nematode population be...The strawberry crimp nematode(Aphelenchoides fragariae) is a serious pathogen of ornamental crops and a significant quarantine concern in approximately 50 countries and regions,including China.A nematode population belonging to the genus Aphelenchoides was isolated from symptomatic leaves of fuchsia plants(Fuchsia×hybrida Hort.ex Sieb.& Voss.) in Chengdu,Sichuan Province,China.Morphological and morphometric characteristics were determined using light microscopy and scanning electron microscopy.Detailed examination revealed diagnostic features consistent with A.fragariae.Three ribosomal DNA(rDNA) regions,i.e.,partial small subunit(SSU) rRNA,D2-D3 expansion segments of the large subunit(LSU) rRNA,and the internal transcribed spacer(ITS),were amplified and sequenced.Bayesian phylogenetic analyses based on these sequences placed the isolate in a well-supported monophyletic clade with reference A.fragariae specimens,clearly separated from other Aphelenchoides species.Furthermore,host-suitability assays demonstrated that this nematode population not only infects and reproduces on Fuchsia×hybrida,but also on Fragaria ananassa and Pteris vittata,two known hosts of A.fragariae.Collectively,morphological,molecular,and host-range evidence confirm the identification of this nematode as A.fragariae.To our knowledge,this represents the first molecular and morphological confirmation of A.fragariae in China,and the first report of Fuchsia×hybrida as a natural host for this species.展开更多
Securing restricted zones such as airports,research facilities,and military bases requires robust and reliable access control mechanisms to prevent unauthorized entry and safeguard critical assets.Face recognition has...Securing restricted zones such as airports,research facilities,and military bases requires robust and reliable access control mechanisms to prevent unauthorized entry and safeguard critical assets.Face recognition has emerged as a key biometric approach for this purpose;however,existing systems are often sensitive to variations in illumination,occlusion,and pose,which degrade their performance in real-world conditions.To address these challenges,this paper proposes a novel hybrid face recognition method that integrates complementary feature descriptors such as Fuzzy-Gabor 2D Fisher Linear Discriminant(FG-2DFLD),Generalized 2D Linear Discriminant Analysis(G2DLDA),andModular-Local Binary Patterns(Modular-LBP)with Dempster–Shafer(DS)evidence theory for decision fusion.The proposed framework extracts global,structural,and local texture features,models them using Gaussian distributions to estimate belief factors,and fuses these belief factors through DS theory to explicitly handle uncertainty and conflict among descriptors.Experimental validation was performed on two widely used benchmark datasets,ORL and Cropped Yale B,achieving recognition rates exceeding 98%,which outperform traditional methods as well as recent deep learning-based approaches.Furthermore,the method demonstrated strong robustness under noisy conditions,maintaining accuracies above 96%with salt-and-pepper and Gaussian noise.These results highlight the effectiveness of the proposed integration strategy in enhancing accuracy,reliability,and resilience compared to single-descriptor and conventional fusion methods.Given its high performance and efficiency,the proposed method shows strong potential for deployment in real-world restricted-zone applications such as smart parking systems,secure facility access,and other high-security domains.展开更多
Recently,Tian et al.published a research paper with significant breakthroughs in Cell[1].The study found that targeting the signalling pathways named Serpine2-lowdensity lipoprotein receptor-related protein 1(Lrp1)and...Recently,Tian et al.published a research paper with significant breakthroughs in Cell[1].The study found that targeting the signalling pathways named Serpine2-lowdensity lipoprotein receptor-related protein 1(Lrp1)and ectonucleoside triphosphate diphosphohydrolase 1(CD39)-adenosine A_(3)receptor(A_(3)AR)is a promising strategy for the treatment of vascular dementia.The Serpine2-Lrp1 signalling pathway primarily exerts its therapeutic effects on myelin regeneration by regulating the differentiation of oligodendrocyte precursor cells.Serpine2 is a secretory serine protease inhibitor regulates proteolytic homeostasis.It may also bind to cell surface receptors such as Lrp1 to directly activate signalling pathways.As a transmembrane glycoprotein receptor,Lrpl mediates the endocytic clearance of ligands.展开更多
Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows rais...Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows raises concerns,particularly the risk of subtle distortions or hallucinations that may undermine diagnostic accuracy and weaken trust in AI-assisted decision-making.To address this challenge,we propose a hybrid deep learning framework designed to detect GAN-induced artifacts in medical images,thereby reinforcing the reliability of AI-driven diagnostics.The framework integrates low-level statistical descriptors,including high-frequency residuals and Gray-Level Co-occurrence Matrix(GLCM)texture features,with high-level semantic representations extracted from a pre-trained ResNet18.This dual-stream approach enables detection of both pixel-level anomalies and structural inconsistencies introduced by GAN-based manipulation.We validated the framework on a curated dataset of 10,000 medical images,evenly split between authentic and GAN-generated samples across four modalities:MRI,CT,X-ray,and fundus photography.To improve generalizability to real-world clinical settings,we incorporated domain adaptation strategies such as adversarial training and style transfer,reducing domain shift by 15%.Experimental results demonstrate robust performance,achieving 92.6%accuracy and an F1-score of 0.91 on synthetic test data,and maintaining strong performance on real-world GAN-modified images with 87.3%accuracy and an F1-score of 0.85.Additionally,the model attained an AUC of 0.96 and an average precision of 0.92,outperforming conventional GAN detection pipelines and baseline Convolutional Neural Network(CNN)architectures.These findings establish the proposed framework as an effective and reliable solution for detecting GAN-induced hallucinations in medical imaging,representing an important step toward building trustworthy and clinically deployable AI systems.展开更多
Amyotrophic lateral sclerosis(ALS)is a fatal,late-onset neurodegenerative disorder characterized by the progressive degeneration of motor neurons in the motor cortex,brainstem,and spinal cord(Feldman et al.,2022).
