BACKGROUND Cat scratch disease(CSD)is the most common human infection caused by Barto-nella henselae(B.henselae).The main manifestation is self-limited lymphaden-opathy that primarily affects adolescents,and typically...BACKGROUND Cat scratch disease(CSD)is the most common human infection caused by Barto-nella henselae(B.henselae).The main manifestation is self-limited lymphaden-opathy that primarily affects adolescents,and typically resolves without treat-ment within 2-4 months.However,individuals with compromised immune systems or immunodeficiency require specific antibacterial therapy following diagnosis.Due to its low incidence,nonspecific clinical manifestations,and diagnostic limitations,this condition often poses challenges for clinicians in terms of missed diagnoses and misdiagnoses.CASE SUMMARY The child was ultimately diagnosed with CSD.The primary manifestations included nocturnal fever,enlargement of lymph nodes in the neck,axilla and groin,and suspected brucellosis;however,both brucellosis tests conducted during the course of the illness yielded negative results.Bone marrow cytology indicated stimulated proliferation.Lymph node biopsy indicated hyperplasia of lymphoid tissue in the cervical lymph nodes(right),with combined immunohisto-chemical findings indicating reactive hyperplasia.Immunohistochemical analysis revealed CD20 B(+),CD3 T(+),BCL-6(+),and BCL-2(-).CD21 FDC networks were present and Ki67 expression in the germinal center was~80%.Blood next-generation sequencing indicated B.henselae sequence number was 3.Serological test results demonstrated positive antibody response to B.henselae IgG(+),B.henselae IgM(+),Bartonella quintana(B.quintana)IgG(-)and B.quintana IgM(-),and the final diagnosis was CSD.CONCLUSION In patients presenting with fever at night and swollen lymph nodes of unknown origin,CSD should be considered.展开更多
To estimate the parameters of the mixed additive and multiplicative(MAM)random error model using the weighted least squares iterative algorithm that requires derivation of the complex weight array,we introduce a deriv...To estimate the parameters of the mixed additive and multiplicative(MAM)random error model using the weighted least squares iterative algorithm that requires derivation of the complex weight array,we introduce a derivative-free cat swarm optimization for parameter estimation.We embed the Powell method,which uses conjugate direction acceleration and does not need to derive the objective function,into the original cat swarm optimization to accelerate its convergence speed and search accuracy.We use the ordinary least squares,weighted least squares,original cat swarm optimization,particle swarm algorithm and improved cat swarm optimization to estimate the parameters of the straight-line fitting MAM model with lower nonlinearity and the DEM MAM model with higher nonlinearity,respectively.The experimental results show that the improved cat swarm optimization has faster convergence speed,higher search accuracy,and better stability than the original cat swarm optimization and the particle swarm algorithm.At the same time,the improved cat swarm optimization can obtain results consistent with the weighted least squares method based on the objective function only while avoiding multiple complex weight array derivations.The method in this paper provides a new idea for theoretical research on parameter estimation of MAM error models.展开更多
Accurate soil prediction is a vital parameter involved to decide appro-priate crop,which is commonly carried out by the farmers.Designing an auto-mated soil prediction tool helps to considerably improve the efficacy of...Accurate soil prediction is a vital parameter involved to decide appro-priate crop,which is commonly carried out by the farmers.Designing an auto-mated soil prediction tool helps to considerably improve the efficacy of the farmers.At the same time,fuzzy logic(FL)approaches can be used for the design of predictive models,particularly,Fuzzy Cognitive Maps(FCMs)have involved the concept of uncertainty representation and cognitive mapping.In other words,the FCM is an integration of the recurrent neural network(RNN)and FL involved in the knowledge engineering phase.In this aspect,this paper introduces effective fuzzy cognitive maps with cat swarm optimization for automated soil classifica-tion(FCMCSO-ASC)technique.The goal of the FCMCSO-ASC technique is to identify and categorize seven different types of soil.To accomplish this,the FCMCSO-ASC technique incorporates local diagonal extrema pattern(LDEP)as a feature extractor for producing a collection of feature vectors.In addition,the FCMCSO model is applied for soil classification and the weight values of the FCM model are optimally adjusted by the use of CSO algorithm.For exam-ining the enhanced soil classification outcomes of the FCMCSO-ASC technique,a series of simulations were carried out on benchmark dataset and the experimen-tal outcomes reported the enhanced performance of the FCMCSO-ASC technique over the recent techniques with maximum accuracy of 96.84%.展开更多
基金Supported by Shaanxi Natural Science Foundation,No.2020SF-004.
