Based on the latest classification by the International Society for the Study of Vascular Anomalies in 2018,vascular malformations(VMs)can be categorized into simple,combined,VMs of major named vessels,and VMs associa...Based on the latest classification by the International Society for the Study of Vascular Anomalies in 2018,vascular malformations(VMs)can be categorized into simple,combined,VMs of major named vessels,and VMs associated with other anomalies.Simple VMs include lymphatic,venous,capillary,and arteriovenous malformations(AVMs).AVMs represent disorders of direct arteriovenous shunts caused by the absence of a capillary bed between the involved arteries and veins.This abnormal vascular communication causes arterial blood to accumulate in the venous vessels,thus resulting in venous hypertension and characteristic clinical manifestations,such as pulsation,tremors,and elevated temperature.AVMs can occur sporadically or as manifestations of syndromic lesions and are considered among the most complex and challenging VMs.The diagnosis and treatment of AVMs can vary depending on the lesion location and associated clinical symptoms,thus complicating their management.Herein,we discuss peripheral AVMs in terms of their clinical manifestations,imaging examinations,and staging systems to provide a comprehensive reference for the treatment,evaluation methods,and follow-up procedures for this vascular anomaly.展开更多
BACKGROUND Accessory and cavitated uterine mass(ACUM)is an uncommon form of connate Müllerian anomaly seen in young and nulliparous women,which presents as chronic periodic pelvic pain and severe dysmenorrhea.The...BACKGROUND Accessory and cavitated uterine mass(ACUM)is an uncommon form of connate Müllerian anomaly seen in young and nulliparous women,which presents as chronic periodic pelvic pain and severe dysmenorrhea.The entity is often underdiagnosed due to a broad differential diagnosis,including rudimentary uterine horn,true cavitated adenomyosis and degenerating fibroids.CASE SUMMARY A 22-year-old woman who presented with severe dysmenorrhea and was initially misdiagnosed with cystic adenomyosis.Gynecological examination and ultrasonography were performed.The patient underwent laparoscopic excision of the mass and histopathological examination confirmed the diagnosis.Postoperatively,the patient did well,with no further dysmenorrhea.CONCLUSION ACUM is difficult to diagnose.A correct diagnosis can be made only after excision and histopathological evaluation.Surgical excision is necessary and can be carried out by laparoscopy.展开更多
Existing power anomaly detection is mainly based on a pattern matching algorithm.However,this method requires a lot of manual work,is time-consuming,and cannot detect unknown anomalies.Moreover,a large amount of label...Existing power anomaly detection is mainly based on a pattern matching algorithm.However,this method requires a lot of manual work,is time-consuming,and cannot detect unknown anomalies.Moreover,a large amount of labeled anomaly data is required in machine learning-based anomaly detection.Therefore,this paper proposes the application of a generative adversarial network(GAN)to massive data stream anomaly identification,diagnosis,and prediction in power dispatching automation systems.Firstly,to address the problem of the small amount of anomaly data,a GAN is used to obtain reliable labeled datasets for fault diagnosis model training based on a few labeled data points.Then,a two-step detection process is designed for the characteristics of grid anomalies,where the generated samples are first input to the XGBoost recognition system to identify the large class of anomalies in the first step.Thereafter,the data processed in the first step are input to the joint model of Convolutional Neural Networks(CNN)and Long short-term memory(LSTM)for fine-grained analysis to detect the small class of anomalies in the second step.Extensive experiments show that our work can reduce a lot of manual work and outperform the state-of-art anomalies classification algorithms for power dispatching data network.展开更多
Background The abnormalities of coronary arteries, though rare and sometimes benign, may first present clinically as myocardial infarction or sudden death. Multi-detector computed tomography （MDCT） is a non-invasive...Background The abnormalities of coronary arteries, though rare and sometimes benign, may first present clinically as myocardial infarction or sudden death. Multi-detector computed tomography （MDCT） is a non-invasive test that is highly suitable for detecting these anomalies. The study aimed to review the 64-MDCT appearance of the coronary artery anomalies in 66 patients and to discuss the clinical importance of these anomalies.Methods In 6014 consecutive patients examined over 12 months by 64-MDCT for the study of coronary artery disease, 66 were diagnosed for coronary artery anomalies. All patients were symptomatic for one or more of the following diseases： chest pain, dyspnoea, palpitations, arrhythmia and myocardial infarction. Nine patients had undergone a coronary angiography. All the CT images were evaluated by two radiologists and one cardiologist. The right coronary artery （RCA） and the conus branch arising separately, myocardial bridging and duplication of arteries were not analysed in our study.Results The incidence of coronary artery anomalies found in our study group was 1.097%. In the selected patients, seven different types of coronary anomalies were found by 64-MDCT examination. The high takeoff, origin of