Lung cancer remains a major global health challenge,with early diagnosis crucial for improved patient survival.Traditional diagnostic techniques,including manual histopathology and radiological assessments,are prone t...Lung cancer remains a major global health challenge,with early diagnosis crucial for improved patient survival.Traditional diagnostic techniques,including manual histopathology and radiological assessments,are prone to errors and variability.Deep learning methods,particularly Vision Transformers(ViT),have shown promise for improving diagnostic accuracy by effectively extracting global features.However,ViT-based approaches face challenges related to computational complexity and limited generalizability.This research proposes the DualSet ViT-PSO-SVM framework,integrating aViTwith dual attentionmechanisms,Particle Swarm Optimization(PSO),and SupportVector Machines(SVM),aiming for efficient and robust lung cancer classification acrossmultiple medical image datasets.The study utilized three publicly available datasets:LIDC-IDRI,LUNA16,and TCIA,encompassing computed tomography(CT)scans and histopathological images.Data preprocessing included normalization,augmentation,and segmentation.Dual attention mechanisms enhanced ViT’s feature extraction capabilities.PSO optimized feature selection,and SVM performed classification.Model performance was evaluated on individual and combined datasets,benchmarked against CNN-based and standard ViT approaches.The DualSet ViT-PSO-SVM significantly outperformed existing methods,achieving superior accuracy rates of 97.85%(LIDC-IDRI),98.32%(LUNA16),and 96.75%(TCIA).Crossdataset evaluations demonstrated strong generalization capabilities and stability across similar imagingmodalities.The proposed framework effectively bridges advanced deep learning techniques with clinical applicability,offering a robust diagnostic tool for lung cancer detection,reducing complexity,and improving diagnostic reliability and interpretability.展开更多
critical for guiding treatment and improving patient outcomes.Traditional molecular subtyping via immuno-histochemistry(IHC)test is invasive,time-consuming,and may not fully represent tumor heterogeneity.This study pr...critical for guiding treatment and improving patient outcomes.Traditional molecular subtyping via immuno-histochemistry(IHC)test is invasive,time-consuming,and may not fully represent tumor heterogeneity.This study proposes a non-invasive approach using digital mammography images and deep learning algorithm for classifying breast cancer molecular subtypes.Four pretrained models,including two Convolutional Neural Networks(MobileNet_V3_Large and VGG-16)and two Vision Transformers(ViT_B_16 and ViT_Base_Patch16_Clip_224)were fine-tuned to classify images into HER2-enriched,Luminal,Normal-like,and Triple Negative subtypes.Hyperparameter tuning,including learning rate adjustment and layer freezing strategies,was applied to optimize performance.Among the evaluated models,ViT_Base_Patch16_Clip_224 achieved the highest test accuracy(94.44%),with equally high precision,recall,and F1-score of 0.94,demonstrating excellent generalization.MobileNet_V3_Large achieved the same accuracy but showed less training stability.In contrast,VGG-16 recorded the lowest performance,indicating a limitation in its generalizability for this classification task.The study also highlighted the superior performance of the Vision Transformer models over CNNs,particularly due to their ability to capture global contextual features and the benefit of CLIP-based pretraining in ViT_Base_Patch16_Clip_224.To enhance clinical applicability,a graphical user interface(GUI)named“BCMS Dx”was developed for streamlined subtype prediction.Deep learning applied to mammography has proven effective for accurate and non-invasive molecular subtyping.The proposed Vision Transformer-based model and supporting GUI offer a promising direction for augmenting diagnostic workflows,minimizing the need for invasive procedures,and advancing personalized breast cancer management.展开更多
Human pose estimation is crucial across diverse applications,from healthcare to human-computer interaction.Integrating inertial measurement units(IMUs)with monocular vision methods holds great potential for leveraging...Human pose estimation is crucial across diverse applications,from healthcare to human-computer interaction.Integrating inertial measurement units(IMUs)with monocular vision methods holds great potential for leveraging complementary modalities;however,existing approaches are often limited by IMU drift,noise,and underutilization of visual information.To address these limitations,we propose a novel dual-stream feature extraction framework that effectively combines temporal IMU data and single-view image features for improved pose estimation.Short-term dependencies in IMU sequences are captured with convolutional layers,while a Transformerbased architecture models long-range temporal dynamics.To mitigate IMU drift and inter-sensor inconsistencies,a complementary filtering module is introduced alongside a cross-channel interaction mechanism.Features from the IMU and image streams are then fused via a dedicated fusion module and further refined utilizing a high-precision regression head for accurate pose prediction.Experimental results on benchmark datasets demonstrate that our method significantly outperforms existing techniques in terms of estimation,accuracy,and robustness,validating the effectiveness of our dual-stream architecture.展开更多
