The accurate segmentation of deep gray matter nuclei is critical for neuropathological research,disease diagnosis and treatment.Existing methods employ the supervised learning training approach,which requires large la...The accurate segmentation of deep gray matter nuclei is critical for neuropathological research,disease diagnosis and treatment.Existing methods employ the supervised learning training approach,which requires large labeled datasets.It is challenging and time-consuming to obtain such datasets for medical image analysis.In addition,these methods based on convolutional neural networks(CNNs)only achieve suboptimal performance due to the locality of convolutional operations.Vision Transformers(ViTs)efficiently model long-range dependencies and thus have the potentiality to outperform these methods in segmentation tasks.To address these issues,we propose a novel hybrid network based on self-supervised pre-training for deep gray matter nuclei segmentation.Specifically,we present a CNN-Transformer hybrid network(CTNet),whose encoder consists of 3D CNN and ViT to learn local spatial-detailed features and global semantic information.A self-supervised learning(SSL)approach that integrates rotation prediction and masked feature reconstruction is proposed to pre-train the CTNet,enabling the model to learn valuable visual representations from unlabeled data.We evaluate the effectiveness of our method on 3T and 7T human brain MRI datasets.The results demonstrate that our CTNet achieves better performance than other comparison models and our pre-training strategy outperforms other advanced self-supervised methods.When the training set has only one sample,our pre-trained CTNet enhances segmentation performance,showing an 8.4%improvement in Dice similarity coefficient(DSC)compared to the randomly initialized CTNet.展开更多
The collection and annotation of lar ge-scale bird datasets are resource-intensive and time-consuming processes that significantly limit the scalability and accuracy of biodiversity monitoring systems.While self-super...The collection and annotation of lar ge-scale bird datasets are resource-intensive and time-consuming processes that significantly limit the scalability and accuracy of biodiversity monitoring systems.While self-supervised learning(SSL)has emerged as a promising approach for leveraging unannotated data,current SSL methods face two critical challenges in bird species recognition:(1)long-tailed data distributions that result in poor performance on underrepresented species;and(2)domain shift issues caused by data augmentation strategies designed to mitigate class imbalance.Here we present SDNet,a novel SSL-based bird recognition framework that integrates diffusion models with large language models(LLMs)to overcome these limitations.SDNet employs LLMs to generate semantically rich textual descriptions for tail-class species by prompting the models with species taxonomy,morphological attributes,and habitat information,producing detailed natural language priors that capture fine-grained visual characteristics(e.g.,plumage patterns,body proportions,and distinctive markings).These textual descriptions are subsequently used by a conditional diffusion model to synthesize new bird image samples through cross-attention mechanisms that fuse textual embeddings with intermediate visual feature representations during the denoising process,ensuring generated images preserve species-specific morphological details while maintaining photorealistic quality.Additionally,we incorporate a Swin Transformer as the feature extraction backbone whose hierarchical window-based attention mechanism and shifted windowing scheme enable multi-scale local feature extraction that proves particularly effective at capturing finegrained discriminative patterns(such as beak shape and feather texture)while mitigating domain shift between synthetic and original images through consistent feature representations across both data sources.SDNet is validated on both a self-constructed dataset(Bird_BXS)an d a publicly available benchmark(Birds_25),demonstrating substantial improvements over conventional SSL approaches.Our results indicate that the synergistic integration of LLMs,diffusion models,and the Swin Transformer architecture contributes significantly to recognition accuracy,particularly for rare and morphologically similar species.These findings highlight the potential of SDNet for addressing fundamental limitations of existing SSL methods in avian recognition tasks and establishing a new paradigm for efficient self-supervised learning in large-scale ornithological vision applications.展开更多
Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This st...Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.展开更多
Recent years have witnessed significant progress in deep learning for remote sensing image Super-Resolution(SR).However,in real-world applications,paired data is often unavailable,making supervised training infeasible...Recent years have witnessed significant progress in deep learning for remote sensing image Super-Resolution(SR).However,in real-world applications,paired data is often unavailable,making supervised training infeasible,while unknown degradation factors constrain reconstruction performance and impair detail recovery.To this end,we propose a Degradation-Adaptive Self-supervised SR method,named DASSR,which recovers high-fidelity details from low-resolution remote sensing images without requiring supervision from high-resolution groundtruth.DASSR employs a dual-path closed-loop architecture,enabling joint learning of SR reconstruction and blur kernel estimation through cycle consistency in the main branch and regularization in the auxiliary branch.Specifically,we incorporate an Edge-Preserving SR network(EPSRN)into DASSR,whose core Hybrid Attention Enhancement Block(HAEB)captures precise structural representations to guide accurate detail reconstruction.Furthermore,a composite loss function is designed,integrating spatial reconstruction consistency,frequencydomain spectrum alignment,and kernel sparsity constraints to ensure stable and efficient self-supervised learning.Experiments on both simulated and real-world remote sensing datasets demonstrate that the proposed DASSR method outperforms competitive deep learning-based SR methods,notably achieving approximately 9%and 15%improvements in the Average Gradient(AG)and Spatial Frequency(SF)metrics,respectively,over the best-performing competitor.展开更多
Computed Tomography(CT)reconstruction is essential inmedical imaging and other engineering fields.However,blurring of the projection during CT imaging can lead to artifacts in the reconstructed images.Projection blur ...Computed Tomography(CT)reconstruction is essential inmedical imaging and other engineering fields.However,blurring of the projection during CT imaging can lead to artifacts in the reconstructed images.Projection blur combines factors such as larger ray sources,scattering and imaging system vibration.To address the problem,we propose DeblurTomo,a novel self-supervised learning-based deblurring and reconstruction algorithm that efficiently reconstructs sharp CT images from blurry input without needing external data and blur measurement.Specifically,we constructed a coordinate-based implicit neural representation reconstruction network,which can map the coordinates to the attenuation coefficient in the reconstructed space formore convenient ray representation.Then,wemodel the blur as aweighted sumof offset rays and design the RayCorrectionNetwork(RCN)andWeight ProposalNetwork(WPN)to fit these rays and their weights bymulti-view consistency and geometric information,thereby extending 2D deblurring to 3D space.In the training phase,we use the blurry input as the supervision signal to optimize the reconstruction network,the RCN,and the WPN simultaneously.Extensive experiments on the widely used synthetic dataset show that DeblurTomo performs superiorly on the limited-angle and sparse-view in the simulated blurred scenarios.Further experiments on real datasets demonstrate the superiority of our method in practical scenarios.展开更多