the coronary artery from the opposite or noncoronary sinus with an anomalous course, and coronary artery fistula were the three common forms of anomalies （n=16, 18 and 16, respectively）. Compared with the results of the coronary angiography, the number of the drainage sites of two coronary artery fistula was less in MDCT images （3 small sites in total）. In all cases, coronary artery computed tomography angiography （CTA） technique was able to recognize the origin of the coronary artery, its three-dimensional course and its spatial relationship with the adjacent structures. Conventional coronary angiography in two cases, however, was unable to provide sufficient information for correct and complete diagnosis.Conclusions In conclusion, the study showed that 64-MDCT, especially the volume rendering technique （VRT）, may be useful for the assessment of complex variations, even if the conventional angiography may not be sufficient. It may be considered as the first-choice imaging modality when an anomalous coronary artery is suspected.展开更多
To guarantee a reliable power supply,the expected operation of all the components in the power system is critical.Distance protection system is primarily responsible of isolating the faulty section from the healthy pa...To guarantee a reliable power supply,the expected operation of all the components in the power system is critical.Distance protection system is primarily responsible of isolating the faulty section from the healthy part for the grid.Failure in protection devices can result in multiple conflicting alarms at the power grid operation center and complex event analysis to manually find the root cause of the observed system state.If not handled in time,it may lead to the propagation of the faults/failures to the adjacent transmission lines and components.With availability of the synchronized measurements from phasor measurement units(PMUs),real-time system monitoring and automated failure diagnosis are feasible.With multiple adverse events and possible data anomalies,the complexity of the problem will be escalated.In this paper,a PMUbased algorithm is presented and discussed to detect the root cause of the failure in transmission protection system based on the observed state,e.g.multiple line tripping andbreaker failures.The failure diagnosis algorithm is further enhanced to come up with the fully functional version of the failure diagnosis tool,which is tailored for the cases in which the PMU anomalies are present.In the developed algorithm,the validity of the PMU data is critical.However,such causes as communication errors or cyber-attacks might lead to the PMU data anomalies.This issue is welladdressed in this paper and some major types of anomaly detection methods suitable for PMU data are discussed.Results show that the ensemble approach has some distinct advantages in data anomaly detection compared to the previously used standalone algorithms.Additionally,the enhanced failure diagnosis method is developed to clean the inaccurate data in case of the anomaly in measured voltage magnitudes.Finally,both original and enhanced versions of the tool are tested on 96-bus test system using the real-time OPAL-RT simulator.The results show the accuracy of the enhanced tool and its advantages over the primary version of the tool.展开更多
基金supported by the Transverse Research Project of Shanghai Ninth People’s Hospital(No.JYHX2022007)
文摘Based on the latest classification by the International Society for the Study of Vascular Anomalies in 2018,vascular malformations(VMs)can be categorized into simple,combined,VMs of major named vessels,and VMs associated with other anomalies.Simple VMs include lymphatic,venous,capillary,and arteriovenous malformations(AVMs).AVMs represent disorders of direct arteriovenous shunts caused by the absence of a capillary bed between the involved arteries and veins.This abnormal vascular communication causes arterial blood to accumulate in the venous vessels,thus resulting in venous hypertension and characteristic clinical manifestations,such as pulsation,tremors,and elevated temperature.AVMs can occur sporadically or as manifestations of syndromic lesions and are considered among the most complex and challenging VMs.The diagnosis and treatment of AVMs can vary depending on the lesion location and associated clinical symptoms,thus complicating their management.Herein,we discuss peripheral AVMs in terms of their clinical manifestations,imaging examinations,and staging systems to provide a comprehensive reference for the treatment,evaluation methods,and follow-up procedures for this vascular anomaly.
文摘BACKGROUND Accessory and cavitated uterine mass(ACUM)is an uncommon form of connate Müllerian anomaly seen in young and nulliparous women,which presents as chronic periodic pelvic pain and severe dysmenorrhea.The entity is often underdiagnosed due to a broad differential diagnosis,including rudimentary uterine horn,true cavitated adenomyosis and degenerating fibroids.CASE SUMMARY A 22-year-old woman who presented with severe dysmenorrhea and was initially misdiagnosed with cystic adenomyosis.Gynecological examination and ultrasonography were performed.The patient underwent laparoscopic excision of the mass and histopathological examination confirmed the diagnosis.Postoperatively,the patient did well,with no further dysmenorrhea.CONCLUSION ACUM is difficult to diagnose.A correct diagnosis can be made only after excision and histopathological evaluation.Surgical excision is necessary and can be carried out by laparoscopy.