After injury,bone tissue initiates a reparative response to restore its structure and function.The failure to initiate or delay this response could result in fracture nonunion.The molecular mechanisms underlying the o...After injury,bone tissue initiates a reparative response to restore its structure and function.The failure to initiate or delay this response could result in fracture nonunion.The molecular mechanisms underlying the occurrence of fracture nonunion are not yet established.We propose that hypoxia-triggered signaling pathways,mediated by reactive oxygen species(ROS)homeostasis,control Bmp2 expression and fracture healing initiation.The excessive ROS leads to oxidative stress and,ultimately,fracture nonunion.In this study,we silenced Apex1,the final ROS signaling transducer that mediates the activation of key transcription factors by their cysteines oxidoreduction,evaluating its role during endochondral ossification and fracture repair.Silencing Apex1 in limb bud mesenchyme results in transient metaphyseal dysplasia derived from impaired chondrocyte differentiation.During bone regeneration,Apex1 silencing induces a fracture nonunion phenotype,characterized by delayed fracture repair initiation,impaired periosteal response,and reduced chondrocyte and osteoblast differentiation.This compromised chondrocyte differentiation hampers callus vascularization and healing progression.Our findings highlight a critical mechanism where hypoxia-driven ROS signaling in mesenchymal progenitors through APEX1 is essential for fracture healing initiation.展开更多
Little is known about how chronic inflammation contributes to the progression of hepatoceUular carcinoma （HCC）, especially the initiation of cancer. To uncover the critical transition from chronic inflammation to HC...Little is known about how chronic inflammation contributes to the progression of hepatoceUular carcinoma （HCC）, especially the initiation of cancer. To uncover the critical transition from chronic inflammation to HCC and the molecular mechanisms at a network level, we analyzed the time-series proteomic data of woodchuck hepatitis virus/c.myc mice and age-matched wt-C57BL/6 mice using our dynamical network biomarker （DNB） model. DNB analysis indicated that the 5th month after birth of transgenic mice was the critical period of cancer initiation, just before the critical transition, which is consistent with clinical symptoms. Meanwhile, the DNB-associated network showed a drastic inversion of protein expression and coexpression levels before and after the critical transition. Two members of DNB, PLA2G6 and CYP2C44, along with their associated differentially expressed proteins, were found to induce dysfunction of arachidonic acid metabolism, further activate inflammatory responses through inflammatory mediator regulation of transient receptor potential channels, and finally lead to impairments of liver detoxification and malignant transition to cancer. As a c-Myc target, PLA2G6 positively correlated with c-Myc in expression, showing a trend from decreasing to increasing during carcinogenesis, with the minimal point at the critical transition or tipping point. Such trend of homologous PLA2G6 and c-Myc was also observed during human hepatocarcinogenesis, with the minimal point at high-grade dysplastic nodules （a stage just before the carcinogenesis）. Our study implies that PLA2G6 might function as an oncogene like famous c-Myc during hepatocar- cinogenesis, while downregulation of PLA2G6 and c-Myc could be a warning signal indicating imminent carcinogenesis.展开更多
The phase group synchronization between any signals is further revealed,which is based on proposing the new concepts of the greatest common factor frequency,the least common multiple period,quantized phase shift resol...The phase group synchronization between any signals is further revealed,which is based on proposing the new concepts of the greatest common factor frequency,the least common multiple period,quantized phase shift resolution,equivalent phase comparison frequency and so on.Then the problem of phase comparison and processing between different frequency signals is solved and shown in detail.Using the basic principle and the variation law of group phase difference,the frequency stability better than 10-14/s can be easily obtained in the time&frequency measurement and control domain,and experimental results also show the phase relations between atomic energy level transition signal and the locked crystal oscillator signal in an active hydrogen atomic clock are strict phase group synchronization,and locked precision with 10-13/s can be reached based on phase group synchronization.The phase group synchronization can provide technical support to frequency linking among radio frequency,microwave and light frequency.展开更多
