Laser wakefield accelerators(LWFAs)offer acceleration gradients up to 1000 times higher than those of conventional radio-frequency accelerators,offering a pathway to significantly more compact and cost-effective accel...Laser wakefield accelerators(LWFAs)offer acceleration gradients up to 1000 times higher than those of conventional radio-frequency accelerators,offering a pathway to significantly more compact and cost-effective accelerator systems.This breakthrough opens up new possibilities for laboratory-scale light sources.All-optical inverse Compton scattering(AOCS)sources driven by LWFAs produce high-brightness,quasimonochromatic X rays with micrometer-scale source sizes,delivering the spatial coherence and resolution required for X-ray phase-contrast imaging(XPCI).These features position AOCS X-ray sources as promising tools for applications in biology,medicine,physics,and materials science.However,previous AOCS-based imaging studies have primarily focused on X-ray absorption imaging.In this work,we report successful experimental demonstrations of edge-enhanced in-line XPCI using energy-tunable,quasi-monochromatic AOCS X rays.With a spatial resolution of~20μm,our results clearly show the potential of high-resolution,AOCS-based XPCI applications.展开更多
Drill string vibration during drilling plays a vital and potentially decisive role in maintaining wellbore stability,as repeated impacts may lead to fatigue and borehole collapse.While drilling through geological laye...Drill string vibration during drilling plays a vital and potentially decisive role in maintaining wellbore stability,as repeated impacts may lead to fatigue and borehole collapse.While drilling through geological layers,a material contrast may act as a localization point for wellbore damage.The hypothesis tested in this paper is that wellbore instability is focused on the boundary between the layers and that mechanical contrasts accelerate the wellbore collapse.In this study,an elastic-plastic damage model was employed to investigate the effects of repeated mechanical impacts on wellbore stability.A 2-dimensional(2D)model of a wellbore surrounded by contrasting materials was developed,and the accumulated damage caused by repeated lateral impacts was monitored.It was found that damage develops not only around the wall of the wellbore but also along the material boundaries.A sensitivity analysis was carried out to identify the impact of contrasts in both elastic(Young's modulus and Poisson's ratio)and plastic(cohesion,friction angle,and dilation angle)parameters between layers.Four damage patterns were identifiedin the simulated models.The results also suggested that the number of impacts required to reach the critical damage was highly affected by the contrast in elastic parameters,while cohesion and friction angle contrasts had a lesser effect.Additionally,increasing the contrast in the dilation angle localized the damage,thus reducing the number of impacts required to trigger wellbore failure.展开更多
To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervis...To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.展开更多
Medical imaging is essential in modern health care,allowing accurate diagnosis and effective treatment planning.These images,however,often demonstrate low contrast,noise,and brightness distortion that reduce their dia...Medical imaging is essential in modern health care,allowing accurate diagnosis and effective treatment planning.These images,however,often demonstrate low contrast,noise,and brightness distortion that reduce their diagnostic reliability.This review presents a structured and comprehensive analysis of advanced histogram equalization(HE)-based techniques for medical image enhancement.Our review methodology encompasses:(1)classical HE approaches and related limitations in medical domains;(2)adaptive schemes like Adaptive Histogram Equalization(AHE)and Contrast Limited Adaptive Histogrma Equalization(CLAHE)and their advance variants;(3)brightnesspreserving schemes like BBHE and MMBEBHE and related algorithms;(4)dynamic and recursive histogram equalization methods incorporating DHE and RMSHE;(5)fuzzy logic-based enhancement methodologies addressing uncertainty and noise in medical images;and(6)hybrid optimization methodologies through the application of metaheuristic algorithms(World Cup Optimization,Particle Swarm Optimization,Genetic Algorithms,along with histogram-based methodologies.)There is also a comparative discussion given based on contrast improvement,image brightness preservation,noise management,and computational efficiency.Such advancements have better capabilities of improving image quality,which is more important for improved diagnosis and image analysis.展开更多
Accurate temperature control and effective oxide removal are essential for achieving high-quality epitaxial growth in molecular beam epitaxy(MBE).However,traditional methods often rely on manual identification of refl...Accurate temperature control and effective oxide removal are essential for achieving high-quality epitaxial growth in molecular beam epitaxy(MBE).However,traditional methods often rely on manual identification of reflection high-energy electron diffraction(RHEED)patterns.This process is heavily influenced by the grower’s experience,leading to issues with reproducibility and limiting the potential for automation.In this report,we propose an unsupervised learning framework for realtime RHEED analysis during the deoxidation process.By incorporating temporal similarity constraints into contrastive learning,our model generates smooth and interpretable feature trajectories that illustrate transitions in the deoxidation state,thus eliminating the need for manual labeling.The model,pre-trained using grouped contrastive loss,shows significant improvement in RHEED feature boundary discrimination and localization of critical regions.We evaluated its generalizability through two transfer learning strategies:calibration-free clustering and few-shot fine-tuning.The pre-trained model achieved a clustering accuracy of 88.1%for GaAs deoxidation samples without additional labels and reached an accuracy of 94.3%to 95.5%after fine-tuning with just five sample pairs across GaAs,Ge,and InAs substrates.This framework is optimized for resource-constrained edge devices,allowing for real-time,plug-and-play integration with existing MBE systems and swift adaptation across various materials and equipment.This work paves the way for greater automation and improved reproducibility in semiconductor manufacturing.展开更多
Lacustrine groundwater discharge(LGD)plays an important role in water resources management.Previous studies have focused on LGD process in a single lake,but the differences in LGD process within the same region have n...Lacustrine groundwater discharge(LGD)plays an important role in water resources management.Previous studies have focused on LGD process in a single lake,but the differences in LGD process within the same region have not been thoroughly investigated.In this study,multiple tracers(hydrochemistry,𝛿D,𝛿18O and 222Rn)were used to compare mechanisms of LGD in Daihai and Ulansuhai Lake in Inner Mongoli1,Northwest China.The hydrochemical types showed a trend from groundwater to lake water,indicating a hydraulic connection between them.In addition,the𝛿D and𝛿18O values of sediment pore water were between the groundwater and lake water,indicating the LGD processes.The radon mass balance model was used to estimate the average groundwater discharge rates of Daihai and Ulansuhai Lake,which were 2.79 mm/day and 3.02 mm/day,respectively.The total nitrogen(TN),total phosphorus(TP),and fluoride inputs associated with LGD in Daihai Lake accounted for 97.52%,96.59%,and 95.84%of the total inputs,respectively.In contrast,TN,TP and fluoride inputs in Ulansuhai Lake were 53.56%,40.98%,and 36.25%,respectively.This indicates that the pollutant inputs associated with LGD posed a potential threat to the ecological stability of Daihai and Ulansuhai Lake.By comparison,the differences of LGD process and associated pollutant flux were controlled by hydrogeological conditions,lakebed permeability and human activities.This study provides a reference for water resources management in Daihai and Ulansuhai Lake basins while improving the understanding of LGD in the Yellow River basin.展开更多