基金supported by the Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.,China,under Grant J2021167.
文摘Existing power anomaly detection is mainly based on a pattern matching algorithm.However,this method requires a lot of manual work,is time-consuming,and cannot detect unknown anomalies.Moreover,a large amount of labeled anomaly data is required in machine learning-based anomaly detection.Therefore,this paper proposes the application of a generative adversarial network(GAN)to massive data stream anomaly identification,diagnosis,and prediction in power dispatching automation systems.Firstly,to address the problem of the small amount of anomaly data,a GAN is used to obtain reliable labeled datasets for fault diagnosis model training based on a few labeled data points.Then,a two-step detection process is designed for the characteristics of grid anomalies,where the generated samples are first input to the XGBoost recognition system to identify the large class of anomalies in the first step.Thereafter,the data processed in the first step are input to the joint model of Convolutional Neural Networks(CNN)and Long short-term memory(LSTM)for fine-grained analysis to detect the small class of anomalies in the second step.Extensive experiments show that our work can reduce a lot of manual work and outperform the state-of-art anomalies classification algorithms for power dispatching data network.
文摘Background The abnormalities of coronary arteries, though rare and sometimes benign, may first present clinically as myocardial infarction or sudden death. Multi-detector computed tomography （MDCT） is a non-invasive test that is highly suitable for detecting these anomalies. The study aimed to review the 64-MDCT appearance of the coronary artery anomalies in 66 patients and to discuss the clinical importance of these anomalies.Methods In 6014 consecutive patients examined over 12 months by 64-MDCT for the study of coronary artery disease, 66 were diagnosed for coronary artery anomalies. All patients were symptomatic for one or more of the following diseases： chest pain, dyspnoea, palpitations, arrhythmia and myocardial infarction. Nine patients had undergone a coronary angiography. All the CT images were evaluated by two radiologists and one cardiologist. The right coronary artery （RCA） and the conus branch arising separately, myocardial bridging and duplication of arteries were not analysed in our study.Results The incidence of coronary artery anomalies found in our study group was 1.097%. In the selected patients, seven different types of coronary anomalies were found by 64-MDCT examination. The high takeoff, origin of the coronary artery from the opposite or noncoronary sinus with an anomalous course, and coronary artery fistula were the three common forms of anomalies （n=16, 18 and 16, respectively）. Compared with the results of the coronary angiography, the number of the drainage sites of two coronary artery fistula was less in MDCT images （3 small sites in total）. In all cases, coronary artery computed tomography angiography （CTA） technique was able to recognize the origin of the coronary artery, its three-dimensional course and its spatial relationship with the adjacent structures. Conventional coronary angiography in two cases, however, was unable to provide sufficient information for correct and complete diagnosis.Conclusions In conclusion, the study showed that 64-MDCT, especially the volume rendering technique （VRT）, may be useful for the assessment of complex variations, even if the conventional angiography may not be sufficient. It may be considered as the first-choice imaging modality when an anomalous coronary artery is suspected.
基金the National Science Foundation(NSF)for supporting this research projectthe help of OPAL-RT support team.
文摘To guarantee a reliable power supply,the expected operation of all the components in the power system is critical.Distance protection system is primarily responsible of isolating the faulty section from the healthy part for the grid.Failure in protection devices can result in multiple conflicting alarms at the power grid operation center and complex event analysis to manually find the root cause of the observed system state.If not handled in time,it may lead to the propagation of the faults/failures to the adjacent transmission lines and components.With availability of the synchronized measurements from phasor measurement units(PMUs),real-time system monitoring and automated failure diagnosis are feasible.With multiple adverse events and possible data anomalies,the complexity of the problem will be escalated.In this paper,a PMUbased algorithm is presented and discussed to detect the root cause of the failure in transmission protection system based on the observed state,e.g.multiple line tripping andbreaker failures.The failure diagnosis algorithm is further enhanced to come up with the fully functional version of the failure diagnosis tool,which is tailored for the cases in which the PMU anomalies are present.In the developed algorithm,the validity of the PMU data is critical.However,such causes as communication errors or cyber-attacks might lead to the PMU data anomalies.This issue is welladdressed in this paper and some major types of anomaly detection methods suitable for PMU data are discussed.Results show that the ensemble approach has some distinct advantages in data anomaly detection compared to the previously used standalone algorithms.Additionally,the enhanced failure diagnosis method is developed to clean the inaccurate data in case of the anomaly in measured voltage magnitudes.Finally,both original and enhanced versions of the tool are tested on 96-bus test system using the real-time OPAL-RT simulator.The results show the accuracy of the enhanced tool and its advantages over the primary version of the tool.
