BACKGROUND The detection rate of peptic ulcer in children is improving,with development of diagnostic procedures.Gastroscopy is the gold standard for the diagnosis of peptic ulcer,but it is an invasive procedure.Gastr...BACKGROUND The detection rate of peptic ulcer in children is improving,with development of diagnostic procedures.Gastroscopy is the gold standard for the diagnosis of peptic ulcer,but it is an invasive procedure.Gastrointestinal contrast-enhanced ultrasonography(CEUS)has the advantages of being painless,noninvasive,nonradioactive,easy to use,and safe.AIM To investigate the clinical value of CEUS for diagnosis and treatment of peptic ulcer in children.METHODS We investigated 43 children with digestive tract symptoms in our hospital from January 2021 to June 2022.All children were examined by routine ultrasound,gastrointestinal CEUS,and gastroscopy.The pathological results of gastroscopy were taken as the gold standard.Routine ultrasonography was performed before gastrointestinal CEUS.Conventional ultrasound showed the thickness of the gastroduodenal wall,gastric peristalsis,and the adjacent organs and tissues around the abdominal cavity.Gastrointestinal CEUS recorded the thickness of the gastroduodenal wall;the size,location and shape of the ulcer;gastric peristalsis;and adjacent organs and tissues around the abdominal cavity.The results of routine ultrasound and gastrointestinal ultrasound were compared with those of gastroscopy to evaluate the diagnostic results and coincidence rate of routine ultrasound and gastrointestinal CEUS.All children received informed consent from their guardians for CEUS.This study was reviewed and approved by the hospital medical ethics committee.RESULTS Among the 43 children,17(15 male,2 female)were diagnosed with peptic ulcer by gastroscopy.There were 26 children with nonpeptic ulcer.There were eight cases of peptic ulcer and 35 of nonpeptic ulcer diagnosed by conventional ultrasound.The diagnostic coincidence rate of peptic ulcer in children diagnosed by conventional ultrasound was 79.1%(34/43),which was significantly different from that of gastroscopy(P=0.033).It indicates that the coincidence rate of gastrointestinal contrast-enhanced ultrasound and gastroscope is low.Fifteen cases of peptic ulcer and 28 of nonpeptic ulcer were diagnosed by CEUS.The diagnostic coincidence rate of peptic ulcer in children was 95.3%(41/43).There was no significant difference between CEUS and gastroscopy(P=0.655).It indicates that the coincidence rate of gastrointestinal contrast-enhanced ultrasound and gastroscope is high.CONCLUSION Gastrointestinal CEUS has a high coincidence rate in the diagnosis of peptic ulcer in children,and can be used as a preliminary examination method.展开更多
One of the basic characteristics of Earth's modern climate is that the Northern Hemisphere(NH) is climatologically warmer than the Southern Hemisphere(SH). Here, model performances of this basic state are examined...One of the basic characteristics of Earth's modern climate is that the Northern Hemisphere(NH) is climatologically warmer than the Southern Hemisphere(SH). Here, model performances of this basic state are examined using simulation results from 26 CMIP6 models. Results show that the CMIP6 models underestimate the contrast in interhemispheric surface temperatures on average(0.8 K for CMIP6 mean versus 1.4 K for reanalysis data mean), and that there is a large intermodel spread, ranging from -0.7 K to 2.3 K. A box model energy budget analysis shows that the contrast in interhemispheric shortwave absorption at the top of the atmosphere, the contrast in interhemispheric greenhouse trapping, and the crossequatorial northward ocean heat transport, are all underestimated in the multimodel mean. By examining the intermodel spread, we find intermodel biases can be tracked back to biases in midlatitude shortwave cloud forcing in AGCMs. Models with a weaker interhemispheric temperature contrast underestimate the shortwave cloud reflection in the SH but overestimate the shortwave cloud reflection in the NH, which are respectively due to underestimation of the cloud fraction over the SH extratropical ocean and overestimation of the cloud liquid water content over the NH extratropical continents.Models that underestimate the interhemispheric temperature contrast exhibit larger double ITCZ biases, characterized by excessive precipitation in the SH tropics. Although this intermodel spread does not account for the multimodel ensemble mean biases, it highlights that improving cloud simulation in AGCMs is essential for simulating the climate realistically in coupled models.展开更多
The optical design of near-infrared phase contrast imaging(NI-PCI)diagnosis on HL-2A is introduced in this paper.This scheme benefits from the great progress of near-infrared laser technology and is a broadening of tr...The optical design of near-infrared phase contrast imaging(NI-PCI)diagnosis on HL-2A is introduced in this paper.This scheme benefits from the great progress of near-infrared laser technology and is a broadening of traditional phase contrast technology.This diagnostic can work as a keen tool to measure plasma wavenumber spectra by inferring string-integrated plasma density fluctuations.Design of both the front optical path which is the path before the laser transmitting into the tokamak plasma and the rear optics which is the path after the laser passing through the plasma is detailed.The 1550 nm laser is chosen as the probe beam and highprecision optical components are designed to fit the laser beam,in which a phase plate with a 194-nm-deep silver groove is the key.Compared with the conventional 10.6μm laser-based PCI system on HL-2A,NI-PCI significantly overcomes the unwanted phase scintillation effect and promotes the measurement capability of high-wavenumber turbulence with an increased maximal measurable wavenumber from 15 cm^(-1)to 32.6 cm^(-1).展开更多
Knowledge graph can assist in improving recommendation performance and is widely applied in various person-alized recommendation domains.However,existing knowledge-aware recommendation methods face challenges such as ...Knowledge graph can assist in improving recommendation performance and is widely applied in various person-alized recommendation domains.However,existing knowledge-aware recommendation methods face challenges such as weak user-item interaction supervisory signals and noise in the knowledge graph.To tackle these issues,this paper proposes a neighbor information contrast-enhanced recommendation method by adding subtle noise to construct contrast views and employing contrastive learning to strengthen supervisory signals and reduce knowledge noise.Specifically,first,this paper adopts heterogeneous propagation and knowledge-aware attention networks to obtain multi-order neighbor embedding of users and items,mining the high-order neighbor informa-tion of users and items.Next,in the neighbor information,this paper introduces weak noise following a uniform distribution to construct neighbor contrast views,effectively reducing the time overhead of view construction.This paper then performs contrastive learning between neighbor views to promote the uniformity of view information,adjusting the neighbor structure,and achieving the goal of reducing the knowledge noise in the knowledge graph.Finally,this paper introduces multi-task learning to mitigate the problem of weak supervisory signals.To validate the effectiveness of our method,experiments are conducted on theMovieLens-1M,MovieLens-20M,Book-Crossing,and Last-FM datasets.The results showthat compared to the best baselines,our method shows significant improvements in AUC and F1.展开更多
In order to address the problem of high false alarm rate and low probabilities of infrared small target detection in complex low-altitude background,an infrared small target detection method based on improved weighted...In order to address the problem of high false alarm rate and low probabilities of infrared small target detection in complex low-altitude background,an infrared small target detection method based on improved weighted local contrast is proposed in this paper.First,the ratio information between the target and local background is utilized as an enhancement factor.The local contrast is calculated by incorporating the heterogeneity between the target and local background.Then,a local product weighted method is designed based on the spatial dissimilarity between target and background to further enhance target while suppressing background.Finally,the location of target is obtained by adaptive threshold segmentation.As experimental results demonstrate,the method shows superior performance in several evaluation metrics compared with six existing algorithms on different datasets containing targets such as unmanned aerial vehicles(UAV).展开更多