The morbidity rate of primary cardiac tumors(PCTs)is only 0.0138%.[1]Calcified amorphous tumors(CATs)are a particularly rare entity with only a few cases reported in the literature,and account for only 2.47%of PCTs.[2...The morbidity rate of primary cardiac tumors(PCTs)is only 0.0138%.[1]Calcified amorphous tumors(CATs)are a particularly rare entity with only a few cases reported in the literature,and account for only 2.47%of PCTs.[2]CATs can occur at any age and have been identified at various intracardiac locations.The clinical manifestations of patients are related to the location and size of the lesion.展开更多
Deep graph contrastive clustering has attracted widespread attentions due to its self-supervised representation learning paradigm and superior clustering performance.Although,two challenges emerge and result in high c...Deep graph contrastive clustering has attracted widespread attentions due to its self-supervised representation learning paradigm and superior clustering performance.Although,two challenges emerge and result in high computational costs.Most existing contrastive methods adopt the data augmentation and then representation learning strategy,where representation learning with trainable graph convolution is coupled with complex and fixed data augmentation,inevitably limiting the efficiency and flexibility.The similarity metric between positive-negative sample pairs is complex and contrastive objective is partial,limiting the discriminability of representation learning.To solve these challenges,a novel wide graph clustering network(WGCN)adhering to representation and then augmentation framework is proposed,which mainly consists of multiorder filter fusion(MFF)and double-level contrastive learning(DCL)modules.Specifically,the MFF module integrates multiorder low-pass filters to extract smooth and multi-scale topological features,utilizing self-attention fusion to reduce redundancy and obtain comprehensive embedding representation.Further,the DCL module constructs two augmented views by the parallel parameter-unshared Siamese encoders rather than complex augmentations on graph.To achieve simple yet effective self-supervised learning,representation self-supervision and structural consistency oriented double-level contrastive loss is designed,where representation self-supervision maximizes the agreement between pairwise augmented embedding representations and structural consistency promotes the mutual information correlation between appending neighborhoods with similar semantics.Extensive experiments on six benchmark datasets demonstrate the superiority of the proposed WGCN,especially highlighting its time-saving characteristic.The code could be available in the https://github.com/Tianxiang Zhao0474/WGCN.展开更多
AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited...AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited adaptive histogram equalization(NICLAHE)to improve retinal image contrast was suggested to aid in the accurate identification of retinal disorders and improve the visibility of fine retinal structures.Additionally,a minimal-order filter was applied to effectively denoise the images without compromising important retinal structures.The novel NICLAHE algorithm was inspired by the classical CLAHE algorithm,but enhanced it by selecting the clip limits and tile sized in a dynamical manner relative to the pixel values in an image as opposed to using fixed values.It was evaluated on the Drive and high-resolution fundus(HRF)datasets on conventional quality measures.RESULTS:The new proposed preprocessing technique was applied to two retinal image databases,Drive and HRF,with four quality metrics being,root mean square error(RMSE),peak signal to noise ratio(PSNR),root mean square contrast(RMSC),and overall contrast.The technique performed superiorly on both the data sets as compared to the traditional enhancement methods.In order to assess the compatibility of the method with automated diagnosis,a deep learning framework named ResNet was applied in the segmentation of retinal blood vessels.Sensitivity,specificity,precision and accuracy were used to analyse the performance.NICLAHE–enhanced images outperformed the traditional techniques on both the datasets with improved accuracy.CONCLUSION:NICLAHE provides better results than traditional methods with less error and improved contrastrelated values.These enhanced images are subsequently measured by sensitivity,specificity,precision,and accuracy,which yield a better result in both datasets.展开更多
BACKGROUND Oil-based iodinated contrast media have excellent contrast properties and are widely used for hysterosalpingographic evaluation of female infertility.On abdominal radiography and computed tomography(CT)scan...BACKGROUND Oil-based iodinated contrast media have excellent contrast properties and are widely used for hysterosalpingographic evaluation of female infertility.On abdominal radiography and computed tomography(CT)scans,their radiodensity is similar to that of metallic objects,which can sometimes lead to diagnostic confusion in the postoperative settings.In this case,retained oil-based contrast medium was observed on an abdominal radiograph following a cesarean section,making it difficult to differentiate from an intraperitoneal foreign body from surgery.The patient was a 37-year-old pregnant woman who was referred to our hospital at 32 weeks and 1 day of pregnancy due to complete placenta previa for mana-gement of pregnancy and delivery.An elective cesarean section was performed at 37 weeks and 3 days.A plain abdominal radiograph taken immediately after surgery revealed a near-round,hyperdense,mass-like shadow with a regular margin in the pelvic cavity.An intraperitoneal foreign body was suspected;therefore,an abdominal CT scan was performed.The foreign body was located on the left side of the pouch of Douglas and had a CT value of 7000 Hounsfield units,similar to that of metals.The CT value strongly suggested the presence of an artificial object.However,further inquiries with the patient and her previous physician revealed a history of hysterosalpingography.Accordingly,retained oil-based iodinated contrast medium was suspected,and observation of the object’s course was adopted.CONCLUSION When intraperitoneal foreign bodies are suspected on postoperative radiographs,the possibility of oil-based iodinated contrast medium retention should be considered.展开更多