Blended acquisition offers efficiency improvements over conventional seismic data acquisition, at the cost of introducing blending noise effects. Besides, seismic data often suffers from irregularly missing shots caus...Blended acquisition offers efficiency improvements over conventional seismic data acquisition, at the cost of introducing blending noise effects. Besides, seismic data often suffers from irregularly missing shots caused by artificial or natural effects during blended acquisition. Therefore, blending noise attenuation and missing shots reconstruction are essential for providing high-quality seismic data for further seismic processing and interpretation. The iterative shrinkage thresholding algorithm can help obtain deblended data based on sparsity assumptions of complete unblended data, and it characterizes seismic data linearly. Supervised learning algorithms can effectively capture the nonlinear relationship between incomplete pseudo-deblended data and complete unblended data. However, the dependence on complete unblended labels limits their practicality in field applications. Consequently, a self-supervised algorithm is presented for simultaneous deblending and interpolation of incomplete blended data, which minimizes the difference between simulated and observed incomplete pseudo-deblended data. The used blind-trace U-Net (BTU-Net) prevents identity mapping during complete unblended data estimation. Furthermore, a multistep process with blending noise simulation-subtraction and missing traces reconstruction-insertion is used in each step to improve the deblending and interpolation performance. Experiments with synthetic and field incomplete blended data demonstrate the effectiveness of the multistep self-supervised BTU-Net algorithm.展开更多
Feature fusion is an important technique in medical image classification that can improve diagnostic accuracy by integrating complementary information from multiple sources.Recently,Deep Learning(DL)has been widely us...Feature fusion is an important technique in medical image classification that can improve diagnostic accuracy by integrating complementary information from multiple sources.Recently,Deep Learning(DL)has been widely used in pulmonary disease diagnosis,such as pneumonia and tuberculosis.However,traditional feature fusion methods often suffer from feature disparity,information loss,redundancy,and increased complexity,hindering the further extension of DL algorithms.To solve this problem,we propose a Graph-Convolution Fusion Network with Self-Supervised Feature Alignment(Self-FAGCFN)to address the limitations of traditional feature fusion methods in deep learning-based medical image classification for respiratory diseases such as pneumonia and tuberculosis.The network integrates Convolutional Neural Networks(CNNs)for robust feature extraction from two-dimensional grid structures and Graph Convolutional Networks(GCNs)within a Graph Neural Network branch to capture features based on graph structure,focusing on significant node representations.Additionally,an Attention-Embedding Ensemble Block is included to capture critical features from GCN outputs.To ensure effective feature alignment between pre-and post-fusion stages,we introduce a feature alignment loss that minimizes disparities.Moreover,to address the limitations of proposed methods,such as inappropriate centroid discrepancies during feature alignment and class imbalance in the dataset,we develop a Feature-Centroid Fusion(FCF)strategy and a Multi-Level Feature-Centroid Update(MLFCU)algorithm,respectively.Extensive experiments on public datasets LungVision and Chest-Xray demonstrate that the Self-FAGCFN model significantly outperforms existing methods in diagnosing pneumonia and tuberculosis,highlighting its potential for practical medical applications.展开更多
Self-supervised monocular depth estimation has emerged as a major research focus in recent years,primarily due to the elimination of ground-truth depth dependence.However,the prevailing architectures in this domain su...Self-supervised monocular depth estimation has emerged as a major research focus in recent years,primarily due to the elimination of ground-truth depth dependence.However,the prevailing architectures in this domain suffer from inherent limitations:existing pose network branches infer camera ego-motion exclusively under static-scene and Lambertian-surface assumptions.These assumptions are often violated in real-world scenarios due to dynamic objects,non-Lambertian reflectance,and unstructured background elements,leading to pervasive artifacts such as depth discontinuities(“holes”),structural collapse,and ambiguous reconstruction.To address these challenges,we propose a novel framework that integrates scene dynamic pose estimation into the conventional self-supervised depth network,enhancing its ability to model complex scene dynamics.Our contributions are threefold:(1)a pixel-wise dynamic pose estimation module that jointly resolves the pose transformations of moving objects and localized scene perturbations;(2)a physically-informed loss function that couples dynamic pose and depth predictions,designed to mitigate depth errors arising from high-speed distant objects and geometrically inconsistent motion profiles;(3)an efficient SE(3)transformation parameterization that streamlines network complexity and temporal pre-processing.Extensive experiments on the KITTI and NYU-V2 benchmarks show that our framework achieves state-of-the-art performance in both quantitative metrics and qualitative visual fidelity,significantly improving the robustness and generalization of monocular depth estimation under dynamic conditions.展开更多
Few-shot learning has emerged as a crucial technique for coral species classification,addressing the challenge of limited labeled data in underwater environments.This study introduces an optimized few-shot learning mo...Few-shot learning has emerged as a crucial technique for coral species classification,addressing the challenge of limited labeled data in underwater environments.This study introduces an optimized few-shot learning model that enhances classification accuracy while minimizing reliance on extensive data collection.The proposed model integrates a hybrid similarity measure combining Euclidean distance and cosine similarity,effectively capturing both feature magnitude and directional relationships.This approach achieves a notable accuracy of 71.8%under a 5-way 5-shot evaluation,outperforming state-of-the-art models such as Prototypical Networks,FEAT,and ESPT by up to 10%.Notably,the model demonstrates high precision in classifying Siderastreidae(87.52%)and Fungiidae(88.95%),underscoring its effectiveness in distinguishing subtle morphological differences.To further enhance performance,we incorporate a self-supervised learning mechanism based on contrastive learning,enabling the model to extract robust representations by leveraging local structural patterns in corals.This enhancement significantly improves classification accuracy,particularly for species with high intra-class variation,leading to an overall accuracy of 76.52%under a 5-way 10-shot evaluation.Additionally,the model exploits the repetitive structures inherent in corals,introducing a local feature aggregation strategy that refines classification through spatial information integration.Beyond its technical contributions,this study presents a scalable and efficient approach for automated coral reef monitoring,reducing annotation costs while maintaining high classification accuracy.By improving few-shot learning performance in underwater environments,our model enhances monitoring accuracy by up to 15%compared to traditional methods,offering a practical solution for large-scale coral conservation efforts.展开更多
Intelligent Transportation Systems(ITS)leverage Integrated Sensing and Communications(ISAC)to enhance data exchange between vehicles and infrastructure in the Internet of Vehicles(IoV).This integration inevitably incr...Intelligent Transportation Systems(ITS)leverage Integrated Sensing and Communications(ISAC)to enhance data exchange between vehicles and infrastructure in the Internet of Vehicles(IoV).This integration inevitably increases computing demands,risking real-time system stability.Vehicle Edge Computing(VEC)addresses this by offloading tasks to Road Side Units(RSUs),ensuring timely services.Our previous work,the FLSimCo algorithm,which uses local resources for federated Self-Supervised Learning(SSL),has a limitation:vehicles often can’t complete all iteration tasks.Our improved algorithm offloads partial tasks to RSUs and optimizes energy consumption by adjusting transmission power,CPU frequency,and task assignment ratios,balancing local and RSU-based training.Meanwhile,setting an offloading threshold further prevents inefficiencies.Simulation results show that the enhanced algorithm reduces energy consumption and improves offloading efficiency and accuracy of federated SSL.展开更多