This paper discusses the inheritance and application of Chinese character reverse contrast typeface style.It begins by analyzing the visual features of Western reverse contrast typeface styles,with a focus on Caslon I...This paper discusses the inheritance and application of Chinese character reverse contrast typeface style.It begins by analyzing the visual features of Western reverse contrast typeface styles,with a focus on Caslon Italian and French Clarendon,providing a Western perspective reference for subsequent Chinese character reverse contrast typeface style designs.The paper then traces the origins of the Chinese reverse contrast style,from the calligraphy style"Lacquer Script"to the earliest printing type"フワンテール形",exploring the historical background and cultural significance of the Chinese reverse contrast style.In the methodology section of Chinese character reverse contrast typeface style design,the discussion is conducted from two dimensions:inheritance and application.In terms of inheritance,through an in-depth analysis of"Lacquer Script"and"フワンテール形"typeface style,the paper summarizes three basic theories for modern Chinese character reverse contrast typeface style design.In the application section,it examines in detail the two most influential recent typeface styles,"Ribaasu"and"Basic Artistic",outlining three directions of application:extreme horizontal stroke variations,exaggerated contrast,and diverse decorative strokes,showcasing new directions and possibilities for Chinese character reverse contrast typeface style design.This paper not only reviews the developmental history of the Chinese character reverse contrast typeface style but also analyzes the design methodology of Chinese character reverse contrast typeface style through specific case studies.展开更多
Introduction: Near-infrared fluorescence imaging is a technique that will establish itself in the short term at the international level because it is recognized for its potential to improve the performance of surgical...Introduction: Near-infrared fluorescence imaging is a technique that will establish itself in the short term at the international level because it is recognized for its potential to improve the performance of surgical interventions, its moderate investment and operating costs and its portability. Although the technology is now mature, there is currently the problem of the availability of contrast agents to be injected IV. The aim of this methodology article is to propose an alternative solution to the need for contrast agents for clinical research, particularly in oncology. Methodology: They consist of coupling a fluorescent marker in the form of an NHS derivative, such as IR DYE manufactured in compliance with GMP, with therapeutic monoclonal antibodies having marketing authorization for molecular imaging. For a given antibody, the marking procedure must be the subject of a validation file on the final preparation filtered on a sterilizing membrane at 0.22 μm. Once the procedure has been validated, it would be unnecessary to repeat the tests before each clinical research examination. A check of the marking by thin-layer chromatography (TLC) and place it in a sample bank at +4˚C for 1 month of each injected formulation would be sufficient for additional tests if necessary. Conclusion: Molecular near-infrared fluorescence imaging is experiencing development, the process of which could be accelerated by greater availability of clinical contrast agents. Alternative solutions are therefore necessary to promote clinical research in this area. These methods must be shared to make it easier for researchers.展开更多
BACKGROUND Ischemic stroke is an entity with high incidence,morbidity,and mortality rates.Carotid artery stenosis is an important and independent risk factor for ischemic stroke.The three current approaches for treati...BACKGROUND Ischemic stroke is an entity with high incidence,morbidity,and mortality rates.Carotid artery stenosis is an important and independent risk factor for ischemic stroke.The three current approaches for treating carotid artery stenosis are drug treatment,carotid endarterectomy(CEA),carotid angioplasty and stenting(CAS).The approach is chosen based on the degree of stenosis.CEA or CAS could have been chosen for the current patient,who had severe carotid stenosis and an iodinated contrast allergy.After thoroughly communicating with the patient,the patient chose CAS for treatment.Therefore,we performed ultrasound-guided CAS to avoid the use of iodinated contrast.CASE SUMMARY The main symptoms of the patient were numbness and weakness of the left limb.Computed tomography angiography of the head and neck at another hospital indicated multiple sites of stenosis in the arteries of the head and neck.The patient requested CAS for treatment but was allergic to iodinated contrast media.Thus,routine digital subtraction angiography(DSA)with iodinated contrast could not be used for the procedure.The diagnosis of this patient was as follows:(1)Right parietal lobe cerebral infarction;(2)multiple sites of stenosis in the arteries of the head and neck(severe stenosis of the right internal carotid artery,severe stenosis of the right subclavian artery);(3)right subclavian steal syndrome;and(4)hypertension(stage 3,high risk).The interventions included routine treatment for cerebral infarction,oral administration of clopidogrel(75 mg qd)and aspirin(100 mg qd),ultrasound-guided CAS,and postoperative follow-up.Postoperative color Doppler ultrasound and cerebrovascular magnetic resonance angiography of the carotid artery showed good vascular recovery,and the postoperative follow-up indicated a good prognosis.CONCLUSION This case study suggests that ultrasound-guided endovascular treatment is a potential option for patients with contraindications to the iodinated contrast agents used in DSA-guided surgery,although excellent surgical operating skills are needed.展开更多
BACKGROUND Contrast-induced encephalopathy(CIE)is a rare transient,reversible abnormality in the structure or function of the nervous system caused by the intravascular use of contrast agents.CIE can present with a ra...BACKGROUND Contrast-induced encephalopathy(CIE)is a rare transient,reversible abnormality in the structure or function of the nervous system caused by the intravascular use of contrast agents.CIE can present with a range of neurological manifestations,including focal neurological deficits(hemiplegia,hemianopia,cortical blindness,aphasia,and parkinsonism)and systemic symptoms(confusion,seizures,and coma).However,if not accurately diagnosed and treated in a timely manner,CIE can cause irreversible damage to patients,especially critically ill patients.CASE SUMMARY A male in his 50 s,2 h after digital subtraction angiography,had a progressive disorder of consciousness,mixed aphasia,bilateral pupillary sluggish light reflex,and right limb weakness.Seven hours after the procedure,he developed unconsciousness,high fever(39.5°C),seizures,hemiplegia,neck stiffness(+),and right Babinski signs(+).computed tomography(CT)findings 2 h postprocedure were very confusing and led us to misdiagnose the patient with subarachnoid hemorrhage.Brain CT was performed again 7 h after the procedure.Compared with the CT 2 h after the procedure,the CT 7 h after the procedure showed that the manifestations of subarachnoid hemorrhage in the left cerebral hemisphere had disappeared and were replaced by brain tissue swelling,and the cerebral sulci had disappeared.Combined with the clinical manifestations of the patient and after the exclusion of subarachnoid hemorrhage and cerebrovascular embolism,we diagnosed the patient with CIE,and intravenous fluids were given for adequate hydration,as well as mannitol,albumin dehydration,furosemide and the glucocorticoid methylprednisolone.After 17 d of active treatment,the patient was discharged with no sequelae.CONCLUSION CIE should be taken seriously,but it is easily misdiagnosed,and once CIE is diagnosed,rapid,accurate diagnosis and treatment are critical steps.Whether a follow-up examination using a contrast agent can be performed should be closely evaluated,and the patient should be fully informed of the associated risks.展开更多