Prostate cancer(PCa)remains a major cause of cancer-related mortality in men,largely due to therapy resistance and metastatic progression.Increasing evidence highlights the tumor microenvironment(TME),particularly can...Prostate cancer(PCa)remains a major cause of cancer-related mortality in men,largely due to therapy resistance and metastatic progression.Increasing evidence highlights the tumor microenvironment(TME),particularly cancer-associated fibroblasts(CAFs),as a critical determinant of disease behavior.CAFs constitute a heterogeneous population originating from fibroblasts,mesenchymal stem cells,endothelial cells,epithelial cells undergoing epithelial-mesenchymal transition(EMT),and adipose tissue.Through dynamic crosstalk with tumor,immune,endothelial,and adipocyte compartments,CAFs orchestrate oncogenic processes including tumor proliferation,invasion,immune evasion,extracellular matrix remodeling,angiogenesis,and metabolic reprogramming.This review comprehensively summarizes the cellular origins,phenotypic and functional heterogeneity,and spatial distribution of CAFs within the prostate TME.We further elucidate the molecular mechanisms by which CAFs regulate PCa progression and therapeutic resistance,and critically evaluate emerging strategies to therapeutically target CAFmediated signaling,metabolic,and immune pathways.By integrating recent advances from single-cell and spatial transcriptomics(ST),our objective is to provide a holistic framework for understanding CAF biology and to highlight potential avenues for stromal reprogramming as an adjunct to current PCa therapies.展开更多
基金supported by the National Natural Science Foundation of China (10776040 60602057)+4 种基金Program for New Century Excellent Talents in University (NCET)the Project of Key Laboratory of Signal and Information Processing of Chongqing (CSTC2009CA2003)the Natural Science Foundation of Chongqing Science and Technology Commission (CSTC2009BB2287)the Natural Science Foundation of Chongqing Municipal Education Commission (KJ060509 KJ080517)
文摘This paper presents an approach of singular value de- composition plus digital phase lock loop to solve the difficult problem of blind pseudo-noise （PN） sequence estimation in low signal to noise ratios （SNR） direct sequence spread spectrum （DS-SS） signals with residual carrier. This approach needs some given parameters, such as the period and code rate of PN sequence. The received signal is firstly sampled and divided into non-overlapping signal vectors according to a temporal window, whose duration is two periods of PN sequence. An autocorrelation matrix is then computed and accumulated by those signal vectors one by one. The PN sequence with residual carrier can be estimated by the principal eigenvector of the autocorrelation matrix. Further more, a digital phase lock loop is used to process the estimated PN sequence, it estimates and tracks the residual carrier and removes the residual carrier in the end. Theory analysis and computer simulation results show that this approach can effectively realize the PN sequence blind estimation from the input DS-SS signals with residual carrier in lower SNR.
基金supported by the National Natural Science Foundation of China(61671352)the open foundation of Key Laboratory of Cognitive Radio and Information Processing,Ministry of Education(Guilin University of Electronic Technology)(CRKL160206)Xi’an University of Science and Technology Doctor(after)Start Gold Project(2017QDJ018)
文摘In order to solve the problem of coherent signal subspace method(CSSM) depending on the estimated accuracy of signal subspace, a new direction of arrival(DOA) estimation method of wideband source, which is based on iterative adaptive spectral reconstruction, is proposed. Firstly, the wideband signals are divided into several narrowband signals of different frequency bins by discrete Fourier transformation(DFT). Then, the signal matched power spectrum in referenced frequency bins is computed, which can form the initial covariance matrix. Finally, the linear restrained minimum variance spectral(Capon spectral) of signals in other frequency bins are reconstructed using sequential iterative means, so the DOA can be estimated by the locations of spectral peaks. Theoretical analysis and simulation results show the proposed method based on the iterative spectral reconstruction for the covariance matrices of all sub-bands can avoid the problem of determining the signal subspace accurately with the coherent signal subspace method under the conditions of small samples and low signal to noise ratio(SNR), and it can also realize full dimensional focusing of different sub-band data, which can be applied to coherent sources and can significantly improve the accuracy of DOA estimation.