BACKGROUND Gastrointestinal dual-contrast ultrasonography(DCUS)is characterized by its high resolution,sensitivity,and specificity.AIM To determine the accuracy of DCUS in predicting lymph node metastasis in middle-ag...BACKGROUND Gastrointestinal dual-contrast ultrasonography(DCUS)is characterized by its high resolution,sensitivity,and specificity.AIM To determine the accuracy of DCUS in predicting lymph node metastasis in middle-aged and elderly patients with gastric cancer(GC).METHODS A total of 100 middle-aged and elderly patients with GC admitted to the Fourth Affiliated Hospital of Soochow University(Dushu Lake Hospital,Suzhou,China)between April 2022 and April 2024 were selected.The baseline data and lymph node metastasis status were collected.DCUS combined with intravenous contrast technology was used to calculate the enhancement time(ET),time to peak(TTP),and slope of the ascending branch wash-in rate(WIR).These indicators were used in assessing lymph node metastasis in patients with GC.RESULTS Among 100 middle-aged and elderly patients with GC,35(35.00%)had lymph node metastases.GC patients with lymph node metastasis had a higher propor-tion of stage II TNM classification and higher WIR values than those without lymph node metastasis.The ET and TTP values were lower in patients with lymph node metastases,and all differences were statistically significant(P<0.05).The area under the curve values for ET,TTP,WIR,and combined diagnosis of GC lymph node metastasis using DCUS were all>0.7.Optimal assessment was achieved when the cutoff values for ET,TTP,and WIR were set at 16.32 seconds,10.67 seconds,and 7.02,res-pectively.CONCLUSION DCUS-mediated assessment of ET,TTP,and WIR can effectively predict and evaluate lymph node metastasis status in patients with GC,with higher sensitivity when used in combination.展开更多
Patent foramen ovale(PFO)is a common congenital heart disorder associated with stroke,decompression sickness and migraine.Combining synchronized contrast transcranial Doppler with contrast transthoracic echocardiograp...Patent foramen ovale(PFO)is a common congenital heart disorder associated with stroke,decompression sickness and migraine.Combining synchronized contrast transcranial Doppler with contrast transthoracic echocardiography has important clinical significance and can improve the accuracy of detecting right-left shunts(RLSs)in patients with PFO.In this letter,regarding an original study presented by Yao et al,we present our insights and discuss how to better help clinicians evaluate changes in PFO-related RLS.展开更多
Kounis syndrome(KS)is a rare but clinically significant condition characterized by the simultaneous occurrence of acute coronary syndrome(ACS)and allergic reactions,which can develop in patients with either normal or ...Kounis syndrome(KS)is a rare but clinically significant condition characterized by the simultaneous occurrence of acute coronary syndrome(ACS)and allergic reactions,which can develop in patients with either normal or diseased coronary arteries.[1,2]The condition is typically triggered by various allergens including medications(particularly contrast media),environmental factors,or food exposures,with symptom onset usually occurring within one hour of exposure.展开更多
Ultrasmall superparamagnetic iron oxide nanoparticles(usSPIONs)are promising alternatives to gadolinium‐based contrast agents for positive contrast enhancement in magnetic resonance imaging(MRI).Unlike larger SPIONs ...Ultrasmall superparamagnetic iron oxide nanoparticles(usSPIONs)are promising alternatives to gadolinium‐based contrast agents for positive contrast enhancement in magnetic resonance imaging(MRI).Unlike larger SPIONs that primarily function as T2/T2*negative contrast agents,usSPIONs with core diameters below 5 nm can effectively shorten T1 relaxation times,producing bright signals in T1‐weighted images.This distinct behavior stems from their unique magnetic properties,including single‐domain configurations,surface spin canting,and rapid Néel relaxation dynamics,which are particularly enhanced at low magnetic field strengths.The biocompatibility of iron oxide,efficient renal clearance pathways,and versatility for surface functionalization offer potential advantages over gadolinium‐based agents,especially regarding safety concerns related to nephrogenic systemic fibrosis and gadolinium deposition.These nanoparticles show particular promise for applications in lowfield MRI,vascular imaging,targeted molecular imaging,and theranostic platforms.Although challenges remain in optimizing synthesis methods for consistent production of monodisperse usSPIONs with tailored surface chemistry,ongoing research continues to advance their potential for clinical translation.This review explores the mechanisms,synthesis approaches,applications,and future perspectives of usSPIONs as positive contrast agents in MRI.展开更多
BACKGROUND Juvenile polyps(JPs)are non-neoplastic polyps.In adults,JPs present with hematochezia in only approximately half the patients and are often found incidentally during endoscopic screening.JPs have no mucosal...BACKGROUND Juvenile polyps(JPs)are non-neoplastic polyps.In adults,JPs present with hematochezia in only approximately half the patients and are often found incidentally during endoscopic screening.JPs have no mucosal fascia at the tip,and spontaneous shedding and massive gastrointestinal hemorrhage may occur.Thus,the JP bleeding detected in this case by extravascular contrast leakage on computed tomography scans and treated with endoscopic clipping is rare.CASE SUMMARY A previously healthy 31-year-old male patient presented with a 2-day history of bloody stools.Upon hospital arrival,rectal examination revealed fresh blood,and abdominal computed tomography scans showed extravascular contrast leakage from the lower rectum’s left-side wall.His blood pressure was slightly low at 104/62 mmHg.However,his pulse rate(69 bpm)and oxygen level(99%on room air)were within normal limits.Emergency endoscopy revealed a pedunculated lesion in the rectum covered by a non-neoplastic mucosal epithelium.No neoplastic lesions were observed at the tip of the polyp;however,pulsatile bleeding was detected at the distal end.We performed endoscopic hemostasis by clipping the stem and then performed a polypectomy above the stem to examine the lesion tissue.Histopathological evaluation revealed a cystically dilated gland without neoplastic lesions.A subsequent total colonoscopy revealed two JPs with characteristic edematous,smooth,and reddish surfaces close to the hemorrhagic lesion.Subsequent histopathological evaluation indicated findings characteristic of JP,such as severe inflammatory cell infiltration of the stroma and cystic dilatation of the glandular ducts.CONCLUSION There are no reports of adult JPs presenting with contrast extravasation where endoscopic hemostasis was successful,as in this case.展开更多
AIM To evaluate the feasibility of reducing the dose of iodinated contrast agent in computed tomography pulmonary angiography(CTPA). METHODS One hundred and twenty-seven patients clinically suspected of having pulmona...AIM To evaluate the feasibility of reducing the dose of iodinated contrast agent in computed tomography pulmonary angiography(CTPA). METHODS One hundred and twenty-seven patients clinically suspected of having pulmonary embolism underwent spiral CTPA, out of whom fifty-seven received 75 mL and the remaining seventy a lower dose of 60 mL of contrast agent. Both doses were administered in a multiphasic injection. A minimum opacification threshold of 250 Hounsfield units(HU) in the main pulmonary artery is used for assessing the technical adequacy of the scans. RESULTS Mean opacification was found to be positively correlated to patient age(Pearson's correlation 0.4255, P < 0.0001) and independent of gender(male:female, 425.6 vs 450.4,P = 0.34). When age is accounted for, the study and control groups did not differ significantly in their mean opacification in the main(436.8 vs 437.9, P = 0.48),left(416.6 vs 419.8, P = 0.45) or the right pulmonary arteries(417.3 vs 423.5, P = 0.40). The number of sub-optimally opacified scans(the mean opacification in the main pulmonary artery < 250 HU) did not differ significantly between the study and control groups(7 vs 10).CONCLUSION A lower dose of iodine contrast at 60 mL can be feasibly used in CTPA without resulting in a higher number of sub-optimally opacified scans.展开更多