Previous deep learning-based super-resolution(SR)methods rely on the assumption that the degradation process is predefined(e.g.,bicubic downsampling).Thus,their performance would suffer from deterioration if the real ...Previous deep learning-based super-resolution(SR)methods rely on the assumption that the degradation process is predefined(e.g.,bicubic downsampling).Thus,their performance would suffer from deterioration if the real degradation is not consistent with the assumption.To deal with real-world scenarios,existing blind SR methods are committed to estimating both the degradation and the super-resolved image with an extra loss or iterative scheme.However,degradation estimation that requires more computation would result in limited SR performance due to the accumulated estimation errors.In this paper,we propose a contrastive regularization built upon contrastive learning to exploit both the information of blurry images and clear images as negative and positive samples,respectively.Contrastive regularization ensures that the restored image is pulled closer to the clear image and pushed far away from the blurry image in the representation space.Furthermore,instead of estimating the degradation,we extract global statistical prior information to capture the character of the distortion.Considering the coupling between the degradation and the low-resolution image,we embed the global prior into the distortion-specific SR network to make our method adaptive to the changes of distortions.We term our distortion-specific network with contrastive regularization as CRDNet.The extensive experiments on synthetic and realworld scenes demonstrate that our lightweight CRDNet surpasses state-of-the-art blind super-resolution approaches.展开更多
System logs are essential for detecting anomalies,querying faults,and tracing attacks.Because of the time-consuming and labor-intensive nature of manual system troubleshooting and anomaly detection,it cannot meet the ...System logs are essential for detecting anomalies,querying faults,and tracing attacks.Because of the time-consuming and labor-intensive nature of manual system troubleshooting and anomaly detection,it cannot meet the actual needs.The implementation of automated log anomaly detection is a topic that demands urgent research.However,the prior work on processing log data is mainly one-dimensional and cannot profoundly learn the complex associations in log data.Meanwhile,there is a lack of attention to the utilization of log labels and usually relies on a large number of labels for detection.This paper proposes a novel and practical detection model named LCC-HGLog,the core of which is the conversion of log anomaly detection into a graph classification problem.Semantic temporal graphs(STG)are constructed by extracting the raw logs’execution sequences and template semantics.Then a unique graph classifier is used to better comprehend each STG’s semantic,sequential,and structural features.The classification model is trained jointly by graph classification loss and label contrastive loss.While achieving discriminability at the class-level,it increases the fine-grained identification at the instance-level,thus achieving detection performance even with a small amount of labeled data.We have conducted numerous experiments on real log datasets,showing that the proposed model outperforms the baseline methods and obtains the best all-around performance.Moreover,the detection performance degrades to less than 1%when only 10%of the labeled data is used.With 200 labeled samples,we can achieve the same or better detection results than the baseline methods.展开更多
Colorless‐to‐black switching has attracted widespread attention for smart windows and multifunctional displays because they are more useful to control solar energy.However,it still remains a challenge owing to the t...Colorless‐to‐black switching has attracted widespread attention for smart windows and multifunctional displays because they are more useful to control solar energy.However,it still remains a challenge owing to the tremendous difficulties in the design of completely reverse absorptions in transmissive and colored states.Herein,we report on an electrochemical device that can switch between colorless and black by using the electrochemical process of hybrid organic–inorganic perovskite MAPbBr_(3),which shows a high integrated contrast ratio of up to 73%from 400 to 800 nm.The perovskite solution can be used as the active layer to assemble the device,showing superior transmittance over the entire visible region in neutral states.By applying an appropriate voltage,the device undergoes reversible switching between colorless and black,which is attributed to the formation of lead and Br_(2)in the redox reaction induced by the electron transfer process in MAPbBr_(3).In addition,the contrast ratio can be modulated over the entire visible region by changing the concentration and the applied voltage.These results contribute toward gaining an insightful understanding of the electrochemical process of perovskites and greatly promoting the development of switchable devices.展开更多
This paper presents an end-to-end deep learning method to solve geometry problems via feature learning and contrastive learning of multimodal data.A key challenge in solving geometry problems using deep learning is to...This paper presents an end-to-end deep learning method to solve geometry problems via feature learning and contrastive learning of multimodal data.A key challenge in solving geometry problems using deep learning is to automatically adapt to the task of understanding single-modal and multimodal problems.Existing methods either focus on single-modal ormultimodal problems,and they cannot fit each other.A general geometry problem solver shouldobviouslybe able toprocess variousmodalproblems at the same time.Inthispaper,a shared feature-learning model of multimodal data is adopted to learn the unified feature representation of text and image,which can solve the heterogeneity issue between multimodal geometry problems.A contrastive learning model of multimodal data enhances the semantic relevance betweenmultimodal features and maps them into a unified semantic space,which can effectively adapt to both single-modal and multimodal downstream tasks.Based on the feature extraction and fusion of multimodal data,a proposed geometry problem solver uses relation extraction,theorem reasoning,and problem solving to present solutions in a readable way.Experimental results show the effectiveness of the method.展开更多
Some reconstruction-based anomaly detection models in multivariate time series have brought impressive performance advancements but suffer from weak generalization ability and a lack of anomaly identification.These li...Some reconstruction-based anomaly detection models in multivariate time series have brought impressive performance advancements but suffer from weak generalization ability and a lack of anomaly identification.These limitations can result in the misjudgment of models,leading to a degradation in overall detection performance.This paper proposes a novel transformer-like anomaly detection model adopting a contrastive learning module and a memory block(CLME)to overcome the above limitations.The contrastive learning module tailored for time series data can learn the contextual relationships to generate temporal fine-grained representations.The memory block can record normal patterns of these representations through the utilization of attention-based addressing and reintegration mechanisms.These two modules together effectively alleviate the problem of generalization.Furthermore,this paper introduces a fusion anomaly detection strategy that comprehensively takes into account the residual and feature spaces.Such a strategy can enlarge the discrepancies between normal and abnormal data,which is more conducive to anomaly identification.The proposed CLME model not only efficiently enhances the generalization performance but also improves the ability of anomaly detection.To validate the efficacy of the proposed approach,extensive experiments are conducted on well-established benchmark datasets,including SWaT,PSM,WADI,and MSL.The results demonstrate outstanding performance,with F1 scores of 90.58%,94.83%,91.58%,and 91.75%,respectively.These findings affirm the superiority of the CLME model over existing stateof-the-art anomaly detection methodologies in terms of its ability to detect anomalies within complex datasets accurately.展开更多