文摘A system that allows computer interaction by disabled people with very low mobility and who cannot use the standard procedure based on keyboard and mouse is presented. The development device uses the patient’s voluntary biomechanical signals, specifically, winks—which constitute an ability that generally remains in this kind of patients—, as interface to control the computer. A prototype based on robust and low-cost elements has been built and its performance has been validated through real trials by 16 users without previous training. The system can be optimized after a learning period in order to be adapted to every user. Also, good results were obtained in a subjective satisfaction survey that was completed by the users after carrying out the test trials.
文摘In many domains of science and technology, as the need for secured transmission of information has grown over the years, a variety of methods have been studied and devised to achieve this goal. In this paper, we present an information securing method using chaos encryption. Our proposal uses only one chaotic oscillator both for signal encryption and decryption for?avoiding the delicate synchronisation step. We carried out numerical and electronic simulations of the proposed circuit using electrocardiographic signals as input. Results obtained from both simulations were compared and exhibited a good agreement proving the suitability of our system for signal encryption and decryption.
文摘The topic of this paper is the utilization of time for optical information processing. As clock rates in computing and communication systems increase and reach the THz border, optical techniques for signal filtering, shaping and clock distribution become attractive. We discuss the use of optics in temporal processing and consider in particular diffractive solutions. In part one of this paper, we discuss the basic concepts of temporal optics.
文摘The topic of this presentation is the utilization of time for optical information processing. As clock rates in computing and communication systems increase and reach the THz border, optical techniques for signal filtering, shaping and clock distribution become attractive. We discuss the use of optics in temporal processing and consider in particular diffractive solutions, in this paper, we describe the use of double diffraction for implementing an ultrafast tapped-delay line.
基金financially supported by the Shaanxi Innovation Team Project,China (2024RS-CXTD-73)the National Natural Science Foundation of China (31772136)。
文摘The strawberry crimp nematode(Aphelenchoides fragariae) is a serious pathogen of ornamental crops and a significant quarantine concern in approximately 50 countries and regions,including China.A nematode population belonging to the genus Aphelenchoides was isolated from symptomatic leaves of fuchsia plants(Fuchsia×hybrida Hort.ex Sieb.& Voss.) in Chengdu,Sichuan Province,China.Morphological and morphometric characteristics were determined using light microscopy and scanning electron microscopy.Detailed examination revealed diagnostic features consistent with A.fragariae.Three ribosomal DNA(rDNA) regions,i.e.,partial small subunit(SSU) rRNA,D2-D3 expansion segments of the large subunit(LSU) rRNA,and the internal transcribed spacer(ITS),were amplified and sequenced.Bayesian phylogenetic analyses based on these sequences placed the isolate in a well-supported monophyletic clade with reference A.fragariae specimens,clearly separated from other Aphelenchoides species.Furthermore,host-suitability assays demonstrated that this nematode population not only infects and reproduces on Fuchsia×hybrida,but also on Fragaria ananassa and Pteris vittata,two known hosts of A.fragariae.Collectively,morphological,molecular,and host-range evidence confirm the identification of this nematode as A.fragariae.To our knowledge,this represents the first molecular and morphological confirmation of A.fragariae in China,and the first report of Fuchsia×hybrida as a natural host for this species.
文摘Securing restricted zones such as airports,research facilities,and military bases requires robust and reliable access control mechanisms to prevent unauthorized entry and safeguard critical assets.Face recognition has emerged as a key biometric approach for this purpose;however,existing systems are often sensitive to variations in illumination,occlusion,and pose,which degrade their performance in real-world conditions.To address these challenges,this paper proposes a novel hybrid face recognition method that integrates complementary feature descriptors such as Fuzzy-Gabor 2D Fisher Linear Discriminant(FG-2DFLD),Generalized 2D Linear Discriminant Analysis(G2DLDA),andModular-Local Binary Patterns(Modular-LBP)with Dempster–Shafer(DS)evidence theory for decision fusion.The proposed framework extracts global,structural,and local texture features,models them using Gaussian distributions to estimate belief factors,and fuses these belief factors through DS theory to explicitly handle uncertainty and conflict among descriptors.Experimental validation was performed on two widely used benchmark datasets,ORL and Cropped Yale B,achieving recognition rates exceeding 98%,which outperform traditional methods as well as recent deep learning-based approaches.Furthermore,the method demonstrated strong robustness under noisy conditions,maintaining accuracies above 96%with salt-and-pepper and Gaussian noise.These results highlight the effectiveness of the proposed integration strategy in enhancing accuracy,reliability,and resilience compared to single-descriptor and conventional fusion methods.Given its high performance and efficiency,the proposed method shows strong potential for deployment in real-world restricted-zone applications such as smart parking systems,secure facility access,and other high-security domains.
基金support from the Sichuan Science and Technology Program(2024JDHJ0043 and 2025YFHZ0121).