Contrastive graph clustering(CGC)has become a prominent method for self-supervised representation learning by contrasting augmented graph data pairs.However,the performance of CGC methods critically depends on the cho...Contrastive graph clustering(CGC)has become a prominent method for self-supervised representation learning by contrasting augmented graph data pairs.However,the performance of CGC methods critically depends on the choice of data augmentation,which usually limits the capacity of network generalization.Besides,most existing methods characterize positive and negative samples based on the nodes themselves,ignoring the influence of neighbors with different hop numbers on the node.In this study,a novel self-cumulative contrastive graph clustering(SC-CGC)method is devised,which is capable of dynamically adjusting the influence of neighbors with different hops.Our intuition is that better neighbors are closer and distant ones are further away in their feature space,thus we can perform neighbor contrasting without data augmentation.To be specific,SC-CGC relies on two neural networks,i.e.,autoencoder network(AE)and graph autoencoder network(GAE),to encode the node information and graph structure,respectively.To make these two networks interact and learn from each other,a dynamic fusion mechanism is devised to transfer the knowledge learned by AE to the corresponding GAE layer by layer.Then,a self-cumulative contrastive loss function is designed to characterize the structural information by dynamically accumulating the influence of the nodes with different hops.Finally,our approach simultaneously refines the representation learning and clustering assignments in a self-supervised manner.Extensive experiments on 8 realistic datasets demonstrate that SC-CGC consistently performs better over SOTA techniques.The code is available at https://github.com/Xiaoqiang-Yan/JAS-SCCGC.展开更多
P-glycoprotein(P-gp)is a transmembrane protein widely involved in the absorption,distribution,metabolism,excretion,and toxicity(ADMET)of drugs within the human body.Accurate prediction of Pgp inhibitors and substrates...P-glycoprotein(P-gp)is a transmembrane protein widely involved in the absorption,distribution,metabolism,excretion,and toxicity(ADMET)of drugs within the human body.Accurate prediction of Pgp inhibitors and substrates is crucial for drug discovery and toxicological assessment.However,existing models rely on limited molecular information,leading to suboptimal model performance for predicting P-gp inhibitors and substrates.To overcome this challenge,we compiled an extensive dataset from public databases and literature,consisting of 5,943 P-gp inhibitors and 4,018 substrates,notable for their high quantity,quality,and structural uniqueness.In addition,we curated two external test sets to validate the model's generalization capability.Subsequently,we developed a multimodal graph contrastive learning(GCL)model for the prediction of P-gp inhibitors and substrates(MC-PGP).This framework integrates three types of features from Simplified Molecular Input Line Entry System(SMILES)sequences,molecular fingerprints,and molecular graphs using an attention-based fusion strategy to generate a unified molecular representation.Furthermore,we employed a GCL approach to enhance structural representations by aligning local and global structures.Extensive experimental results highlight the superior performance of MC-PGP,which achieves improvements in the area under the curve of receiver operating characteristic(AUC-ROC)of 9.82%and 10.62%on the external P-gp inhibitor and external P-gp substrate datasets,respectively,compared with 12 state-of-the-art methods.Furthermore,the interpretability analysis of all three molecular feature types offers comprehensive and complementary insights,demonstrating that MC-PGP effectively identifies key functional groups involved in P-gp interactions.These chemically intuitive insights provide valuable guidance for the design and optimization of drug candidates.展开更多
Quantitative phase imaging(QPI)enables non-invasive cellular analysis by utilizing cell thickness and refractive index as intrinsic probes,revolutionizing label-free microscopy in cellular research.Differential phase ...Quantitative phase imaging(QPI)enables non-invasive cellular analysis by utilizing cell thickness and refractive index as intrinsic probes,revolutionizing label-free microscopy in cellular research.Differential phase contrast(DPC),a non-interferometric QPI technique,requires only four intensity images under asymmetric illumination to recover the phase of a sample,offering the advantages of being label-free,non-coherent and highly robust.Its phase reconstruction result relies on precise modeling of the phase transfer function(PTF).However,in real optical systems,the PTF will deviate from its theoretical ideal due to the unknown wavefront aberrations,which will lead to significant artifacts and distortions in the reconstructed phase.We propose an aberration-corrected DPC(ACDPC)method that utilizes three intensity images under annular illumination to jointly retrieve the aberration and the phase,achieving high-quality QPI with minimal raw data.By employing three annular illuminations precisely matched to the numerical aperture of the objective lens,the object information is transmitted into the acquired intensity with a high signal-to-noise ratio.Phase retrieval is achieved by an iterative deconvolution algorithm that uses simulated annealing to estimate the aberration and further employs regularized deconvolution to reconstruct the phase,ultimately obtaining a refined complex pupil function and an aberration-corrected quantitative phase.We demonstrate that ACDPC is robust to multi-order aberrations without any priori knowledge,and can effectively retrieve and correct system aberrations to obtain high-quality quantitative phase.Experimental results show that ACDPC can clearly reproduce subcellular structures such as vesicles and lipid droplets with higher resolution than conventional DPC,which opens up new possibilities for more accurate subcellular structure analysis in cell biology.展开更多
Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities.Unlike existing approaches that often focus on static gestures and require extensive labeled data,the pro...Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities.Unlike existing approaches that often focus on static gestures and require extensive labeled data,the proposed wearable wristband with selfsupervised contrastive learning excels at dynamic motion tracking and adapts rapidly across multiple scenarios.It features a four-channel sensing array composed of an ionic hydrogel with hierarchical microcone structures and ultrathin flexible electrodes,resulting in high-sensitivity capacitance output.Through wireless transmission from a Wi-Fi module,the proposed algorithm learns latent features from the unlabeled signals of random wrist movements.Remarkably,only few-shot labeled data are sufficient for fine-tuning the model,enabling rapid adaptation to various tasks.The system achieves a high accuracy of 94.9%in different scenarios,including the prediction of eight-direction commands,and air-writing of all numbers and letters.The proposed method facilitates smooth transitions between multiple tasks without the need for modifying the structure or undergoing extensive task-specific training.Its utility has been further extended to enhance human–machine interaction over digital platforms,such as game controls,calculators,and three-language login systems,offering users a natural and intuitive way of communication.展开更多
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB0530000)the Discipline Construction Foundation of“Double World-class Project”.