Interference signals recognition plays an important role in anti-jamming communication.With the development of deep learning,many supervised interference signals recognition algorithms based on deep learning have emer...Interference signals recognition plays an important role in anti-jamming communication.With the development of deep learning,many supervised interference signals recognition algorithms based on deep learning have emerged recently and show better performance than traditional recognition algorithms.However,there is no unsupervised interference signals recognition algorithm at present.In this paper,an unsupervised interference signals recognition method called double phases and double dimensions contrastive clustering(DDCC)is proposed.Specifically,in the first phase,four data augmentation strategies for interference signals are used in data-augmentation-based(DA-based)contrastive learning.In the second phase,the original dataset’s k-nearest neighbor set(KNNset)is designed in double dimensions contrastive learning.In addition,a dynamic entropy parameter strategy is proposed.The simulation experiments of 9 types of interference signals show that random cropping is the best one of the four data augmentation strategies;the feature dimensional contrastive learning in the second phase can improve the clustering purity;the dynamic entropy parameter strategy can improve the stability of DDCC effectively.The unsupervised interference signals recognition results of DDCC and five other deep clustering algorithms show that the clustering performance of DDCC is superior to other algorithms.In particular,the clustering purity of our method is above 92%,SCAN’s is 81%,and the other three methods’are below 71%when jammingnoise-ratio(JNR)is−5 dB.In addition,our method is close to the supervised learning algorithm.展开更多
A method was proposed to analyze the influences of the non-ideal spectroscopic performance of optical components and orientation errors of a laser tracing measurement optical system on the tracing measurement performa...A method was proposed to analyze the influences of the non-ideal spectroscopic performance of optical components and orientation errors of a laser tracing measurement optical system on the tracing measurement performance.A comprehensive model of the interference fringe contrast based on the laser tracing system s measurement principle was established in this study.Simulation results based on ZEMAX verified the model.According to the simulation results,the placement angle of the analyzer had a direct influence on the interference fringe contrast.When the angle of the polarized light to the analyzer’s transmission axis increased from 65°to 85°,each contrast of the four-way interference fringes decreased from 0.9996 to 0.3528,the interference fringe contrast is decreased by 65%.Under the split ratio of beam splitters in the interference part(BS 1)of 5∶5,when the splitting ratio of BS 2 changed from 2∶8 to 8∶2,the fringe contrast of the interference signals received by the photodetectors increased,but the injection light intensity onto the PSD reflected by BS 2 decreased.The significant influence of the tracing performance was verified by the experiments.When splitting ratio of BS 2 increased,the contrast of the interference fringes increased.Due to the weakening of the incident light intensity of the PSD caused by the change of BS 2 splitting ratio,the response time of the tracing system is increased by 23.7 ms.As a result,the tracing performance of the laser tracing measurement optical system was degraded.An important theoretical basis was provided to evaluate and improve the accuracy and reliability of laser tracing measurement systems.展开更多
Multimodal sentiment analysis is an essential area of research in artificial intelligence that combines multiple modes,such as text and image,to accurately assess sentiment.However,conventional approaches that rely on...Multimodal sentiment analysis is an essential area of research in artificial intelligence that combines multiple modes,such as text and image,to accurately assess sentiment.However,conventional approaches that rely on unimodal pre-trained models for feature extraction from each modality often overlook the intrinsic connections of semantic information between modalities.This limitation is attributed to their training on unimodal data,and necessitates the use of complex fusion mechanisms for sentiment analysis.In this study,we present a novel approach that combines a vision-language pre-trained model with a proposed multimodal contrastive learning method.Our approach harnesses the power of transfer learning by utilizing a vision-language pre-trained model to extract both visual and textual representations in a unified framework.We employ a Transformer architecture to integrate these representations,thereby enabling the capture of rich semantic infor-mation in image-text pairs.To further enhance the representation learning of these pairs,we introduce our proposed multimodal contrastive learning method,which leads to improved performance in sentiment analysis tasks.Our approach is evaluated through extensive experiments on two publicly accessible datasets,where we demonstrate its effectiveness.We achieve a significant improvement in sentiment analysis accuracy,indicating the supe-riority of our approach over existing techniques.These results highlight the potential of multimodal sentiment analysis and underscore the importance of considering the intrinsic semantic connections between modalities for accurate sentiment assessment.展开更多
Objective:To evaluate the impact of hyperuricemia on the occurrence of contrast agentrelated acute kidney injury after percutaneous coronary intervention.Methods:Retrieve PubMed,Embase,Cochrane Library,Web of Science,...Objective:To evaluate the impact of hyperuricemia on the occurrence of contrast agentrelated acute kidney injury after percutaneous coronary intervention.Methods:Retrieve PubMed,Embase,Cochrane Library,Web of Science,CNKI,Wanfang,and VIP databases,and publish articles on the correlation between hyperuricemia and contrast agent-related acute kidney damage after percutaneous coronary intervention from the establishment of the database to August 162023.Two researchers independently conducted literature screening and data extraction to evaluate the bias risk of inclusion in the study,and conducted metaanalysis using Review Manager 5.4 software.Results:A total of 12 articles were included,including 11676 patients.The meta-analysis results showed that compared with patients without hyperuricemia,patients with hyperuricemia had a higher risk of developing PC-AKI,with an incidence rate of 22.3%.Hyperuricemia was a risk factor for the occurrence of PCAKI(OR=2.03,95%CI:1.58-2.61);Patients with hyperuricemia have a higher risk of death after PC-AKI,with a mortality rate of 7.5%.Hyperuricemia is a risk factor for early death in PC-AKI patients(OR=2.33,95%CI:1.81-3.00);The probability of CRRT treatment after PCAKI in patients with hyperuricemia is higher,at 3.14%.Hyperuricemia is an influencing factor for CRRT treatment in PC-AKI patients(OR=7,95%CI:2.83-17.30).Conclusion:Existing research evidence suggests that the presence of hyperuricemia is an independent risk factor for the occurrence of PC-AKI,and it significantly increases the hospital mortality rate and the risk of renal replacement therapy in PC-AKI patients.展开更多
基金Supported by Scientific Research Fund of the Wenzhou Science and Technology Division,No.Y2020798 and No.Y2020805.