Maize yield is critically endangered by diseases throughout its growth cycle,posing significant risks to food security.The spatial and temporal dynamics of maize yield loss and the rate of yield loss attributable to t...Maize yield is critically endangered by diseases throughout its growth cycle,posing significant risks to food security.The spatial and temporal dynamics of maize yield loss and the rate of yield loss attributable to these threats on a regional scale have been challenging to ascertain due to scarce continuous observation data.This study compiled county-level data on maize yield and yield loss across China's six primary cropping regions over twenty years from 1999 to 2018.These include the Spring-sown area of Northern China(1-NC),the Summer-sown Huang-Huai-Hai Plain(2-HHP),the Southwest Mountain(3-SM),the Southern Hilly(4-SH),the Northwest Irrigated(5-NI),and the Qinghai-Tibet Plateau Maize Regions(6-QTP).We identified 15 major diseases affecting these regions.The annual average yield loss due to maize diseases in the regions 1-NC,2-HHP,3-SM,4-SH,5-NI,and 6-QTP were 0.40,0.58,0.12,0.05,0.04 and<0.01 million tons,respectively,and the corresponding average yield loss rate(the ratio of yield loss to total yield)in these regions was 0.63,0.90,0.65,0.63,0.44,and 0.05.The yield loss due to all diseases increased for three regions in 3-SM,4-SH and 5-NI.The yield loss rate due to diseases significantly increased in region 4-SH and 5-NI.Predominantly,maize leaf blight has become the most significant threats.In region 1-NC,maize head smut(D1)and maize leaf blight(D2)were the primary diseases.In region 2-HHP,maize leaf blight(D2),maize rust(D3),maize brown spot(D5),Curvularia leaf spot(D7),and maize virus disease(D14)were the key pathogens.Bivariate trend analysis(joint analysis of yield loss and loss rate trends)indicated that maize head smut(D1)decreased significantly in 1-NC,while in 2-HHP,six diseases showed a significant decrease in both yield loss and loss rate,namely sheath blight(D4),brown spot(D5),root rot(D11),downy mildew(D12)and virus disease(D14).By providing a long-term,national-scale perspective,this study not only supports the development of broad management strategies but also guides the creation of precise,region-specific control protocols to safeguard maize production.展开更多
Tinnitus:the hearing of a sound that has not been produced by any external or internal source,is a rather heterogeneous hearing disorder.Background/Objectives:Hearing loss has been shown to be the main risk factor for...Tinnitus:the hearing of a sound that has not been produced by any external or internal source,is a rather heterogeneous hearing disorder.Background/Objectives:Hearing loss has been shown to be the main risk factor for tinnitus while emotional disorders are risk factors for developing intrusive or bothersome tinnitus.Moreover,aging has also been identified as another risk factor.The aim of this paper was to analyse the correlation between hearing loss,age and tinnitus severity in a cohort of 610 tinnitus sufferers.Methods:Age,audiometric(hearing condition)and tinnitus(time duration and severity)data were assessed and analysed for all subjects just after recruiting(baseline).Furthermore,the average hearing loss(HL)curves of the participants for age groups were compared to these with the corresponding Age Related HL(ARHL).Results:For most of the age groups,the measured HL curves exceeded in 10-20 dB those of the ARHL.The average age of tinnitus onset(age minus tinnitus duration)was found to be 44-46 years in both men and women.Weak correlation between audiometric feature and tinnitus distress was observed.Conclusions:Hearing loss has been shown to be a clear risk factor for triggering tinnitus(86%of participants were hearing impaired).In this cohort,average measures of hearing loss showed,at most,weak associations with tinnitus-related distress,suggesting that non-audiological factors may play a predominant role.展开更多
Background:Brucellosis is a zoonotic infection common in Mediterranean countries and the Middle East.Neurological involvement,although rare,can lead to severe complications,including sensorineural hearing loss(SNHL).T...Background:Brucellosis is a zoonotic infection common in Mediterranean countries and the Middle East.Neurological involvement,although rare,can lead to severe complications,including sensorineural hearing loss(SNHL).This case is particularly noteworthy as it highlights irreversible auditory nerve damage in brucellosis,emphasizing the importance of early diagnosis and treatment to prevent permanent neurological consequences.The novelty of this case lies in the severity of auditory involvement despite timely treatment.Case Presentation:A 43-year-old male farmer of Maghrebi origin presented with neurobrucellosis complicated by severe,irreversible bilateral sensorineural hearing loss.The patient initially reported symptoms of hearing loss and dizziness,which were confirmed to be associated with auditory nerve involvement.Wright's serology and polymerase chain reaction(PCR)testing confirmed brucellosis.Despite appropriate and prolonged antibiotic therapy,including drugs that penetrate the meningeal barrier and act intracellularly,the patient's auditory impairment remained permanent.The patient is currently a candidate for cochlear implantation to manage his severe hearing loss.Neurological symptoms did not improve with treatment,but cochlear implantation may offer a potential solution to his hearing deficit.Conclusions:This case highlights the importance of early recognition and intervention in brucellosis cases,particularly those with neurological involvement.Delayed diagnosis and treatment can result in irreversible neurological damage.It also underscores the potential for cochlear implantation in patients with severe,irreversible sensorineural hearing loss caused by neurobrucellosis.Cochlear implantation offers an important solution for patients with brucellosis-related hearing deficits,improving their quality of life despite the neurological damage caused by the infection.展开更多
Recurrent pregnancy loss(RPL)affects 2%-5%of couples attempting to conceive.It is a highly heterogenous condition attributed to several factors including endocrine dysfunction,auto immune disorders,thrombophilia,genet...Recurrent pregnancy loss(RPL)affects 2%-5%of couples attempting to conceive.It is a highly heterogenous condition attributed to several factors including endocrine dysfunction,auto immune disorders,thrombophilia,genetic abnormalities,infectious diseases,uterine anomalies,sperm DNA fragmentation,and epigenetics.Among genetic causes,chromosomal abnormalities are the most frequent etiological factor of early miscarriage,accounting for 50%–60%of first trimester abortions.Numerical or structural chromosomal changes may result in spontaneous miscarriages.These anomalies arise as a result of chromosomal translocation,non-disjunction,or mutations[1].Transmission of parental chromosomal abnormalities may be one of the chances for a recurrence of miscarriage in the first trimester of pregnancy,albeit the cause is unknown[2,3].展开更多
With the increasing penetration of renewable energy,the coordination of energy storage with thermal power for frequency regulation has become an effective means to enhance grid frequency security.Addressing the challe...With the increasing penetration of renewable energy,the coordination of energy storage with thermal power for frequency regulation has become an effective means to enhance grid frequency security.Addressing the challenge of improving the frequency regulation performance of a thermal-storage primary frequency regulation system while reducing its associated losses,this paper proposes a multi-dimensional cooperative optimization strategy for the control parameters of a combined thermal-storage system,considering regulation losses.First,the frequency regulation losses of various components within the thermal power unit are quantified,and a calculation method for energy storage regulation loss is proposed,based on Depth of Discharge(DOD)and C-rate.Second,a thermal-storage cooperative control method based on series compensation is developed to improve the system’s frequency regulation performance.Third,targeting system regulation loss cost and regulation output,and considering constraints on output overshoot and system parameters,an improved Particle Swarm Optimization(PSO)algorithm is employed to tune the parameters of the low-pass filter and the series compensator,thereby reducing regulation losses while enhancing performance.Finally,simulation results demonstrate that the total loss cost of the proposed control strategy is comparable to that of a system with only thermal power participation.However,the thermal power loss cost is reduced by 42.16%compared to the thermal-only case,while simultaneously improving system frequency stability.Thus,the proposed strategy effectively balances system frequency stability and economic efficiency.展开更多