文摘Laser wakefield accelerators(LWFAs)offer acceleration gradients up to 1000 times higher than those of conventional radio-frequency accelerators,offering a pathway to significantly more compact and cost-effective accelerator systems.This breakthrough opens up new possibilities for laboratory-scale light sources.All-optical inverse Compton scattering(AOCS)sources driven by LWFAs produce high-brightness,quasimonochromatic X rays with micrometer-scale source sizes,delivering the spatial coherence and resolution required for X-ray phase-contrast imaging(XPCI).These features position AOCS X-ray sources as promising tools for applications in biology,medicine,physics,and materials science.However,previous AOCS-based imaging studies have primarily focused on X-ray absorption imaging.In this work,we report successful experimental demonstrations of edge-enhanced in-line XPCI using energy-tunable,quasi-monochromatic AOCS X rays.With a spatial resolution of~20μm,our results clearly show the potential of high-resolution,AOCS-based XPCI applications.
基金support from the Research Council of Norway,Equinor,and Sekal with NFR project(Grant No.308826).
文摘Drill string vibration during drilling plays a vital and potentially decisive role in maintaining wellbore stability,as repeated impacts may lead to fatigue and borehole collapse.While drilling through geological layers,a material contrast may act as a localization point for wellbore damage.The hypothesis tested in this paper is that wellbore instability is focused on the boundary between the layers and that mechanical contrasts accelerate the wellbore collapse.In this study,an elastic-plastic damage model was employed to investigate the effects of repeated mechanical impacts on wellbore stability.A 2-dimensional(2D)model of a wellbore surrounded by contrasting materials was developed,and the accumulated damage caused by repeated lateral impacts was monitored.It was found that damage develops not only around the wall of the wellbore but also along the material boundaries.A sensitivity analysis was carried out to identify the impact of contrasts in both elastic(Young's modulus and Poisson's ratio)and plastic(cohesion,friction angle,and dilation angle)parameters between layers.Four damage patterns were identifiedin the simulated models.The results also suggested that the number of impacts required to reach the critical damage was highly affected by the contrast in elastic parameters,while cohesion and friction angle contrasts had a lesser effect.Additionally,increasing the contrast in the dilation angle localized the damage,thus reducing the number of impacts required to trigger wellbore failure.
基金supported by the National Natural Science Foundation of China Funded Project(Project Name:Research on Robust Adaptive Allocation Mechanism of Human Machine Co-Driving System Based on NMS Features,Project Approval Number:52172381).
文摘To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under grant No.(IFPDP-261-22).
文摘Medical imaging is essential in modern health care,allowing accurate diagnosis and effective treatment planning.These images,however,often demonstrate low contrast,noise,and brightness distortion that reduce their diagnostic reliability.This review presents a structured and comprehensive analysis of advanced histogram equalization(HE)-based techniques for medical image enhancement.Our review methodology encompasses:(1)classical HE approaches and related limitations in medical domains;(2)adaptive schemes like Adaptive Histogram Equalization(AHE)and Contrast Limited Adaptive Histogrma Equalization(CLAHE)and their advance variants;(3)brightnesspreserving schemes like BBHE and MMBEBHE and related algorithms;(4)dynamic and recursive histogram equalization methods incorporating DHE and RMSHE;(5)fuzzy logic-based enhancement methodologies addressing uncertainty and noise in medical images;and(6)hybrid optimization methodologies through the application of metaheuristic algorithms(World Cup Optimization,Particle Swarm Optimization,Genetic Algorithms,along with histogram-based methodologies.)There is also a comparative discussion given based on contrast improvement,image brightness preservation,noise management,and computational efficiency.Such advancements have better capabilities of improving image quality,which is more important for improved diagnosis and image analysis.
基金supported by the Beijing Natural Science Foundation(Grant Nos.F251036 and L248103)CAS Project for Young Scientists in Basic Research(Grant Nos.YSBR-090 and YSBR-05)National Natural Science Foundation of China(Grant No.62274159).
文摘Accurate temperature control and effective oxide removal are essential for achieving high-quality epitaxial growth in molecular beam epitaxy(MBE).However,traditional methods often rely on manual identification of reflection high-energy electron diffraction(RHEED)patterns.This process is heavily influenced by the grower’s experience,leading to issues with reproducibility and limiting the potential for automation.In this report,we propose an unsupervised learning framework for realtime RHEED analysis during the deoxidation process.By incorporating temporal similarity constraints into contrastive learning,our model generates smooth and interpretable feature trajectories that illustrate transitions in the deoxidation state,thus eliminating the need for manual labeling.The model,pre-trained using grouped contrastive loss,shows significant improvement in RHEED feature boundary discrimination and localization of critical regions.We evaluated its generalizability through two transfer learning strategies:calibration-free clustering and few-shot fine-tuning.The pre-trained model achieved a clustering accuracy of 88.1%for GaAs deoxidation samples without additional labels and reached an accuracy of 94.3%to 95.5%after fine-tuning with just five sample pairs across GaAs,Ge,and InAs substrates.This framework is optimized for resource-constrained edge devices,allowing for real-time,plug-and-play integration with existing MBE systems and swift adaptation across various materials and equipment.This work paves the way for greater automation and improved reproducibility in semiconductor manufacturing.
基金supported by the Natural Science Foundation of Inner Mongolia Autonomous Region of China(No.2023QN04011)the National Natural Science Foundation of China(Nos.42307092 and 52279067)+1 种基金Ordos Science and Technology Major Project(No.ZD20232303)Project of Key Laboratory of River and Lake in Inner Mongolia Autonomous Region(No.2022QZBZ0003).