文摘BACKGROUND The detection rate of peptic ulcer in children is improving,with development of diagnostic procedures.Gastroscopy is the gold standard for the diagnosis of peptic ulcer,but it is an invasive procedure.Gastrointestinal contrast-enhanced ultrasonography(CEUS)has the advantages of being painless,noninvasive,nonradioactive,easy to use,and safe.AIM To investigate the clinical value of CEUS for diagnosis and treatment of peptic ulcer in children.METHODS We investigated 43 children with digestive tract symptoms in our hospital from January 2021 to June 2022.All children were examined by routine ultrasound,gastrointestinal CEUS,and gastroscopy.The pathological results of gastroscopy were taken as the gold standard.Routine ultrasonography was performed before gastrointestinal CEUS.Conventional ultrasound showed the thickness of the gastroduodenal wall,gastric peristalsis,and the adjacent organs and tissues around the abdominal cavity.Gastrointestinal CEUS recorded the thickness of the gastroduodenal wall;the size,location and shape of the ulcer;gastric peristalsis;and adjacent organs and tissues around the abdominal cavity.The results of routine ultrasound and gastrointestinal ultrasound were compared with those of gastroscopy to evaluate the diagnostic results and coincidence rate of routine ultrasound and gastrointestinal CEUS.All children received informed consent from their guardians for CEUS.This study was reviewed and approved by the hospital medical ethics committee.RESULTS Among the 43 children,17(15 male,2 female)were diagnosed with peptic ulcer by gastroscopy.There were 26 children with nonpeptic ulcer.There were eight cases of peptic ulcer and 35 of nonpeptic ulcer diagnosed by conventional ultrasound.The diagnostic coincidence rate of peptic ulcer in children diagnosed by conventional ultrasound was 79.1%(34/43),which was significantly different from that of gastroscopy(P=0.033).It indicates that the coincidence rate of gastrointestinal contrast-enhanced ultrasound and gastroscope is low.Fifteen cases of peptic ulcer and 28 of nonpeptic ulcer were diagnosed by CEUS.The diagnostic coincidence rate of peptic ulcer in children was 95.3%(41/43).There was no significant difference between CEUS and gastroscopy(P=0.655).It indicates that the coincidence rate of gastrointestinal contrast-enhanced ultrasound and gastroscope is high.CONCLUSION Gastrointestinal CEUS has a high coincidence rate in the diagnosis of peptic ulcer in children,and can be used as a preliminary examination method.
基金supported by the National Natural Science Foundation of China (Grant No. 41888101)。
文摘One of the basic characteristics of Earth's modern climate is that the Northern Hemisphere(NH) is climatologically warmer than the Southern Hemisphere(SH). Here, model performances of this basic state are examined using simulation results from 26 CMIP6 models. Results show that the CMIP6 models underestimate the contrast in interhemispheric surface temperatures on average(0.8 K for CMIP6 mean versus 1.4 K for reanalysis data mean), and that there is a large intermodel spread, ranging from -0.7 K to 2.3 K. A box model energy budget analysis shows that the contrast in interhemispheric shortwave absorption at the top of the atmosphere, the contrast in interhemispheric greenhouse trapping, and the crossequatorial northward ocean heat transport, are all underestimated in the multimodel mean. By examining the intermodel spread, we find intermodel biases can be tracked back to biases in midlatitude shortwave cloud forcing in AGCMs. Models with a weaker interhemispheric temperature contrast underestimate the shortwave cloud reflection in the SH but overestimate the shortwave cloud reflection in the NH, which are respectively due to underestimation of the cloud fraction over the SH extratropical ocean and overestimation of the cloud liquid water content over the NH extratropical continents.Models that underestimate the interhemispheric temperature contrast exhibit larger double ITCZ biases, characterized by excessive precipitation in the SH tropics. Although this intermodel spread does not account for the multimodel ensemble mean biases, it highlights that improving cloud simulation in AGCMs is essential for simulating the climate realistically in coupled models.
基金supported by the National Key Research and Development Program of China(Nos.2019YFE03090100,2022YFE03100002)National Natural Science Foundation of China(No.12075241)。
文摘The optical design of near-infrared phase contrast imaging(NI-PCI)diagnosis on HL-2A is introduced in this paper.This scheme benefits from the great progress of near-infrared laser technology and is a broadening of traditional phase contrast technology.This diagnostic can work as a keen tool to measure plasma wavenumber spectra by inferring string-integrated plasma density fluctuations.Design of both the front optical path which is the path before the laser transmitting into the tokamak plasma and the rear optics which is the path after the laser passing through the plasma is detailed.The 1550 nm laser is chosen as the probe beam and highprecision optical components are designed to fit the laser beam,in which a phase plate with a 194-nm-deep silver groove is the key.Compared with the conventional 10.6μm laser-based PCI system on HL-2A,NI-PCI significantly overcomes the unwanted phase scintillation effect and promotes the measurement capability of high-wavenumber turbulence with an increased maximal measurable wavenumber from 15 cm^(-1)to 32.6 cm^(-1).
基金supported by the Natural Science Foundation of Ningxia Province(No.2023AAC03316)the Ningxia Hui Autonomous Region Education Department Higher Edu-cation Key Scientific Research Project(No.NYG2022051)the North Minzu University Graduate Innovation Project(YCX23146).
文摘Knowledge graph can assist in improving recommendation performance and is widely applied in various person-alized recommendation domains.However,existing knowledge-aware recommendation methods face challenges such as weak user-item interaction supervisory signals and noise in the knowledge graph.To tackle these issues,this paper proposes a neighbor information contrast-enhanced recommendation method by adding subtle noise to construct contrast views and employing contrastive learning to strengthen supervisory signals and reduce knowledge noise.Specifically,first,this paper adopts heterogeneous propagation and knowledge-aware attention networks to obtain multi-order neighbor embedding of users and items,mining the high-order neighbor informa-tion of users and items.Next,in the neighbor information,this paper introduces weak noise following a uniform distribution to construct neighbor contrast views,effectively reducing the time overhead of view construction.This paper then performs contrastive learning between neighbor views to promote the uniformity of view information,adjusting the neighbor structure,and achieving the goal of reducing the knowledge noise in the knowledge graph.Finally,this paper introduces multi-task learning to mitigate the problem of weak supervisory signals.To validate the effectiveness of our method,experiments are conducted on theMovieLens-1M,MovieLens-20M,Book-Crossing,and Last-FM datasets.The results showthat compared to the best baselines,our method shows significant improvements in AUC and F1.