Objective:To investigate the potential link between chromosomal polymorphisms in couples who had a medical history of idiopathic recurrent pregnancy loss.Methods:Cytogenetic investigation was conducted with mitogen(Ph...Objective:To investigate the potential link between chromosomal polymorphisms in couples who had a medical history of idiopathic recurrent pregnancy loss.Methods:Cytogenetic investigation was conducted with mitogen(Phytohemagglutinin-M,Gibco)stimulated blood T lymphocytes by Giemsa trypsin Giemsa banding and Ag-NOR banding on 580 couples with a history of idiopathic recurrent pregnancy loss and 240 couples from the general population.Thirty good chromosomal spreads were captured,karyotyped,and analyzed.The karyotypes were designated using the International System for Human Cytogenomic Nomenclature 2024.Pearson Chi-square test was used to compare the frequency of chromosomal polymorphism variations in the idiopathic recurrent pregnancy loss group with the general population group.Results:A conventional cytogenetic investigation revealed that 45.43%of couples experiencing idiopathic recurrent pregnancy loss presented with various types of chromosomal polymorphic variants,compared to 11.88%in the general population.The overall frequency of these chromosomal polymorphic variants was significantly higher in the idiopathic recurrent pregnancy loss group compared to the general population group(OR 9.97,95%CI 6.99-14.21;P<0.05).Additionally,the prevalence of polymorphic variants was higher among males(49.14%)than females(41.72%)(P=0.01).Conclusions:Chromosomal polymorphic analysis may play a crucial role in the assessment and careful clinical management of cases with idiopathic recurrent pregnancy loss,especially when no other conclusive reasons are identified during the initial evaluation.Therefore,heteromorphism should not be overlooked while investigating the causes of idiopathic recurrent pregnancy loss.展开更多
Circadian sensitivity significantly influences the severity of noise-induced hearing loss(NIHL),but the underlying mechanisms remain unclear.Here,we applied single-cell RNA sequencing to 97,043 cochlear cells,identify...Circadian sensitivity significantly influences the severity of noise-induced hearing loss(NIHL),but the underlying mechanisms remain unclear.Here,we applied single-cell RNA sequencing to 97,043 cochlear cells,identifying macrophages as the primary immune responders to acoustic trauma,with a notable increase in their proportion in the cochlea.Immunofluorescence confirmed significant recruitment and activation of cochlear macrophages following noise exposure,while in vivo macrophage depletion resulted in the recovery of hearing.Furthermore,analyses of differentially-expressed genes and pathways revealed pronounced activation of NLRP3 inflammasome signaling in macrophages during night-time noise exposure.Measurements of elevated IL-1βand IL-18 expression in cochlear macrophages by multiplex immunohistochemistry correlated with heightened inflammation in the night-time exposure group.These findings were further confirmed by the administration of the selective NLRP3 inhibitor CY-09,which mitigated inflammasome activation,preserved synaptic integrity,and protect against hearing loss.In conclusion,our findings underscore the role of macrophage-driven NLRP3 inflammasome activation in mediating circadian variations in cochlear damage,offering a potential therapeutic target for mitigating NIHL.展开更多
Designing appropriate loss functions is critical to the success of supervised learning models.However,most conventional losses are fixed and manually designed,making them suboptimal for diverse and dynamic learning sc...Designing appropriate loss functions is critical to the success of supervised learning models.However,most conventional losses are fixed and manually designed,making them suboptimal for diverse and dynamic learning scenarios.In this work,we propose an Adaptive Meta-Loss Network(Adaptive-MLN)that learns to generate taskagnostic loss functions tailored to evolving classification problems.Unlike traditional methods that rely on static objectives,Adaptive-MLN treats the loss function itself as a trainable component,parameterized by a shallow neural network.To enable flexible,gradient-free optimization,we introduce a hybrid evolutionary approach that combines GeneticAlgorithms(GA)for global exploration and Evolution Strategies(ES)for local refinement.This co-evolutionary process dynamically adjusts the loss landscape,improvingmodel generalization without relying on analytic gradients or handcrafted heuristics.Experimental evaluations on synthetic tasks and the CIFAR-10 andMNIST datasets demonstrate that our approach consistently outperforms standard losses such as Cross-Entropy and Mean Squared Error in terms of accuracy,convergence,and adaptability.展开更多
The micro-riblet structures have been demonstrated effective in controlling the Total Pressure Loss(TPL)of aero-engine blades.However,due to the considerable scale gap between micro-texture and an actual aero-engine b...The micro-riblet structures have been demonstrated effective in controlling the Total Pressure Loss(TPL)of aero-engine blades.However,due to the considerable scale gap between micro-texture and an actual aero-engine blade,wind tunnel tests and numerical simulations with massive grids directly describing the global flow field are costly for aerodynamic evaluation.Furthermore,the fine micro surface structure brings unavoidable manufacturing errors,and the probability prediction contributes to gaining the confidence interval of the results.Therefore,a novel relay-based probabilistic model for multi-fidelity scenarios in the TPL prediction of a compressor cascade with micro-riblet surfaces is proposed to trade off accuracy and efficiency.Combined with the low-fidelity flow data generated by an aerodynamic solution strategy using the boundary surrogate model and the high-fidelity flow data from the experiment,the relay-based modeling has been achieved through knowledge transferring,and the confidence interval can be provided by the Gaussian Process Regression(GPR)model.The TPL of compressor cascades with micro-riblet surfaces under different surface structures at March number Ma=0.64,0.74,0.84 have been evaluated using the Relay-Based Probabilistic(RBP)model.The results illustrate that the RBP model could provide higher accuracy than the Single-Fidelity-Data-Driven(SFDD)prediction model,which show the promising potential of multi-fidelity scenarios data fusion in the aerodynamic evaluation of multi-scale configurations.展开更多
The lateral transport of labile organic carbon represents a critical pathway for soil organic carbon(SOC) loss,reducing organic carbon sequestration and increasing the risk of waterbody pollution.Livestock manure appl...The lateral transport of labile organic carbon represents a critical pathway for soil organic carbon(SOC) loss,reducing organic carbon sequestration and increasing the risk of waterbody pollution.Livestock manure application on croplands serves as a common fertilizer reduction practice to sustain crop yields,enhance SOC sequestration,and reduce water erosion.However,limited quantitative assessments have examined the effects of livestock manure substitution on labile organic carbon lateral loss and fluxes in long-term experiments.This study conducted a three-year field investigation on subtropical sloping croplands to assess the impact of livestock manure substitution on dissolved organic carbon(DOC) and particulate organic carbon(POC) loss via surface runoff,interflow and eroded sediments.There are four treatments:no fertilization(CK);chemical nitrogen fertilizer(SF),40% nitrogen substitution with pig manure(PMF),and 100% nitrogen substitution from pig manure(PM).Compared to SF treatment,long-term livestock manure substitution in PMF and PM treatments significantly(P<0.05) reduced annual cumulative surface runoff fluxes by 13.5 and 21.6%,respectively.Manure applications decreased annual sediment fluxes by 12.9 and 19.1%,respectively.Soil water stable aggregates for mean weight diameter(MWD) increased significantly by 37.7 and 73.6%.Annual cumulative POC loss flux via eroded sediment under PMF and PM treatments increased significantly(P<0.05) by 61.1 and 47.9%,respectively.The labile organic carbon loss fluxes,including DOC and POC losses,under PMF and PM treatments increased significantly(P<0.05) by 11.9 and 31.4%,respectively.These results demonstrate that while water erosion intensity decreases due to enhanced soil aggregate stability,the risk of labile organic carbon loss increases after long-term livestock manure substitution in subtropical sloping croplands.Future research should examine labile organic carbon lateral migration under various soil types and slope gradients for livestock manure application in subtropical agricultural ecosystem croplands to better understand extreme rainfall effects.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 62071405the National Natural Science Foundation of China under Grant 12175189.