文摘Lacustrine groundwater discharge(LGD)plays an important role in water resources management.Previous studies have focused on LGD process in a single lake,but the differences in LGD process within the same region have not been thoroughly investigated.In this study,multiple tracers(hydrochemistry,𝛿D,𝛿18O and 222Rn)were used to compare mechanisms of LGD in Daihai and Ulansuhai Lake in Inner Mongoli1,Northwest China.The hydrochemical types showed a trend from groundwater to lake water,indicating a hydraulic connection between them.In addition,the𝛿D and𝛿18O values of sediment pore water were between the groundwater and lake water,indicating the LGD processes.The radon mass balance model was used to estimate the average groundwater discharge rates of Daihai and Ulansuhai Lake,which were 2.79 mm/day and 3.02 mm/day,respectively.The total nitrogen(TN),total phosphorus(TP),and fluoride inputs associated with LGD in Daihai Lake accounted for 97.52%,96.59%,and 95.84%of the total inputs,respectively.In contrast,TN,TP and fluoride inputs in Ulansuhai Lake were 53.56%,40.98%,and 36.25%,respectively.This indicates that the pollutant inputs associated with LGD posed a potential threat to the ecological stability of Daihai and Ulansuhai Lake.By comparison,the differences of LGD process and associated pollutant flux were controlled by hydrogeological conditions,lakebed permeability and human activities.This study provides a reference for water resources management in Daihai and Ulansuhai Lake basins while improving the understanding of LGD in the Yellow River basin.
文摘The morbidity rate of primary cardiac tumors(PCTs)is only 0.0138%.[1]Calcified amorphous tumors(CATs)are a particularly rare entity with only a few cases reported in the literature,and account for only 2.47%of PCTs.[2]CATs can occur at any age and have been identified at various intracardiac locations.The clinical manifestations of patients are related to the location and size of the lesion.
基金supported by the National Natural Science Foundation of China(62225303,62403043,62433004)the Beijing Natural Science Foundation(4244085)+1 种基金the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(GZC20230203)the China Postdoctoral Science Foundation(2023M740201)。
文摘Deep graph contrastive clustering has attracted widespread attentions due to its self-supervised representation learning paradigm and superior clustering performance.Although,two challenges emerge and result in high computational costs.Most existing contrastive methods adopt the data augmentation and then representation learning strategy,where representation learning with trainable graph convolution is coupled with complex and fixed data augmentation,inevitably limiting the efficiency and flexibility.The similarity metric between positive-negative sample pairs is complex and contrastive objective is partial,limiting the discriminability of representation learning.To solve these challenges,a novel wide graph clustering network(WGCN)adhering to representation and then augmentation framework is proposed,which mainly consists of multiorder filter fusion(MFF)and double-level contrastive learning(DCL)modules.Specifically,the MFF module integrates multiorder low-pass filters to extract smooth and multi-scale topological features,utilizing self-attention fusion to reduce redundancy and obtain comprehensive embedding representation.Further,the DCL module constructs two augmented views by the parallel parameter-unshared Siamese encoders rather than complex augmentations on graph.To achieve simple yet effective self-supervised learning,representation self-supervision and structural consistency oriented double-level contrastive loss is designed,where representation self-supervision maximizes the agreement between pairwise augmented embedding representations and structural consistency promotes the mutual information correlation between appending neighborhoods with similar semantics.Extensive experiments on six benchmark datasets demonstrate the superiority of the proposed WGCN,especially highlighting its time-saving characteristic.The code could be available in the https://github.com/Tianxiang Zhao0474/WGCN.
文摘AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited adaptive histogram equalization(NICLAHE)to improve retinal image contrast was suggested to aid in the accurate identification of retinal disorders and improve the visibility of fine retinal structures.Additionally,a minimal-order filter was applied to effectively denoise the images without compromising important retinal structures.The novel NICLAHE algorithm was inspired by the classical CLAHE algorithm,but enhanced it by selecting the clip limits and tile sized in a dynamical manner relative to the pixel values in an image as opposed to using fixed values.It was evaluated on the Drive and high-resolution fundus(HRF)datasets on conventional quality measures.RESULTS:The new proposed preprocessing technique was applied to two retinal image databases,Drive and HRF,with four quality metrics being,root mean square error(RMSE),peak signal to noise ratio(PSNR),root mean square contrast(RMSC),and overall contrast.The technique performed superiorly on both the data sets as compared to the traditional enhancement methods.In order to assess the compatibility of the method with automated diagnosis,a deep learning framework named ResNet was applied in the segmentation of retinal blood vessels.Sensitivity,specificity,precision and accuracy were used to analyse the performance.NICLAHE–enhanced images outperformed the traditional techniques on both the datasets with improved accuracy.CONCLUSION:NICLAHE provides better results than traditional methods with less error and improved contrastrelated values.These enhanced images are subsequently measured by sensitivity,specificity,precision,and accuracy,which yield a better result in both datasets.
文摘BACKGROUND Oil-based iodinated contrast media have excellent contrast properties and are widely used for hysterosalpingographic evaluation of female infertility.On abdominal radiography and computed tomography(CT)scans,their radiodensity is similar to that of metallic objects,which can sometimes lead to diagnostic confusion in the postoperative settings.In this case,retained oil-based contrast medium was observed on an abdominal radiograph following a cesarean section,making it difficult to differentiate from an intraperitoneal foreign body from surgery.The patient was a 37-year-old pregnant woman who was referred to our hospital at 32 weeks and 1 day of pregnancy due to complete placenta previa for mana-gement of pregnancy and delivery.An elective cesarean section was performed at 37 weeks and 3 days.A plain abdominal radiograph taken immediately after surgery revealed a near-round,hyperdense,mass-like shadow with a regular margin in the pelvic cavity.An intraperitoneal foreign body was suspected;therefore,an abdominal CT scan was performed.The foreign body was located on the left side of the pouch of Douglas and had a CT value of 7000 Hounsfield units,similar to that of metals.The CT value strongly suggested the presence of an artificial object.However,further inquiries with the patient and her previous physician revealed a history of hysterosalpingography.Accordingly,retained oil-based iodinated contrast medium was suspected,and observation of the object’s course was adopted.CONCLUSION When intraperitoneal foreign bodies are suspected on postoperative radiographs,the possibility of oil-based iodinated contrast medium retention should be considered.