基金supported by the National Natural Science Foundation of China (No.U1833203),the National Natural Science Foundation of China (No.62301036)the Aviation Science Foundation (No.2020Z019055001)China Postdoctoral Science Foundation Funded Project (No.2022M720446)。
文摘In order to address the problem of high false alarm rate and low probabilities of infrared small target detection in complex low-altitude background,an infrared small target detection method based on improved weighted local contrast is proposed in this paper.First,the ratio information between the target and local background is utilized as an enhancement factor.The local contrast is calculated by incorporating the heterogeneity between the target and local background.Then,a local product weighted method is designed based on the spatial dissimilarity between target and background to further enhance target while suppressing background.Finally,the location of target is obtained by adaptive threshold segmentation.As experimental results demonstrate,the method shows superior performance in several evaluation metrics compared with six existing algorithms on different datasets containing targets such as unmanned aerial vehicles(UAV).
文摘This paper discusses the inheritance and application of Chinese character reverse contrast typeface style.It begins by analyzing the visual features of Western reverse contrast typeface styles,with a focus on Caslon Italian and French Clarendon,providing a Western perspective reference for subsequent Chinese character reverse contrast typeface style designs.The paper then traces the origins of the Chinese reverse contrast style,from the calligraphy style"Lacquer Script"to the earliest printing type"フワンテール形",exploring the historical background and cultural significance of the Chinese reverse contrast style.In the methodology section of Chinese character reverse contrast typeface style design,the discussion is conducted from two dimensions:inheritance and application.In terms of inheritance,through an in-depth analysis of"Lacquer Script"and"フワンテール形"typeface style,the paper summarizes three basic theories for modern Chinese character reverse contrast typeface style design.In the application section,it examines in detail the two most influential recent typeface styles,"Ribaasu"and"Basic Artistic",outlining three directions of application:extreme horizontal stroke variations,exaggerated contrast,and diverse decorative strokes,showcasing new directions and possibilities for Chinese character reverse contrast typeface style design.This paper not only reviews the developmental history of the Chinese character reverse contrast typeface style but also analyzes the design methodology of Chinese character reverse contrast typeface style through specific case studies.
文摘Introduction: Near-infrared fluorescence imaging is a technique that will establish itself in the short term at the international level because it is recognized for its potential to improve the performance of surgical interventions, its moderate investment and operating costs and its portability. Although the technology is now mature, there is currently the problem of the availability of contrast agents to be injected IV. The aim of this methodology article is to propose an alternative solution to the need for contrast agents for clinical research, particularly in oncology. Methodology: They consist of coupling a fluorescent marker in the form of an NHS derivative, such as IR DYE manufactured in compliance with GMP, with therapeutic monoclonal antibodies having marketing authorization for molecular imaging. For a given antibody, the marking procedure must be the subject of a validation file on the final preparation filtered on a sterilizing membrane at 0.22 μm. Once the procedure has been validated, it would be unnecessary to repeat the tests before each clinical research examination. A check of the marking by thin-layer chromatography (TLC) and place it in a sample bank at +4˚C for 1 month of each injected formulation would be sufficient for additional tests if necessary. Conclusion: Molecular near-infrared fluorescence imaging is experiencing development, the process of which could be accelerated by greater availability of clinical contrast agents. Alternative solutions are therefore necessary to promote clinical research in this area. These methods must be shared to make it easier for researchers.
基金Supported by the Foundation of the Longhua District Health System Project Cooperation 2022(basic and clinical research in ischemic stroke).
文摘BACKGROUND Ischemic stroke is an entity with high incidence,morbidity,and mortality rates.Carotid artery stenosis is an important and independent risk factor for ischemic stroke.The three current approaches for treating carotid artery stenosis are drug treatment,carotid endarterectomy(CEA),carotid angioplasty and stenting(CAS).The approach is chosen based on the degree of stenosis.CEA or CAS could have been chosen for the current patient,who had severe carotid stenosis and an iodinated contrast allergy.After thoroughly communicating with the patient,the patient chose CAS for treatment.Therefore,we performed ultrasound-guided CAS to avoid the use of iodinated contrast.CASE SUMMARY The main symptoms of the patient were numbness and weakness of the left limb.Computed tomography angiography of the head and neck at another hospital indicated multiple sites of stenosis in the arteries of the head and neck.The patient requested CAS for treatment but was allergic to iodinated contrast media.Thus,routine digital subtraction angiography(DSA)with iodinated contrast could not be used for the procedure.The diagnosis of this patient was as follows:(1)Right parietal lobe cerebral infarction;(2)multiple sites of stenosis in the arteries of the head and neck(severe stenosis of the right internal carotid artery,severe stenosis of the right subclavian artery);(3)right subclavian steal syndrome;and(4)hypertension(stage 3,high risk).The interventions included routine treatment for cerebral infarction,oral administration of clopidogrel(75 mg qd)and aspirin(100 mg qd),ultrasound-guided CAS,and postoperative follow-up.Postoperative color Doppler ultrasound and cerebrovascular magnetic resonance angiography of the carotid artery showed good vascular recovery,and the postoperative follow-up indicated a good prognosis.CONCLUSION This case study suggests that ultrasound-guided endovascular treatment is a potential option for patients with contraindications to the iodinated contrast agents used in DSA-guided surgery,although excellent surgical operating skills are needed.
文摘BACKGROUND Contrast-induced encephalopathy(CIE)is a rare transient,reversible abnormality in the structure or function of the nervous system caused by the intravascular use of contrast agents.CIE can present with a range of neurological manifestations,including focal neurological deficits(hemiplegia,hemianopia,cortical blindness,aphasia,and parkinsonism)and systemic symptoms(confusion,seizures,and coma).However,if not accurately diagnosed and treated in a timely manner,CIE can cause irreversible damage to patients,especially critically ill patients.CASE SUMMARY A male in his 50 s,2 h after digital subtraction angiography,had a progressive disorder of consciousness,mixed aphasia,bilateral pupillary sluggish light reflex,and right limb weakness.Seven hours after the procedure,he developed unconsciousness,high fever(39.5°C),seizures,hemiplegia,neck stiffness(+),and right Babinski signs(+).computed tomography(CT)findings 2 h postprocedure were very confusing and led us to misdiagnose the patient with subarachnoid hemorrhage.Brain CT was performed again 7 h after the procedure.Compared with the CT 2 h after the procedure,the CT 7 h after the procedure showed that the manifestations of subarachnoid hemorrhage in the left cerebral hemisphere had disappeared and were replaced by brain tissue swelling,and the cerebral sulci had disappeared.Combined with the clinical manifestations of the patient and after the exclusion of subarachnoid hemorrhage and cerebrovascular embolism,we diagnosed the patient with CIE,and intravenous fluids were given for adequate hydration,as well as mannitol,albumin dehydration,furosemide and the glucocorticoid methylprednisolone.After 17 d of active treatment,the patient was discharged with no sequelae.CONCLUSION CIE should be taken seriously,but it is easily misdiagnosed,and once CIE is diagnosed,rapid,accurate diagnosis and treatment are critical steps.Whether a follow-up examination using a contrast agent can be performed should be closely evaluated,and the patient should be fully informed of the associated risks.