文摘Recently,Tian et al.published a research paper with significant breakthroughs in Cell[1].The study found that targeting the signalling pathways named Serpine2-lowdensity lipoprotein receptor-related protein 1(Lrp1)and ectonucleoside triphosphate diphosphohydrolase 1(CD39)-adenosine A_(3)receptor(A_(3)AR)is a promising strategy for the treatment of vascular dementia.The Serpine2-Lrp1 signalling pathway primarily exerts its therapeutic effects on myelin regeneration by regulating the differentiation of oligodendrocyte precursor cells.Serpine2 is a secretory serine protease inhibitor regulates proteolytic homeostasis.It may also bind to cell surface receptors such as Lrp1 to directly activate signalling pathways.As a transmembrane glycoprotein receptor,Lrpl mediates the endocytic clearance of ligands.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2601).
文摘Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows raises concerns,particularly the risk of subtle distortions or hallucinations that may undermine diagnostic accuracy and weaken trust in AI-assisted decision-making.To address this challenge,we propose a hybrid deep learning framework designed to detect GAN-induced artifacts in medical images,thereby reinforcing the reliability of AI-driven diagnostics.The framework integrates low-level statistical descriptors,including high-frequency residuals and Gray-Level Co-occurrence Matrix(GLCM)texture features,with high-level semantic representations extracted from a pre-trained ResNet18.This dual-stream approach enables detection of both pixel-level anomalies and structural inconsistencies introduced by GAN-based manipulation.We validated the framework on a curated dataset of 10,000 medical images,evenly split between authentic and GAN-generated samples across four modalities:MRI,CT,X-ray,and fundus photography.To improve generalizability to real-world clinical settings,we incorporated domain adaptation strategies such as adversarial training and style transfer,reducing domain shift by 15%.Experimental results demonstrate robust performance,achieving 92.6%accuracy and an F1-score of 0.91 on synthetic test data,and maintaining strong performance on real-world GAN-modified images with 87.3%accuracy and an F1-score of 0.85.Additionally,the model attained an AUC of 0.96 and an average precision of 0.92,outperforming conventional GAN detection pipelines and baseline Convolutional Neural Network(CNN)architectures.These findings establish the proposed framework as an effective and reliable solution for detecting GAN-induced hallucinations in medical imaging,representing an important step toward building trustworthy and clinically deployable AI systems.
基金supported by the Else Kröner-Fresenius-Stiftung(2021-EKSE.95)the Deutsche Forschungsgemeinschaft(CRC1678 and Germany’s Excellence Strategy-CECAD,EXC 2030-390661388)(to DV).
文摘Amyotrophic lateral sclerosis(ALS)is a fatal,late-onset neurodegenerative disorder characterized by the progressive degeneration of motor neurons in the motor cortex,brainstem,and spinal cord(Feldman et al.,2022).
文摘Lung cancer remains a major global health challenge,with early diagnosis crucial for improved patient survival.Traditional diagnostic techniques,including manual histopathology and radiological assessments,are prone to errors and variability.Deep learning methods,particularly Vision Transformers(ViT),have shown promise for improving diagnostic accuracy by effectively extracting global features.However,ViT-based approaches face challenges related to computational complexity and limited generalizability.This research proposes the DualSet ViT-PSO-SVM framework,integrating aViTwith dual attentionmechanisms,Particle Swarm Optimization(PSO),and SupportVector Machines(SVM),aiming for efficient and robust lung cancer classification acrossmultiple medical image datasets.The study utilized three publicly available datasets:LIDC-IDRI,LUNA16,and TCIA,encompassing computed tomography(CT)scans and histopathological images.Data preprocessing included normalization,augmentation,and segmentation.Dual attention mechanisms enhanced ViT’s feature extraction capabilities.PSO optimized feature selection,and SVM performed classification.Model performance was evaluated on individual and combined datasets,benchmarked against CNN-based and standard ViT approaches.The DualSet ViT-PSO-SVM significantly outperformed existing methods,achieving superior accuracy rates of 97.85%(LIDC-IDRI),98.32%(LUNA16),and 96.75%(TCIA).Crossdataset evaluations demonstrated strong generalization capabilities and stability across similar imagingmodalities.The proposed framework effectively bridges advanced deep learning techniques with clinical applicability,offering a robust diagnostic tool for lung cancer detection,reducing complexity,and improving diagnostic reliability and interpretability.
基金funded by the Ministry of Higher Education(MoHE)Malaysia through the Fundamental Research Grant Scheme—Early Career Researcher(FRGS-EC),grant number FRGSEC/1/2024/ICT02/UNIMAP/02/8.