文摘The accurate segmentation of deep gray matter nuclei is critical for neuropathological research,disease diagnosis and treatment.Existing methods employ the supervised learning training approach,which requires large labeled datasets.It is challenging and time-consuming to obtain such datasets for medical image analysis.In addition,these methods based on convolutional neural networks(CNNs)only achieve suboptimal performance due to the locality of convolutional operations.Vision Transformers(ViTs)efficiently model long-range dependencies and thus have the potentiality to outperform these methods in segmentation tasks.To address these issues,we propose a novel hybrid network based on self-supervised pre-training for deep gray matter nuclei segmentation.Specifically,we present a CNN-Transformer hybrid network(CTNet),whose encoder consists of 3D CNN and ViT to learn local spatial-detailed features and global semantic information.A self-supervised learning(SSL)approach that integrates rotation prediction and masked feature reconstruction is proposed to pre-train the CTNet,enabling the model to learn valuable visual representations from unlabeled data.We evaluate the effectiveness of our method on 3T and 7T human brain MRI datasets.The results demonstrate that our CTNet achieves better performance than other comparison models and our pre-training strategy outperforms other advanced self-supervised methods.When the training set has only one sample,our pre-trained CTNet enhances segmentation performance,showing an 8.4%improvement in Dice similarity coefficient(DSC)compared to the randomly initialized CTNet.
基金supported by the National Natural Science Foundation of China(32471964)。
文摘The collection and annotation of lar ge-scale bird datasets are resource-intensive and time-consuming processes that significantly limit the scalability and accuracy of biodiversity monitoring systems.While self-supervised learning(SSL)has emerged as a promising approach for leveraging unannotated data,current SSL methods face two critical challenges in bird species recognition:(1)long-tailed data distributions that result in poor performance on underrepresented species;and(2)domain shift issues caused by data augmentation strategies designed to mitigate class imbalance.Here we present SDNet,a novel SSL-based bird recognition framework that integrates diffusion models with large language models(LLMs)to overcome these limitations.SDNet employs LLMs to generate semantically rich textual descriptions for tail-class species by prompting the models with species taxonomy,morphological attributes,and habitat information,producing detailed natural language priors that capture fine-grained visual characteristics(e.g.,plumage patterns,body proportions,and distinctive markings).These textual descriptions are subsequently used by a conditional diffusion model to synthesize new bird image samples through cross-attention mechanisms that fuse textual embeddings with intermediate visual feature representations during the denoising process,ensuring generated images preserve species-specific morphological details while maintaining photorealistic quality.Additionally,we incorporate a Swin Transformer as the feature extraction backbone whose hierarchical window-based attention mechanism and shifted windowing scheme enable multi-scale local feature extraction that proves particularly effective at capturing finegrained discriminative patterns(such as beak shape and feather texture)while mitigating domain shift between synthetic and original images through consistent feature representations across both data sources.SDNet is validated on both a self-constructed dataset(Bird_BXS)an d a publicly available benchmark(Birds_25),demonstrating substantial improvements over conventional SSL approaches.Our results indicate that the synergistic integration of LLMs,diffusion models,and the Swin Transformer architecture contributes significantly to recognition accuracy,particularly for rare and morphologically similar species.These findings highlight the potential of SDNet for addressing fundamental limitations of existing SSL methods in avian recognition tasks and establishing a new paradigm for efficient self-supervised learning in large-scale ornithological vision applications.
基金Supported by Sichuan Science and Technology Program(2023YFSY0026,2023YFH0004)Supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government(MSIT)(No.RS-2022-00155885,Artificial Intelligence Convergence Innovation Human Resources Development(Hanyang University ERICA)).
文摘Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.
基金National Natural Science Foundation of China(Nos.42501465,42471504)。
文摘Recent years have witnessed significant progress in deep learning for remote sensing image Super-Resolution(SR).However,in real-world applications,paired data is often unavailable,making supervised training infeasible,while unknown degradation factors constrain reconstruction performance and impair detail recovery.To this end,we propose a Degradation-Adaptive Self-supervised SR method,named DASSR,which recovers high-fidelity details from low-resolution remote sensing images without requiring supervision from high-resolution groundtruth.DASSR employs a dual-path closed-loop architecture,enabling joint learning of SR reconstruction and blur kernel estimation through cycle consistency in the main branch and regularization in the auxiliary branch.Specifically,we incorporate an Edge-Preserving SR network(EPSRN)into DASSR,whose core Hybrid Attention Enhancement Block(HAEB)captures precise structural representations to guide accurate detail reconstruction.Furthermore,a composite loss function is designed,integrating spatial reconstruction consistency,frequencydomain spectrum alignment,and kernel sparsity constraints to ensure stable and efficient self-supervised learning.Experiments on both simulated and real-world remote sensing datasets demonstrate that the proposed DASSR method outperforms competitive deep learning-based SR methods,notably achieving approximately 9%and 15%improvements in the Average Gradient(AG)and Spatial Frequency(SF)metrics,respectively,over the best-performing competitor.
基金supported in part by the National Natural Science Foundation of China under Grants 62472434 and 62402171in part by the National Key Research and Development Program of China under Grant 2022YFF1203001+1 种基金in part by the Science and Technology Innovation Program of Hunan Province under Grant 2022RC3061in part by the Sci-Tech Innovation 2030 Agenda under Grant 2023ZD0508600.
文摘Computed Tomography(CT)reconstruction is essential inmedical imaging and other engineering fields.However,blurring of the projection during CT imaging can lead to artifacts in the reconstructed images.Projection blur combines factors such as larger ray sources,scattering and imaging system vibration.To address the problem,we propose DeblurTomo,a novel self-supervised learning-based deblurring and reconstruction algorithm that efficiently reconstructs sharp CT images from blurry input without needing external data and blur measurement.Specifically,we constructed a coordinate-based implicit neural representation reconstruction network,which can map the coordinates to the attenuation coefficient in the reconstructed space formore convenient ray representation.Then,wemodel the blur as aweighted sumof offset rays and design the RayCorrectionNetwork(RCN)andWeight ProposalNetwork(WPN)to fit these rays and their weights bymulti-view consistency and geometric information,thereby extending 2D deblurring to 3D space.In the training phase,we use the blurry input as the supervision signal to optimize the reconstruction network,the RCN,and the WPN simultaneously.Extensive experiments on the widely used synthetic dataset show that DeblurTomo performs superiorly on the limited-angle and sparse-view in the simulated blurred scenarios.Further experiments on real datasets demonstrate the superiority of our method in practical scenarios.
基金supported by the National Natural Science Foundation of China(42374134,42304125,U20B6005)the Science and Technology Commission of Shanghai Municipality(23JC1400502)the Fundamental Research Funds for the Central Universities.