基金supported by the National Natural Science Foundation of China(61971165)the Key Research and Development Program of Hubei Province(2020BAB113)。
文摘Previous deep learning-based super-resolution(SR)methods rely on the assumption that the degradation process is predefined(e.g.,bicubic downsampling).Thus,their performance would suffer from deterioration if the real degradation is not consistent with the assumption.To deal with real-world scenarios,existing blind SR methods are committed to estimating both the degradation and the super-resolved image with an extra loss or iterative scheme.However,degradation estimation that requires more computation would result in limited SR performance due to the accumulated estimation errors.In this paper,we propose a contrastive regularization built upon contrastive learning to exploit both the information of blurry images and clear images as negative and positive samples,respectively.Contrastive regularization ensures that the restored image is pulled closer to the clear image and pushed far away from the blurry image in the representation space.Furthermore,instead of estimating the degradation,we extract global statistical prior information to capture the character of the distortion.Considering the coupling between the degradation and the low-resolution image,we embed the global prior into the distortion-specific SR network to make our method adaptive to the changes of distortions.We term our distortion-specific network with contrastive regularization as CRDNet.The extensive experiments on synthetic and realworld scenes demonstrate that our lightweight CRDNet surpasses state-of-the-art blind super-resolution approaches.
基金the National Natural Science Foundation of China(U20B2045).
文摘System logs are essential for detecting anomalies,querying faults,and tracing attacks.Because of the time-consuming and labor-intensive nature of manual system troubleshooting and anomaly detection,it cannot meet the actual needs.The implementation of automated log anomaly detection is a topic that demands urgent research.However,the prior work on processing log data is mainly one-dimensional and cannot profoundly learn the complex associations in log data.Meanwhile,there is a lack of attention to the utilization of log labels and usually relies on a large number of labels for detection.This paper proposes a novel and practical detection model named LCC-HGLog,the core of which is the conversion of log anomaly detection into a graph classification problem.Semantic temporal graphs(STG)are constructed by extracting the raw logs’execution sequences and template semantics.Then a unique graph classifier is used to better comprehend each STG’s semantic,sequential,and structural features.The classification model is trained jointly by graph classification loss and label contrastive loss.While achieving discriminability at the class-level,it increases the fine-grained identification at the instance-level,thus achieving detection performance even with a small amount of labeled data.We have conducted numerous experiments on real log datasets,showing that the proposed model outperforms the baseline methods and obtains the best all-around performance.Moreover,the detection performance degrades to less than 1%when only 10%of the labeled data is used.With 200 labeled samples,we can achieve the same or better detection results than the baseline methods.
基金Natural Science Foundation of Hebei Province(China),Grant/Award Numbers:B2020203013,B2021203016Science and Technology Project of Hebei Education Department(China),Grant/Award Number:QN2020137+3 种基金Cultivation Project for Basic Research Innovation of Yanshan University(China),Grant/Award Number:2021LGZD015Subsidy for Hebei Key Laboratory of Applied Chemistry after Operation Performance(China),Grant/Award Number:22567616HNatural Science Foundation of Heilongjiang Province(China),Grant/Award Number:LH2022B025Fundamental Research Funds for the Provincial Universities of Heilongjiang Province(China),Grant/Award Number:KYYWF10236190104。
文摘Colorless‐to‐black switching has attracted widespread attention for smart windows and multifunctional displays because they are more useful to control solar energy.However,it still remains a challenge owing to the tremendous difficulties in the design of completely reverse absorptions in transmissive and colored states.Herein,we report on an electrochemical device that can switch between colorless and black by using the electrochemical process of hybrid organic–inorganic perovskite MAPbBr_(3),which shows a high integrated contrast ratio of up to 73%from 400 to 800 nm.The perovskite solution can be used as the active layer to assemble the device,showing superior transmittance over the entire visible region in neutral states.By applying an appropriate voltage,the device undergoes reversible switching between colorless and black,which is attributed to the formation of lead and Br_(2)in the redox reaction induced by the electron transfer process in MAPbBr_(3).In addition,the contrast ratio can be modulated over the entire visible region by changing the concentration and the applied voltage.These results contribute toward gaining an insightful understanding of the electrochemical process of perovskites and greatly promoting the development of switchable devices.
基金supported by the NationalNatural Science Foundation of China (No.62107014,Jian P.,62177025,He B.)the Key R&D and Promotion Projects of Henan Province (No.212102210147,Jian P.)Innovative Education Program for Graduate Students at North China University of Water Resources and Electric Power,China (No.YK-2021-99,Guo F.).
文摘This paper presents an end-to-end deep learning method to solve geometry problems via feature learning and contrastive learning of multimodal data.A key challenge in solving geometry problems using deep learning is to automatically adapt to the task of understanding single-modal and multimodal problems.Existing methods either focus on single-modal ormultimodal problems,and they cannot fit each other.A general geometry problem solver shouldobviouslybe able toprocess variousmodalproblems at the same time.Inthispaper,a shared feature-learning model of multimodal data is adopted to learn the unified feature representation of text and image,which can solve the heterogeneity issue between multimodal geometry problems.A contrastive learning model of multimodal data enhances the semantic relevance betweenmultimodal features and maps them into a unified semantic space,which can effectively adapt to both single-modal and multimodal downstream tasks.Based on the feature extraction and fusion of multimodal data,a proposed geometry problem solver uses relation extraction,theorem reasoning,and problem solving to present solutions in a readable way.Experimental results show the effectiveness of the method.