文摘critical for guiding treatment and improving patient outcomes.Traditional molecular subtyping via immuno-histochemistry(IHC)test is invasive,time-consuming,and may not fully represent tumor heterogeneity.This study proposes a non-invasive approach using digital mammography images and deep learning algorithm for classifying breast cancer molecular subtypes.Four pretrained models,including two Convolutional Neural Networks(MobileNet_V3_Large and VGG-16)and two Vision Transformers(ViT_B_16 and ViT_Base_Patch16_Clip_224)were fine-tuned to classify images into HER2-enriched,Luminal,Normal-like,and Triple Negative subtypes.Hyperparameter tuning,including learning rate adjustment and layer freezing strategies,was applied to optimize performance.Among the evaluated models,ViT_Base_Patch16_Clip_224 achieved the highest test accuracy(94.44%),with equally high precision,recall,and F1-score of 0.94,demonstrating excellent generalization.MobileNet_V3_Large achieved the same accuracy but showed less training stability.In contrast,VGG-16 recorded the lowest performance,indicating a limitation in its generalizability for this classification task.The study also highlighted the superior performance of the Vision Transformer models over CNNs,particularly due to their ability to capture global contextual features and the benefit of CLIP-based pretraining in ViT_Base_Patch16_Clip_224.To enhance clinical applicability,a graphical user interface(GUI)named“BCMS Dx”was developed for streamlined subtype prediction.Deep learning applied to mammography has proven effective for accurate and non-invasive molecular subtyping.The proposed Vision Transformer-based model and supporting GUI offer a promising direction for augmenting diagnostic workflows,minimizing the need for invasive procedures,and advancing personalized breast cancer management.
基金support provided by the European University of Atlantic.
文摘Human pose estimation is crucial across diverse applications,from healthcare to human-computer interaction.Integrating inertial measurement units(IMUs)with monocular vision methods holds great potential for leveraging complementary modalities;however,existing approaches are often limited by IMU drift,noise,and underutilization of visual information.To address these limitations,we propose a novel dual-stream feature extraction framework that effectively combines temporal IMU data and single-view image features for improved pose estimation.Short-term dependencies in IMU sequences are captured with convolutional layers,while a Transformerbased architecture models long-range temporal dynamics.To mitigate IMU drift and inter-sensor inconsistencies,a complementary filtering module is introduced alongside a cross-channel interaction mechanism.Features from the IMU and image streams are then fused via a dedicated fusion module and further refined utilizing a high-precision regression head for accurate pose prediction.Experimental results on benchmark datasets demonstrate that our method significantly outperforms existing techniques in terms of estimation,accuracy,and robustness,validating the effectiveness of our dual-stream architecture.
基金supported by funds of the Ministerio de Ciencia, Innovación y Universidadesco-financed by European Regional Development Fund-FEDER “A way to make Europe” (Project Ref:PID2023-153309OB-I00) supported by MCIN/AEl/ 10.13039/501100011033/ FEDER, UE+10 种基金Ministerio de Ciencia, Innovación y Universidades through Instituto de Salud Carlos Ⅲ and European Regional Development Funds “A way to make Europe” (PI17/00136, PI20/00076)European Union Horizon 2020 program (grant agreement #874889, HEALIKICK) to F. Granero-MoltóNext Generation EU, Plan de Recuperación, Transformación y Resiliencia RICORS TERAV ISCIII (RD21/0017/0009)H2020-MSCA-RISE-2019 (grant agreement #872648, MEPHOS) to F. Próspersupported by a fellowship from “Asociación de Amigos de la Universidad de Navarra”supported by a fellowship CIMA AC from “Fundación para la Investigación Médica Aplicada”funded by grants PID2022-104776RB-100 and CB16/11/00399 (CIBER CV) from MCIN/AEI/10.13039/501100011033La Caixa Research Health Foundation (Ref. HR23-00084)supported by a fellowship of “Asociación de Amigos de la Universidad de Navarra” and “Obra Social La Caixa”the research leading to these results has received funding from “la Caixa” Banking Foundationsupported by a Sara Borrell grant (CD22/00027) from the Instituto Carlos Ⅲ and Next Generation EU。
文摘After injury,bone tissue initiates a reparative response to restore its structure and function.The failure to initiate or delay this response could result in fracture nonunion.The molecular mechanisms underlying the occurrence of fracture nonunion are not yet established.We propose that hypoxia-triggered signaling pathways,mediated by reactive oxygen species(ROS)homeostasis,control Bmp2 expression and fracture healing initiation.The excessive ROS leads to oxidative stress and,ultimately,fracture nonunion.In this study,we silenced Apex1,the final ROS signaling transducer that mediates the activation of key transcription factors by their cysteines oxidoreduction,evaluating its role during endochondral ossification and fracture repair.Silencing Apex1 in limb bud mesenchyme results in transient metaphyseal dysplasia derived from impaired chondrocyte differentiation.During bone regeneration,Apex1 silencing induces a fracture nonunion phenotype,characterized by delayed fracture repair initiation,impaired periosteal response,and reduced chondrocyte and osteoblast differentiation.This compromised chondrocyte differentiation hampers callus vascularization and healing progression.Our findings highlight a critical mechanism where hypoxia-driven ROS signaling in mesenchymal progenitors through APEX1 is essential for fracture healing initiation.