文摘Blended acquisition offers efficiency improvements over conventional seismic data acquisition, at the cost of introducing blending noise effects. Besides, seismic data often suffers from irregularly missing shots caused by artificial or natural effects during blended acquisition. Therefore, blending noise attenuation and missing shots reconstruction are essential for providing high-quality seismic data for further seismic processing and interpretation. The iterative shrinkage thresholding algorithm can help obtain deblended data based on sparsity assumptions of complete unblended data, and it characterizes seismic data linearly. Supervised learning algorithms can effectively capture the nonlinear relationship between incomplete pseudo-deblended data and complete unblended data. However, the dependence on complete unblended labels limits their practicality in field applications. Consequently, a self-supervised algorithm is presented for simultaneous deblending and interpolation of incomplete blended data, which minimizes the difference between simulated and observed incomplete pseudo-deblended data. The used blind-trace U-Net (BTU-Net) prevents identity mapping during complete unblended data estimation. Furthermore, a multistep process with blending noise simulation-subtraction and missing traces reconstruction-insertion is used in each step to improve the deblending and interpolation performance. Experiments with synthetic and field incomplete blended data demonstrate the effectiveness of the multistep self-supervised BTU-Net algorithm.
基金supported by the National Natural Science Foundation of China(62276092,62303167)the Postdoctoral Fellowship Program(Grade C)of China Postdoctoral Science Foundation(GZC20230707)+3 种基金the Key Science and Technology Program of Henan Province,China(242102211051,242102211042,212102310084)Key Scientiffc Research Projects of Colleges and Universities in Henan Province,China(25A520009)the China Postdoctoral Science Foundation(2024M760808)the Henan Province medical science and technology research plan joint construction project(LHGJ2024069).
文摘Feature fusion is an important technique in medical image classification that can improve diagnostic accuracy by integrating complementary information from multiple sources.Recently,Deep Learning(DL)has been widely used in pulmonary disease diagnosis,such as pneumonia and tuberculosis.However,traditional feature fusion methods often suffer from feature disparity,information loss,redundancy,and increased complexity,hindering the further extension of DL algorithms.To solve this problem,we propose a Graph-Convolution Fusion Network with Self-Supervised Feature Alignment(Self-FAGCFN)to address the limitations of traditional feature fusion methods in deep learning-based medical image classification for respiratory diseases such as pneumonia and tuberculosis.The network integrates Convolutional Neural Networks(CNNs)for robust feature extraction from two-dimensional grid structures and Graph Convolutional Networks(GCNs)within a Graph Neural Network branch to capture features based on graph structure,focusing on significant node representations.Additionally,an Attention-Embedding Ensemble Block is included to capture critical features from GCN outputs.To ensure effective feature alignment between pre-and post-fusion stages,we introduce a feature alignment loss that minimizes disparities.Moreover,to address the limitations of proposed methods,such as inappropriate centroid discrepancies during feature alignment and class imbalance in the dataset,we develop a Feature-Centroid Fusion(FCF)strategy and a Multi-Level Feature-Centroid Update(MLFCU)algorithm,respectively.Extensive experiments on public datasets LungVision and Chest-Xray demonstrate that the Self-FAGCFN model significantly outperforms existing methods in diagnosing pneumonia and tuberculosis,highlighting its potential for practical medical applications.
基金supported in part by the National Natural Science Foundation of China under Grants 62071345。
文摘Self-supervised monocular depth estimation has emerged as a major research focus in recent years,primarily due to the elimination of ground-truth depth dependence.However,the prevailing architectures in this domain suffer from inherent limitations:existing pose network branches infer camera ego-motion exclusively under static-scene and Lambertian-surface assumptions.These assumptions are often violated in real-world scenarios due to dynamic objects,non-Lambertian reflectance,and unstructured background elements,leading to pervasive artifacts such as depth discontinuities(“holes”),structural collapse,and ambiguous reconstruction.To address these challenges,we propose a novel framework that integrates scene dynamic pose estimation into the conventional self-supervised depth network,enhancing its ability to model complex scene dynamics.Our contributions are threefold:(1)a pixel-wise dynamic pose estimation module that jointly resolves the pose transformations of moving objects and localized scene perturbations;(2)a physically-informed loss function that couples dynamic pose and depth predictions,designed to mitigate depth errors arising from high-speed distant objects and geometrically inconsistent motion profiles;(3)an efficient SE(3)transformation parameterization that streamlines network complexity and temporal pre-processing.Extensive experiments on the KITTI and NYU-V2 benchmarks show that our framework achieves state-of-the-art performance in both quantitative metrics and qualitative visual fidelity,significantly improving the robustness and generalization of monocular depth estimation under dynamic conditions.
基金funded by theNational Science and TechnologyCouncil(NSTC),Taiwan,under grant numbers NSTC 112-2634-F-019-001 and NSTC 113-2634-F-A49-007.
文摘Few-shot learning has emerged as a crucial technique for coral species classification,addressing the challenge of limited labeled data in underwater environments.This study introduces an optimized few-shot learning model that enhances classification accuracy while minimizing reliance on extensive data collection.The proposed model integrates a hybrid similarity measure combining Euclidean distance and cosine similarity,effectively capturing both feature magnitude and directional relationships.This approach achieves a notable accuracy of 71.8%under a 5-way 5-shot evaluation,outperforming state-of-the-art models such as Prototypical Networks,FEAT,and ESPT by up to 10%.Notably,the model demonstrates high precision in classifying Siderastreidae(87.52%)and Fungiidae(88.95%),underscoring its effectiveness in distinguishing subtle morphological differences.To further enhance performance,we incorporate a self-supervised learning mechanism based on contrastive learning,enabling the model to extract robust representations by leveraging local structural patterns in corals.This enhancement significantly improves classification accuracy,particularly for species with high intra-class variation,leading to an overall accuracy of 76.52%under a 5-way 10-shot evaluation.Additionally,the model exploits the repetitive structures inherent in corals,introducing a local feature aggregation strategy that refines classification through spatial information integration.Beyond its technical contributions,this study presents a scalable and efficient approach for automated coral reef monitoring,reducing annotation costs while maintaining high classification accuracy.By improving few-shot learning performance in underwater environments,our model enhances monitoring accuracy by up to 15%compared to traditional methods,offering a practical solution for large-scale coral conservation efforts.
文摘Intelligent Transportation Systems(ITS)leverage Integrated Sensing and Communications(ISAC)to enhance data exchange between vehicles and infrastructure in the Internet of Vehicles(IoV).This integration inevitably increases computing demands,risking real-time system stability.Vehicle Edge Computing(VEC)addresses this by offloading tasks to Road Side Units(RSUs),ensuring timely services.Our previous work,the FLSimCo algorithm,which uses local resources for federated Self-Supervised Learning(SSL),has a limitation:vehicles often can’t complete all iteration tasks.Our improved algorithm offloads partial tasks to RSUs and optimizes energy consumption by adjusting transmission power,CPU frequency,and task assignment ratios,balancing local and RSU-based training.Meanwhile,setting an offloading threshold further prevents inefficiencies.Simulation results show that the enhanced algorithm reduces energy consumption and improves offloading efficiency and accuracy of federated SSL.
基金supported by the National Key Research and Development Program of China,China(2022YFF1301801)Agricultural scientific and technological innovation project of Shandong Academy of Agricultural Sciences,China(333 Project)(06202214442066)+1 种基金Beijing Natural Science Foundation,China(5232018)Technology Innovation Project of Shandong Academy of Agricultural Sciences,China(06202214442062).