文摘BACKGROUND Gastrointestinal dual-contrast ultrasonography(DCUS)is characterized by its high resolution,sensitivity,and specificity.AIM To determine the accuracy of DCUS in predicting lymph node metastasis in middle-aged and elderly patients with gastric cancer(GC).METHODS A total of 100 middle-aged and elderly patients with GC admitted to the Fourth Affiliated Hospital of Soochow University(Dushu Lake Hospital,Suzhou,China)between April 2022 and April 2024 were selected.The baseline data and lymph node metastasis status were collected.DCUS combined with intravenous contrast technology was used to calculate the enhancement time(ET),time to peak(TTP),and slope of the ascending branch wash-in rate(WIR).These indicators were used in assessing lymph node metastasis in patients with GC.RESULTS Among 100 middle-aged and elderly patients with GC,35(35.00%)had lymph node metastases.GC patients with lymph node metastasis had a higher propor-tion of stage II TNM classification and higher WIR values than those without lymph node metastasis.The ET and TTP values were lower in patients with lymph node metastases,and all differences were statistically significant(P<0.05).The area under the curve values for ET,TTP,WIR,and combined diagnosis of GC lymph node metastasis using DCUS were all>0.7.Optimal assessment was achieved when the cutoff values for ET,TTP,and WIR were set at 16.32 seconds,10.67 seconds,and 7.02,res-pectively.CONCLUSION DCUS-mediated assessment of ET,TTP,and WIR can effectively predict and evaluate lymph node metastasis status in patients with GC,with higher sensitivity when used in combination.
文摘Patent foramen ovale(PFO)is a common congenital heart disorder associated with stroke,decompression sickness and migraine.Combining synchronized contrast transcranial Doppler with contrast transthoracic echocardiography has important clinical significance and can improve the accuracy of detecting right-left shunts(RLSs)in patients with PFO.In this letter,regarding an original study presented by Yao et al,we present our insights and discuss how to better help clinicians evaluate changes in PFO-related RLS.
基金supported by the National Key Research and Development Program of China(No.2022YFB380-7300)the National Natural Science Foundation of China(No.12471455)+2 种基金the Clinical Cohort Construction Program of Peking University Third Hospital(BYSYDL2022005)the Key Clinical Projects of Peking University Third Hospital(BYSYZD2023006)the Innovation&Transfer Fund of Peking University Third Hospital(BYSYZHKC2023-109).
文摘Kounis syndrome(KS)is a rare but clinically significant condition characterized by the simultaneous occurrence of acute coronary syndrome(ACS)and allergic reactions,which can develop in patients with either normal or diseased coronary arteries.[1,2]The condition is typically triggered by various allergens including medications(particularly contrast media),environmental factors,or food exposures,with symptom onset usually occurring within one hour of exposure.
文摘Ultrasmall superparamagnetic iron oxide nanoparticles(usSPIONs)are promising alternatives to gadolinium‐based contrast agents for positive contrast enhancement in magnetic resonance imaging(MRI).Unlike larger SPIONs that primarily function as T2/T2*negative contrast agents,usSPIONs with core diameters below 5 nm can effectively shorten T1 relaxation times,producing bright signals in T1‐weighted images.This distinct behavior stems from their unique magnetic properties,including single‐domain configurations,surface spin canting,and rapid Néel relaxation dynamics,which are particularly enhanced at low magnetic field strengths.The biocompatibility of iron oxide,efficient renal clearance pathways,and versatility for surface functionalization offer potential advantages over gadolinium‐based agents,especially regarding safety concerns related to nephrogenic systemic fibrosis and gadolinium deposition.These nanoparticles show particular promise for applications in lowfield MRI,vascular imaging,targeted molecular imaging,and theranostic platforms.Although challenges remain in optimizing synthesis methods for consistent production of monodisperse usSPIONs with tailored surface chemistry,ongoing research continues to advance their potential for clinical translation.This review explores the mechanisms,synthesis approaches,applications,and future perspectives of usSPIONs as positive contrast agents in MRI.
文摘BACKGROUND Juvenile polyps(JPs)are non-neoplastic polyps.In adults,JPs present with hematochezia in only approximately half the patients and are often found incidentally during endoscopic screening.JPs have no mucosal fascia at the tip,and spontaneous shedding and massive gastrointestinal hemorrhage may occur.Thus,the JP bleeding detected in this case by extravascular contrast leakage on computed tomography scans and treated with endoscopic clipping is rare.CASE SUMMARY A previously healthy 31-year-old male patient presented with a 2-day history of bloody stools.Upon hospital arrival,rectal examination revealed fresh blood,and abdominal computed tomography scans showed extravascular contrast leakage from the lower rectum’s left-side wall.His blood pressure was slightly low at 104/62 mmHg.However,his pulse rate(69 bpm)and oxygen level(99%on room air)were within normal limits.Emergency endoscopy revealed a pedunculated lesion in the rectum covered by a non-neoplastic mucosal epithelium.No neoplastic lesions were observed at the tip of the polyp;however,pulsatile bleeding was detected at the distal end.We performed endoscopic hemostasis by clipping the stem and then performed a polypectomy above the stem to examine the lesion tissue.Histopathological evaluation revealed a cystically dilated gland without neoplastic lesions.A subsequent total colonoscopy revealed two JPs with characteristic edematous,smooth,and reddish surfaces close to the hemorrhagic lesion.Subsequent histopathological evaluation indicated findings characteristic of JP,such as severe inflammatory cell infiltration of the stroma and cystic dilatation of the glandular ducts.CONCLUSION There are no reports of adult JPs presenting with contrast extravasation where endoscopic hemostasis was successful,as in this case.
文摘AIM To evaluate the feasibility of reducing the dose of iodinated contrast agent in computed tomography pulmonary angiography(CTPA). METHODS One hundred and twenty-seven patients clinically suspected of having pulmonary embolism underwent spiral CTPA, out of whom fifty-seven received 75 mL and the remaining seventy a lower dose of 60 mL of contrast agent. Both doses were administered in a multiphasic injection. A minimum opacification threshold of 250 Hounsfield units(HU) in the main pulmonary artery is used for assessing the technical adequacy of the scans. RESULTS Mean opacification was found to be positively correlated to patient age(Pearson's correlation 0.4255, P < 0.0001) and independent of gender(male:female, 425.6 vs 450.4,P = 0.34). When age is accounted for, the study and control groups did not differ significantly in their mean opacification in the main(436.8 vs 437.9, P = 0.48),left(416.6 vs 419.8, P = 0.45) or the right pulmonary arteries(417.3 vs 423.5, P = 0.40). The number of sub-optimally opacified scans(the mean opacification in the main pulmonary artery < 250 HU) did not differ significantly between the study and control groups(7 vs 10).CONCLUSION A lower dose of iodine contrast at 60 mL can be feasibly used in CTPA without resulting in a higher number of sub-optimally opacified scans.
基金supported by the National Natural Science Foundation of China(62371423,62450002,62425107)China Postdoctoral Science Foundation(2020M682357).