基金support from the Major National Science and Technology Special Projects(2016ZX02301003-004-007)the Natural Science Foundation of Hebei Province(F2020202067)。
文摘Some reconstruction-based anomaly detection models in multivariate time series have brought impressive performance advancements but suffer from weak generalization ability and a lack of anomaly identification.These limitations can result in the misjudgment of models,leading to a degradation in overall detection performance.This paper proposes a novel transformer-like anomaly detection model adopting a contrastive learning module and a memory block(CLME)to overcome the above limitations.The contrastive learning module tailored for time series data can learn the contextual relationships to generate temporal fine-grained representations.The memory block can record normal patterns of these representations through the utilization of attention-based addressing and reintegration mechanisms.These two modules together effectively alleviate the problem of generalization.Furthermore,this paper introduces a fusion anomaly detection strategy that comprehensively takes into account the residual and feature spaces.Such a strategy can enlarge the discrepancies between normal and abnormal data,which is more conducive to anomaly identification.The proposed CLME model not only efficiently enhances the generalization performance but also improves the ability of anomaly detection.To validate the efficacy of the proposed approach,extensive experiments are conducted on well-established benchmark datasets,including SWaT,PSM,WADI,and MSL.The results demonstrate outstanding performance,with F1 scores of 90.58%,94.83%,91.58%,and 91.75%,respectively.These findings affirm the superiority of the CLME model over existing stateof-the-art anomaly detection methodologies in terms of its ability to detect anomalies within complex datasets accurately.
基金This research was supported by the National Natural Science Foundation of China under Grant No.U19B2016.,and Zhejiang Provincial Key Lab of Data Storage and Transmission Technology,Hangzhou Dianzi University.
文摘Interference signals recognition plays an important role in anti-jamming communication.With the development of deep learning,many supervised interference signals recognition algorithms based on deep learning have emerged recently and show better performance than traditional recognition algorithms.However,there is no unsupervised interference signals recognition algorithm at present.In this paper,an unsupervised interference signals recognition method called double phases and double dimensions contrastive clustering(DDCC)is proposed.Specifically,in the first phase,four data augmentation strategies for interference signals are used in data-augmentation-based(DA-based)contrastive learning.In the second phase,the original dataset’s k-nearest neighbor set(KNNset)is designed in double dimensions contrastive learning.In addition,a dynamic entropy parameter strategy is proposed.The simulation experiments of 9 types of interference signals show that random cropping is the best one of the four data augmentation strategies;the feature dimensional contrastive learning in the second phase can improve the clustering purity;the dynamic entropy parameter strategy can improve the stability of DDCC effectively.The unsupervised interference signals recognition results of DDCC and five other deep clustering algorithms show that the clustering performance of DDCC is superior to other algorithms.In particular,the clustering purity of our method is above 92%,SCAN’s is 81%,and the other three methods’are below 71%when jammingnoise-ratio(JNR)is−5 dB.In addition,our method is close to the supervised learning algorithm.
基金Sponsored by the National Natural Science Foundation of China(Grant No.52175491).
文摘A method was proposed to analyze the influences of the non-ideal spectroscopic performance of optical components and orientation errors of a laser tracing measurement optical system on the tracing measurement performance.A comprehensive model of the interference fringe contrast based on the laser tracing system s measurement principle was established in this study.Simulation results based on ZEMAX verified the model.According to the simulation results,the placement angle of the analyzer had a direct influence on the interference fringe contrast.When the angle of the polarized light to the analyzer’s transmission axis increased from 65°to 85°,each contrast of the four-way interference fringes decreased from 0.9996 to 0.3528,the interference fringe contrast is decreased by 65%.Under the split ratio of beam splitters in the interference part(BS 1)of 5∶5,when the splitting ratio of BS 2 changed from 2∶8 to 8∶2,the fringe contrast of the interference signals received by the photodetectors increased,but the injection light intensity onto the PSD reflected by BS 2 decreased.The significant influence of the tracing performance was verified by the experiments.When splitting ratio of BS 2 increased,the contrast of the interference fringes increased.Due to the weakening of the incident light intensity of the PSD caused by the change of BS 2 splitting ratio,the response time of the tracing system is increased by 23.7 ms.As a result,the tracing performance of the laser tracing measurement optical system was degraded.An important theoretical basis was provided to evaluate and improve the accuracy and reliability of laser tracing measurement systems.
基金supported by Science and Technology Research Project of Jiangxi Education Department.Project Grant No.GJJ2203306.
文摘Multimodal sentiment analysis is an essential area of research in artificial intelligence that combines multiple modes,such as text and image,to accurately assess sentiment.However,conventional approaches that rely on unimodal pre-trained models for feature extraction from each modality often overlook the intrinsic connections of semantic information between modalities.This limitation is attributed to their training on unimodal data,and necessitates the use of complex fusion mechanisms for sentiment analysis.In this study,we present a novel approach that combines a vision-language pre-trained model with a proposed multimodal contrastive learning method.Our approach harnesses the power of transfer learning by utilizing a vision-language pre-trained model to extract both visual and textual representations in a unified framework.We employ a Transformer architecture to integrate these representations,thereby enabling the capture of rich semantic infor-mation in image-text pairs.To further enhance the representation learning of these pairs,we introduce our proposed multimodal contrastive learning method,which leads to improved performance in sentiment analysis tasks.Our approach is evaluated through extensive experiments on two publicly accessible datasets,where we demonstrate its effectiveness.We achieve a significant improvement in sentiment analysis accuracy,indicating the supe-riority of our approach over existing techniques.These results highlight the potential of multimodal sentiment analysis and underscore the importance of considering the intrinsic semantic connections between modalities for accurate sentiment assessment.
基金Key Research and Development Program of the Ministry of Science and Technology(No.2018YFC1704304)。
文摘Objective:To evaluate the impact of hyperuricemia on the occurrence of contrast agentrelated acute kidney injury after percutaneous coronary intervention.Methods:Retrieve PubMed,Embase,Cochrane Library,Web of Science,CNKI,Wanfang,and VIP databases,and publish articles on the correlation between hyperuricemia and contrast agent-related acute kidney damage after percutaneous coronary intervention from the establishment of the database to August 162023.Two researchers independently conducted literature screening and data extraction to evaluate the bias risk of inclusion in the study,and conducted metaanalysis using Review Manager 5.4 software.Results:A total of 12 articles were included,including 11676 patients.The meta-analysis results showed that compared with patients without hyperuricemia,patients with hyperuricemia had a higher risk of developing PC-AKI,with an incidence rate of 22.3%.Hyperuricemia was a risk factor for the occurrence of PCAKI(OR=2.03,95%CI:1.58-2.61);Patients with hyperuricemia have a higher risk of death after PC-AKI,with a mortality rate of 7.5%.Hyperuricemia is a risk factor for early death in PC-AKI patients(OR=2.33,95%CI:1.81-3.00);The probability of CRRT treatment after PCAKI in patients with hyperuricemia is higher,at 3.14%.Hyperuricemia is an influencing factor for CRRT treatment in PC-AKI patients(OR=7,95%CI:2.83-17.30).Conclusion:Existing research evidence suggests that the presence of hyperuricemia is an independent risk factor for the occurrence of PC-AKI,and it significantly increases the hospital mortality rate and the risk of renal replacement therapy in PC-AKI patients.