文摘Maize yield is critically endangered by diseases throughout its growth cycle,posing significant risks to food security.The spatial and temporal dynamics of maize yield loss and the rate of yield loss attributable to these threats on a regional scale have been challenging to ascertain due to scarce continuous observation data.This study compiled county-level data on maize yield and yield loss across China's six primary cropping regions over twenty years from 1999 to 2018.These include the Spring-sown area of Northern China(1-NC),the Summer-sown Huang-Huai-Hai Plain(2-HHP),the Southwest Mountain(3-SM),the Southern Hilly(4-SH),the Northwest Irrigated(5-NI),and the Qinghai-Tibet Plateau Maize Regions(6-QTP).We identified 15 major diseases affecting these regions.The annual average yield loss due to maize diseases in the regions 1-NC,2-HHP,3-SM,4-SH,5-NI,and 6-QTP were 0.40,0.58,0.12,0.05,0.04 and<0.01 million tons,respectively,and the corresponding average yield loss rate(the ratio of yield loss to total yield)in these regions was 0.63,0.90,0.65,0.63,0.44,and 0.05.The yield loss due to all diseases increased for three regions in 3-SM,4-SH and 5-NI.The yield loss rate due to diseases significantly increased in region 4-SH and 5-NI.Predominantly,maize leaf blight has become the most significant threats.In region 1-NC,maize head smut(D1)and maize leaf blight(D2)were the primary diseases.In region 2-HHP,maize leaf blight(D2),maize rust(D3),maize brown spot(D5),Curvularia leaf spot(D7),and maize virus disease(D14)were the key pathogens.Bivariate trend analysis(joint analysis of yield loss and loss rate trends)indicated that maize head smut(D1)decreased significantly in 1-NC,while in 2-HHP,six diseases showed a significant decrease in both yield loss and loss rate,namely sheath blight(D4),brown spot(D5),root rot(D11),downy mildew(D12)and virus disease(D14).By providing a long-term,national-scale perspective,this study not only supports the development of broad management strategies but also guides the creation of precise,region-specific control protocols to safeguard maize production.
文摘Tinnitus:the hearing of a sound that has not been produced by any external or internal source,is a rather heterogeneous hearing disorder.Background/Objectives:Hearing loss has been shown to be the main risk factor for tinnitus while emotional disorders are risk factors for developing intrusive or bothersome tinnitus.Moreover,aging has also been identified as another risk factor.The aim of this paper was to analyse the correlation between hearing loss,age and tinnitus severity in a cohort of 610 tinnitus sufferers.Methods:Age,audiometric(hearing condition)and tinnitus(time duration and severity)data were assessed and analysed for all subjects just after recruiting(baseline).Furthermore,the average hearing loss(HL)curves of the participants for age groups were compared to these with the corresponding Age Related HL(ARHL).Results:For most of the age groups,the measured HL curves exceeded in 10-20 dB those of the ARHL.The average age of tinnitus onset(age minus tinnitus duration)was found to be 44-46 years in both men and women.Weak correlation between audiometric feature and tinnitus distress was observed.Conclusions:Hearing loss has been shown to be a clear risk factor for triggering tinnitus(86%of participants were hearing impaired).In this cohort,average measures of hearing loss showed,at most,weak associations with tinnitus-related distress,suggesting that non-audiological factors may play a predominant role.
文摘Background:Brucellosis is a zoonotic infection common in Mediterranean countries and the Middle East.Neurological involvement,although rare,can lead to severe complications,including sensorineural hearing loss(SNHL).This case is particularly noteworthy as it highlights irreversible auditory nerve damage in brucellosis,emphasizing the importance of early diagnosis and treatment to prevent permanent neurological consequences.The novelty of this case lies in the severity of auditory involvement despite timely treatment.Case Presentation:A 43-year-old male farmer of Maghrebi origin presented with neurobrucellosis complicated by severe,irreversible bilateral sensorineural hearing loss.The patient initially reported symptoms of hearing loss and dizziness,which were confirmed to be associated with auditory nerve involvement.Wright's serology and polymerase chain reaction(PCR)testing confirmed brucellosis.Despite appropriate and prolonged antibiotic therapy,including drugs that penetrate the meningeal barrier and act intracellularly,the patient's auditory impairment remained permanent.The patient is currently a candidate for cochlear implantation to manage his severe hearing loss.Neurological symptoms did not improve with treatment,but cochlear implantation may offer a potential solution to his hearing deficit.Conclusions:This case highlights the importance of early recognition and intervention in brucellosis cases,particularly those with neurological involvement.Delayed diagnosis and treatment can result in irreversible neurological damage.It also underscores the potential for cochlear implantation in patients with severe,irreversible sensorineural hearing loss caused by neurobrucellosis.Cochlear implantation offers an important solution for patients with brucellosis-related hearing deficits,improving their quality of life despite the neurological damage caused by the infection.
文摘Recurrent pregnancy loss(RPL)affects 2%-5%of couples attempting to conceive.It is a highly heterogenous condition attributed to several factors including endocrine dysfunction,auto immune disorders,thrombophilia,genetic abnormalities,infectious diseases,uterine anomalies,sperm DNA fragmentation,and epigenetics.Among genetic causes,chromosomal abnormalities are the most frequent etiological factor of early miscarriage,accounting for 50%–60%of first trimester abortions.Numerical or structural chromosomal changes may result in spontaneous miscarriages.These anomalies arise as a result of chromosomal translocation,non-disjunction,or mutations[1].Transmission of parental chromosomal abnormalities may be one of the chances for a recurrence of miscarriage in the first trimester of pregnancy,albeit the cause is unknown[2,3].
基金supported by the Science and Technology Development Project of Jilin Province(Project No.YDZJ202301ZYTS284).
文摘With the increasing penetration of renewable energy,the coordination of energy storage with thermal power for frequency regulation has become an effective means to enhance grid frequency security.Addressing the challenge of improving the frequency regulation performance of a thermal-storage primary frequency regulation system while reducing its associated losses,this paper proposes a multi-dimensional cooperative optimization strategy for the control parameters of a combined thermal-storage system,considering regulation losses.First,the frequency regulation losses of various components within the thermal power unit are quantified,and a calculation method for energy storage regulation loss is proposed,based on Depth of Discharge(DOD)and C-rate.Second,a thermal-storage cooperative control method based on series compensation is developed to improve the system’s frequency regulation performance.Third,targeting system regulation loss cost and regulation output,and considering constraints on output overshoot and system parameters,an improved Particle Swarm Optimization(PSO)algorithm is employed to tune the parameters of the low-pass filter and the series compensator,thereby reducing regulation losses while enhancing performance.Finally,simulation results demonstrate that the total loss cost of the proposed control strategy is comparable to that of a system with only thermal power participation.However,the thermal power loss cost is reduced by 42.16%compared to the thermal-only case,while simultaneously improving system frequency stability.Thus,the proposed strategy effectively balances system frequency stability and economic efficiency.
基金funded by the Technology Development Board(TDB)of India's Ministry of Science and Technology(TDB/M-25/2018-19).