文摘Contrastive graph clustering(CGC)has become a prominent method for self-supervised representation learning by contrasting augmented graph data pairs.However,the performance of CGC methods critically depends on the choice of data augmentation,which usually limits the capacity of network generalization.Besides,most existing methods characterize positive and negative samples based on the nodes themselves,ignoring the influence of neighbors with different hop numbers on the node.In this study,a novel self-cumulative contrastive graph clustering(SC-CGC)method is devised,which is capable of dynamically adjusting the influence of neighbors with different hops.Our intuition is that better neighbors are closer and distant ones are further away in their feature space,thus we can perform neighbor contrasting without data augmentation.To be specific,SC-CGC relies on two neural networks,i.e.,autoencoder network(AE)and graph autoencoder network(GAE),to encode the node information and graph structure,respectively.To make these two networks interact and learn from each other,a dynamic fusion mechanism is devised to transfer the knowledge learned by AE to the corresponding GAE layer by layer.Then,a self-cumulative contrastive loss function is designed to characterize the structural information by dynamically accumulating the influence of the nodes with different hops.Finally,our approach simultaneously refines the representation learning and clustering assignments in a self-supervised manner.Extensive experiments on 8 realistic datasets demonstrate that SC-CGC consistently performs better over SOTA techniques.The code is available at https://github.com/Xiaoqiang-Yan/JAS-SCCGC.
基金supported by the National Key Research and Development Program of China(Program No.:2022YFF1203003)the National Natural Science Foundation of China(Grant No.:82373791).
文摘P-glycoprotein(P-gp)is a transmembrane protein widely involved in the absorption,distribution,metabolism,excretion,and toxicity(ADMET)of drugs within the human body.Accurate prediction of Pgp inhibitors and substrates is crucial for drug discovery and toxicological assessment.However,existing models rely on limited molecular information,leading to suboptimal model performance for predicting P-gp inhibitors and substrates.To overcome this challenge,we compiled an extensive dataset from public databases and literature,consisting of 5,943 P-gp inhibitors and 4,018 substrates,notable for their high quantity,quality,and structural uniqueness.In addition,we curated two external test sets to validate the model's generalization capability.Subsequently,we developed a multimodal graph contrastive learning(GCL)model for the prediction of P-gp inhibitors and substrates(MC-PGP).This framework integrates three types of features from Simplified Molecular Input Line Entry System(SMILES)sequences,molecular fingerprints,and molecular graphs using an attention-based fusion strategy to generate a unified molecular representation.Furthermore,we employed a GCL approach to enhance structural representations by aligning local and global structures.Extensive experimental results highlight the superior performance of MC-PGP,which achieves improvements in the area under the curve of receiver operating characteristic(AUC-ROC)of 9.82%and 10.62%on the external P-gp inhibitor and external P-gp substrate datasets,respectively,compared with 12 state-of-the-art methods.Furthermore,the interpretability analysis of all three molecular feature types offers comprehensive and complementary insights,demonstrating that MC-PGP effectively identifies key functional groups involved in P-gp interactions.These chemically intuitive insights provide valuable guidance for the design and optimization of drug candidates.
基金supported by the National Natural Science Foundation of China(62305162,62227818,62361136588)China Postdoctoral Science Foundation(2023TQ0160,2023M731683)+5 种基金Nanjing University of Science and Technology independent research project(30923010305)National Key Research and Development Program of China(2024YFE0101300)Biomedical Competition Foundation of Jiangsu Province(BE2022847)Key National Industrial Technology Cooperation Foundation of Jiangsu Province(BZ2022039)Fundamental Research Funds for the Central Universities(2023102001)Open Research Fund of Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense(JSGP202105,JSGP202201,JSGPCXZNGZ202401)。
文摘Quantitative phase imaging(QPI)enables non-invasive cellular analysis by utilizing cell thickness and refractive index as intrinsic probes,revolutionizing label-free microscopy in cellular research.Differential phase contrast(DPC),a non-interferometric QPI technique,requires only four intensity images under asymmetric illumination to recover the phase of a sample,offering the advantages of being label-free,non-coherent and highly robust.Its phase reconstruction result relies on precise modeling of the phase transfer function(PTF).However,in real optical systems,the PTF will deviate from its theoretical ideal due to the unknown wavefront aberrations,which will lead to significant artifacts and distortions in the reconstructed phase.We propose an aberration-corrected DPC(ACDPC)method that utilizes three intensity images under annular illumination to jointly retrieve the aberration and the phase,achieving high-quality QPI with minimal raw data.By employing three annular illuminations precisely matched to the numerical aperture of the objective lens,the object information is transmitted into the acquired intensity with a high signal-to-noise ratio.Phase retrieval is achieved by an iterative deconvolution algorithm that uses simulated annealing to estimate the aberration and further employs regularized deconvolution to reconstruct the phase,ultimately obtaining a refined complex pupil function and an aberration-corrected quantitative phase.We demonstrate that ACDPC is robust to multi-order aberrations without any priori knowledge,and can effectively retrieve and correct system aberrations to obtain high-quality quantitative phase.Experimental results show that ACDPC can clearly reproduce subcellular structures such as vesicles and lipid droplets with higher resolution than conventional DPC,which opens up new possibilities for more accurate subcellular structure analysis in cell biology.
基金supported by the Research Grant Fund from Kwangwoon University in 2023,the National Natural Science Foundation of China under Grant(62311540155)the Taishan Scholars Project Special Funds(tsqn202312035)the open research foundation of State Key Laboratory of Integrated Chips and Systems.
文摘Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities.Unlike existing approaches that often focus on static gestures and require extensive labeled data,the proposed wearable wristband with selfsupervised contrastive learning excels at dynamic motion tracking and adapts rapidly across multiple scenarios.It features a four-channel sensing array composed of an ionic hydrogel with hierarchical microcone structures and ultrathin flexible electrodes,resulting in high-sensitivity capacitance output.Through wireless transmission from a Wi-Fi module,the proposed algorithm learns latent features from the unlabeled signals of random wrist movements.Remarkably,only few-shot labeled data are sufficient for fine-tuning the model,enabling rapid adaptation to various tasks.The system achieves a high accuracy of 94.9%in different scenarios,including the prediction of eight-direction commands,and air-writing of all numbers and letters.The proposed method facilitates smooth transitions between multiple tasks without the need for modifying the structure or undergoing extensive task-specific training.Its utility has been further extended to enhance human–machine interaction over digital platforms,such as game controls,calculators,and three-language login systems,offering users a natural and intuitive way of communication.