文摘Little is known about how chronic inflammation contributes to the progression of hepatoceUular carcinoma （HCC）, especially the initiation of cancer. To uncover the critical transition from chronic inflammation to HCC and the molecular mechanisms at a network level, we analyzed the time-series proteomic data of woodchuck hepatitis virus/c.myc mice and age-matched wt-C57BL/6 mice using our dynamical network biomarker （DNB） model. DNB analysis indicated that the 5th month after birth of transgenic mice was the critical period of cancer initiation, just before the critical transition, which is consistent with clinical symptoms. Meanwhile, the DNB-associated network showed a drastic inversion of protein expression and coexpression levels before and after the critical transition. Two members of DNB, PLA2G6 and CYP2C44, along with their associated differentially expressed proteins, were found to induce dysfunction of arachidonic acid metabolism, further activate inflammatory responses through inflammatory mediator regulation of transient receptor potential channels, and finally lead to impairments of liver detoxification and malignant transition to cancer. As a c-Myc target, PLA2G6 positively correlated with c-Myc in expression, showing a trend from decreasing to increasing during carcinogenesis, with the minimal point at the critical transition or tipping point. Such trend of homologous PLA2G6 and c-Myc was also observed during human hepatocarcinogenesis, with the minimal point at high-grade dysplastic nodules （a stage just before the carcinogenesis）. Our study implies that PLA2G6 might function as an oncogene like famous c-Myc during hepatocar- cinogenesis, while downregulation of PLA2G6 and c-Myc could be a warning signal indicating imminent carcinogenesis.
基金supported by the Joint Fund for Fostering Talents of National Natural Science Foundation of China and Henan Province(Grant No.U1304618)the Open Fund of Key Laboratory of Precision Navigation and Timing Technology of Chinese Academy of Sciences(Grant No.2012PNTT01)+4 种基金the Postdoctoral Grant of China(Grant Nos.2011M501446,2012T50798)the Basic and Advanced Technology Research Foundation of Henan Province(Grant No.122300410169)The Key Science and Technology Foundation of Zhengzhou City(Grant Nos.131PPTGG411-6,131PCXTD594)the Doctor Fund of Zhengzhou University of Light Industry(Grant No.2011BSJJ031)the Fundamental Research Funds for the Central Universities(Grant No.K5051204003)
文摘The phase group synchronization between any signals is further revealed,which is based on proposing the new concepts of the greatest common factor frequency,the least common multiple period,quantized phase shift resolution,equivalent phase comparison frequency and so on.Then the problem of phase comparison and processing between different frequency signals is solved and shown in detail.Using the basic principle and the variation law of group phase difference,the frequency stability better than 10-14/s can be easily obtained in the time&frequency measurement and control domain,and experimental results also show the phase relations between atomic energy level transition signal and the locked crystal oscillator signal in an active hydrogen atomic clock are strict phase group synchronization,and locked precision with 10-13/s can be reached based on phase group synchronization.The phase group synchronization can provide technical support to frequency linking among radio frequency,microwave and light frequency.
文摘Prostate cancer(PCa)remains a major cause of cancer-related mortality in men,largely due to therapy resistance and metastatic progression.Increasing evidence highlights the tumor microenvironment(TME),particularly cancer-associated fibroblasts(CAFs),as a critical determinant of disease behavior.CAFs constitute a heterogeneous population originating from fibroblasts,mesenchymal stem cells,endothelial cells,epithelial cells undergoing epithelial-mesenchymal transition(EMT),and adipose tissue.Through dynamic crosstalk with tumor,immune,endothelial,and adipocyte compartments,CAFs orchestrate oncogenic processes including tumor proliferation,invasion,immune evasion,extracellular matrix remodeling,angiogenesis,and metabolic reprogramming.This review comprehensively summarizes the cellular origins,phenotypic and functional heterogeneity,and spatial distribution of CAFs within the prostate TME.We further elucidate the molecular mechanisms by which CAFs regulate PCa progression and therapeutic resistance,and critically evaluate emerging strategies to therapeutically target CAFmediated signaling,metabolic,and immune pathways.By integrating recent advances from single-cell and spatial transcriptomics(ST),our objective is to provide a holistic framework for understanding CAF biology and to highlight potential avenues for stromal reprogramming as an adjunct to current PCa therapies.