文摘BACKGROUND Cat scratch disease(CSD)is the most common human infection caused by Barto-nella henselae(B.henselae).The main manifestation is self-limited lymphaden-opathy that primarily affects adolescents,and typically resolves without treat-ment within 2-4 months.However,individuals with compromised immune systems or immunodeficiency require specific antibacterial therapy following diagnosis.Due to its low incidence,nonspecific clinical manifestations,and diagnostic limitations,this condition often poses challenges for clinicians in terms of missed diagnoses and misdiagnoses.CASE SUMMARY The child was ultimately diagnosed with CSD.The primary manifestations included nocturnal fever,enlargement of lymph nodes in the neck,axilla and groin,and suspected brucellosis;however,both brucellosis tests conducted during the course of the illness yielded negative results.Bone marrow cytology indicated stimulated proliferation.Lymph node biopsy indicated hyperplasia of lymphoid tissue in the cervical lymph nodes(right),with combined immunohisto-chemical findings indicating reactive hyperplasia.Immunohistochemical analysis revealed CD20 B(+),CD3 T(+),BCL-6(+),and BCL-2(-).CD21 FDC networks were present and Ki67 expression in the germinal center was~80%.Blood next-generation sequencing indicated B.henselae sequence number was 3.Serological test results demonstrated positive antibody response to B.henselae IgG(+),B.henselae IgM(+),Bartonella quintana(B.quintana)IgG(-)and B.quintana IgM(-),and the final diagnosis was CSD.CONCLUSION In patients presenting with fever at night and swollen lymph nodes of unknown origin,CSD should be considered.
基金supported by the National Natural Science Foundation of China(No.42174011 and No.41874001).
文摘To estimate the parameters of the mixed additive and multiplicative(MAM)random error model using the weighted least squares iterative algorithm that requires derivation of the complex weight array,we introduce a derivative-free cat swarm optimization for parameter estimation.We embed the Powell method,which uses conjugate direction acceleration and does not need to derive the objective function,into the original cat swarm optimization to accelerate its convergence speed and search accuracy.We use the ordinary least squares,weighted least squares,original cat swarm optimization,particle swarm algorithm and improved cat swarm optimization to estimate the parameters of the straight-line fitting MAM model with lower nonlinearity and the DEM MAM model with higher nonlinearity,respectively.The experimental results show that the improved cat swarm optimization has faster convergence speed,higher search accuracy,and better stability than the original cat swarm optimization and the particle swarm algorithm.At the same time,the improved cat swarm optimization can obtain results consistent with the weighted least squares method based on the objective function only while avoiding multiple complex weight array derivations.The method in this paper provides a new idea for theoretical research on parameter estimation of MAM error models.
基金supported by the Researchers Supporting Program(TUMA-Project-2021-27)Almaarefa University,Riyadh,Saudi Arabia.Taif University Researchers Supporting Project Number(TURSP-2020/161)Taif University,Taif,Saudi Arabia.
文摘Accurate soil prediction is a vital parameter involved to decide appro-priate crop,which is commonly carried out by the farmers.Designing an auto-mated soil prediction tool helps to considerably improve the efficacy of the farmers.At the same time,fuzzy logic(FL)approaches can be used for the design of predictive models,particularly,Fuzzy Cognitive Maps(FCMs)have involved the concept of uncertainty representation and cognitive mapping.In other words,the FCM is an integration of the recurrent neural network(RNN)and FL involved in the knowledge engineering phase.In this aspect,this paper introduces effective fuzzy cognitive maps with cat swarm optimization for automated soil classifica-tion(FCMCSO-ASC)technique.The goal of the FCMCSO-ASC technique is to identify and categorize seven different types of soil.To accomplish this,the FCMCSO-ASC technique incorporates local diagonal extrema pattern(LDEP)as a feature extractor for producing a collection of feature vectors.In addition,the FCMCSO model is applied for soil classification and the weight values of the FCM model are optimally adjusted by the use of CSO algorithm.For exam-ining the enhanced soil classification outcomes of the FCMCSO-ASC technique,a series of simulations were carried out on benchmark dataset and the experimen-tal outcomes reported the enhanced performance of the FCMCSO-ASC technique over the recent techniques with maximum accuracy of 96.84%.