文摘Objective:To investigate the potential link between chromosomal polymorphisms in couples who had a medical history of idiopathic recurrent pregnancy loss.Methods:Cytogenetic investigation was conducted with mitogen(Phytohemagglutinin-M,Gibco)stimulated blood T lymphocytes by Giemsa trypsin Giemsa banding and Ag-NOR banding on 580 couples with a history of idiopathic recurrent pregnancy loss and 240 couples from the general population.Thirty good chromosomal spreads were captured,karyotyped,and analyzed.The karyotypes were designated using the International System for Human Cytogenomic Nomenclature 2024.Pearson Chi-square test was used to compare the frequency of chromosomal polymorphism variations in the idiopathic recurrent pregnancy loss group with the general population group.Results:A conventional cytogenetic investigation revealed that 45.43%of couples experiencing idiopathic recurrent pregnancy loss presented with various types of chromosomal polymorphic variants,compared to 11.88%in the general population.The overall frequency of these chromosomal polymorphic variants was significantly higher in the idiopathic recurrent pregnancy loss group compared to the general population group(OR 9.97,95%CI 6.99-14.21;P<0.05).Additionally,the prevalence of polymorphic variants was higher among males(49.14%)than females(41.72%)(P=0.01).Conclusions:Chromosomal polymorphic analysis may play a crucial role in the assessment and careful clinical management of cases with idiopathic recurrent pregnancy loss,especially when no other conclusive reasons are identified during the initial evaluation.Therefore,heteromorphism should not be overlooked while investigating the causes of idiopathic recurrent pregnancy loss.
基金supported by the Scientific and Innovative Action Plan of Shanghai(CN)(22Y11902000)the National Natural Science Foundation of China(82371144 and 82201273)+2 种基金the Cross-Disciplinary Research Fund of Shanghai Ninth People's Hospital,Shanghai Jiao Tong University School of Medicine(JYJC202231)the Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases(14DZ2260300)We extend our gratitude to Prof.Hao Wu and the Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases for providing essential resources and laboratory facilities,and to Prof.Lei Song and Prof.Zhiyong Liu for valuable insights and guidance.
文摘Circadian sensitivity significantly influences the severity of noise-induced hearing loss(NIHL),but the underlying mechanisms remain unclear.Here,we applied single-cell RNA sequencing to 97,043 cochlear cells,identifying macrophages as the primary immune responders to acoustic trauma,with a notable increase in their proportion in the cochlea.Immunofluorescence confirmed significant recruitment and activation of cochlear macrophages following noise exposure,while in vivo macrophage depletion resulted in the recovery of hearing.Furthermore,analyses of differentially-expressed genes and pathways revealed pronounced activation of NLRP3 inflammasome signaling in macrophages during night-time noise exposure.Measurements of elevated IL-1βand IL-18 expression in cochlear macrophages by multiplex immunohistochemistry correlated with heightened inflammation in the night-time exposure group.These findings were further confirmed by the administration of the selective NLRP3 inhibitor CY-09,which mitigated inflammasome activation,preserved synaptic integrity,and protect against hearing loss.In conclusion,our findings underscore the role of macrophage-driven NLRP3 inflammasome activation in mediating circadian variations in cochlear damage,offering a potential therapeutic target for mitigating NIHL.
基金supported by the National Natural Science Foundation of China(NSFC)under Grant number:82171965.
文摘Designing appropriate loss functions is critical to the success of supervised learning models.However,most conventional losses are fixed and manually designed,making them suboptimal for diverse and dynamic learning scenarios.In this work,we propose an Adaptive Meta-Loss Network(Adaptive-MLN)that learns to generate taskagnostic loss functions tailored to evolving classification problems.Unlike traditional methods that rely on static objectives,Adaptive-MLN treats the loss function itself as a trainable component,parameterized by a shallow neural network.To enable flexible,gradient-free optimization,we introduce a hybrid evolutionary approach that combines GeneticAlgorithms(GA)for global exploration and Evolution Strategies(ES)for local refinement.This co-evolutionary process dynamically adjusts the loss landscape,improvingmodel generalization without relying on analytic gradients or handcrafted heuristics.Experimental evaluations on synthetic tasks and the CIFAR-10 andMNIST datasets demonstrate that our approach consistently outperforms standard losses such as Cross-Entropy and Mean Squared Error in terms of accuracy,convergence,and adaptability.
基金supported by the National Natural Science Foundation of China(No.12301672)the Shanghai Science and Technology Innovation Action Plan(Yangfan Special Project),China(No.23YF1401300)。
文摘The micro-riblet structures have been demonstrated effective in controlling the Total Pressure Loss(TPL)of aero-engine blades.However,due to the considerable scale gap between micro-texture and an actual aero-engine blade,wind tunnel tests and numerical simulations with massive grids directly describing the global flow field are costly for aerodynamic evaluation.Furthermore,the fine micro surface structure brings unavoidable manufacturing errors,and the probability prediction contributes to gaining the confidence interval of the results.Therefore,a novel relay-based probabilistic model for multi-fidelity scenarios in the TPL prediction of a compressor cascade with micro-riblet surfaces is proposed to trade off accuracy and efficiency.Combined with the low-fidelity flow data generated by an aerodynamic solution strategy using the boundary surrogate model and the high-fidelity flow data from the experiment,the relay-based modeling has been achieved through knowledge transferring,and the confidence interval can be provided by the Gaussian Process Regression(GPR)model.The TPL of compressor cascades with micro-riblet surfaces under different surface structures at March number Ma=0.64,0.74,0.84 have been evaluated using the Relay-Based Probabilistic(RBP)model.The results illustrate that the RBP model could provide higher accuracy than the Single-Fidelity-Data-Driven(SFDD)prediction model,which show the promising potential of multi-fidelity scenarios data fusion in the aerodynamic evaluation of multi-scale configurations.
基金funded by the Joint Funds of the National Natural Science Foundation of China (U20A20107 and U22A20562)the National Key Research and Development Program of China (2023YFD1900201-3)the International Cooperation Project,Ministry of Science and Technology of China (G2023019005L)。
文摘The lateral transport of labile organic carbon represents a critical pathway for soil organic carbon(SOC) loss,reducing organic carbon sequestration and increasing the risk of waterbody pollution.Livestock manure application on croplands serves as a common fertilizer reduction practice to sustain crop yields,enhance SOC sequestration,and reduce water erosion.However,limited quantitative assessments have examined the effects of livestock manure substitution on labile organic carbon lateral loss and fluxes in long-term experiments.This study conducted a three-year field investigation on subtropical sloping croplands to assess the impact of livestock manure substitution on dissolved organic carbon(DOC) and particulate organic carbon(POC) loss via surface runoff,interflow and eroded sediments.There are four treatments:no fertilization(CK);chemical nitrogen fertilizer(SF),40% nitrogen substitution with pig manure(PMF),and 100% nitrogen substitution from pig manure(PM).Compared to SF treatment,long-term livestock manure substitution in PMF and PM treatments significantly(P<0.05) reduced annual cumulative surface runoff fluxes by 13.5 and 21.6%,respectively.Manure applications decreased annual sediment fluxes by 12.9 and 19.1%,respectively.Soil water stable aggregates for mean weight diameter(MWD) increased significantly by 37.7 and 73.6%.Annual cumulative POC loss flux via eroded sediment under PMF and PM treatments increased significantly(P<0.05) by 61.1 and 47.9%,respectively.The labile organic carbon loss fluxes,including DOC and POC losses,under PMF and PM treatments increased significantly(P<0.05) by 11.9 and 31.4%,respectively.These results demonstrate that while water erosion intensity decreases due to enhanced soil aggregate stability,the risk of labile organic carbon loss increases after long-term livestock manure substitution in subtropical sloping croplands.Future research should examine labile organic carbon lateral migration under various soil types and slope gradients for livestock manure application in subtropical agricultural ecosystem croplands to better understand extreme rainfall effects.
