An illness known as pneumonia causes inflammation in the lungs.Since there is so much information available fromvarious X-ray images,diagnosing pneumonia has typically proven challenging.To improve image quality and s...An illness known as pneumonia causes inflammation in the lungs.Since there is so much information available fromvarious X-ray images,diagnosing pneumonia has typically proven challenging.To improve image quality and speed up the diagnosis of pneumonia,numerous approaches have been devised.To date,several methods have been employed to identify pneumonia.The Convolutional Neural Network(CNN)has achieved outstanding success in identifying and diagnosing diseases in the fields of medicine and radiology.However,these methods are complex,inefficient,and imprecise to analyze a big number of datasets.In this paper,a new hybrid method for the automatic classification and identification of Pneumonia from chest X-ray images is proposed.The proposed method(ABOCNN)utilized theAfrican BuffaloOptimization(ABO)algorithmto enhanceCNNperformance and accuracy.The Weinmed filter is employed for pre-processing to eliminate unwanted noises from chest X-ray images,followed by feature extraction using the Grey Level Co-Occurrence Matrix(GLCM)approach.Relevant features are then selected from the dataset using the ABO algorithm,and ultimately,high-performance deep learning using the CNN approach is introduced for the classification and identification of Pneumonia.Experimental results on various datasets showed that,when contrasted to other approaches,the ABO-CNN outperforms them all for the classification tasks.The proposed method exhibits superior values like 96.95%,88%,86%,and 86%for accuracy,precision,recall,and F1-score,respectively.展开更多
A cross-sectional study was conducted to determine the Seropositivity of Toxoplasma gondii in water buffalo in three Iraqi governorates (Baghdad, Dhi Qar, and Maysan) and to estimate the risk aspects related to infest...A cross-sectional study was conducted to determine the Seropositivity of Toxoplasma gondii in water buffalo in three Iraqi governorates (Baghdad, Dhi Qar, and Maysan) and to estimate the risk aspects related to infestation throughout the period from January to December 2019. A total of 430 serum samples were inspected with a commercial ELISA (Enzyme linked immunosorbent assay) kit. Indirect multi-species kit. The overall Seropositivity of T. gondii in the examined local buffalo was 7.4%, and the highest rate (9.3%) was in Baghdad Governorate. A multivariate regression analysis revealed that adult buffalo (OR = 7.10;95% CI: 0.87-57.68;P = 0.067) and young herds (OR = 8.42;95% CI: 1.07-66.02;P = 0.043) were more subject to infestation from young buffalo and large herds. Furthermore, the hazard of toxoplasmosis was increased in winter especially among animals in contact with cats. It is therefore requisite to determine risk aspects to evaluate which mitigation, control, prevention and procedures should be carried out to diminish, control and prevent infestation with T. gondii and its propagation.展开更多
Background During mammalian pre-implantation embryonic development(PED),the process of maternal-to-zygote transition(MZT)is well orchestrated by epigenetic modification and gene sequential expression,and it is related...Background During mammalian pre-implantation embryonic development(PED),the process of maternal-to-zygote transition(MZT)is well orchestrated by epigenetic modification and gene sequential expression,and it is related to the embryonic genome activation(EGA).During MZT,the embryos are sensitive to the environment and easy to arrest at this stage in vitro.However,the timing and regulation mechanism of EGA in buffaloes remain obscure.Results Buffalo pre-implantation embryos were subjected to trace cell based RNA-seq and whole-genome bisulfite sequencing(WGBS)to draw landscapes of transcription and DNA-methylation.Four typical developmental steps were classified during buffalo PED.Buffalo major EGA was identified at the 16-cell stage by the comprehensive analy-sis of gene expression and DNA methylation dynamics.By weighted gene co-expression network analysis,stage-spe-cific modules were identified during buffalo maternal-to-zygotic transition,and key signaling pathways and biological process events were further revealed.Programmed and continuous activation of these pathways was necessary for success of buffalo EGA.In addition,the hub gene,CDK1,was identified to play a critical role in buffalo EGA.Conclusions Our study provides a landscape of transcription and DNA methylation in buffalo PED and reveals deeply the molecular mechanism of the buffalo EGA and genetic programming during buffalo MZT.It will lay a foundation for improving the in vitro development of buffalo embryos.展开更多
Software needs modifications and requires revisions regularly.Owing to these revisions,retesting software becomes essential to ensure that the enhancements made,have not affected its bug-free functioning.The time and ...Software needs modifications and requires revisions regularly.Owing to these revisions,retesting software becomes essential to ensure that the enhancements made,have not affected its bug-free functioning.The time and cost incurred in this process,need to be reduced by the method of test case selection and prioritization.It is observed that many nature-inspired techniques are applied in this area.African Buffalo Optimization is one such approach,applied to regression test selection and prioritization.In this paper,the proposed work explains and proves the applicability of the African Buffalo Optimization approach to test case selection and prioritization.The proposed algorithm converges in polynomial time(O(n^(2))).In this paper,the empirical evaluation of applying African Buffalo Optimization for test case prioritization is done on sample data set with multiple iterations.An astounding 62.5%drop in size and a 48.57%drop in the runtime of the original test suite were recorded.The obtained results are compared with Ant Colony Optimization.The comparative analysis indicates that African Buffalo Optimization and Ant Colony Optimization exhibit similar fault detection capabilities(80%),and a reduction in the overall execution time and size of the resultant test suite.The results and analysis,hence,advocate and encourages the use of African Buffalo Optimization in the area of test case selection and prioritization.展开更多
Objective:To optimize the concentration of PDC-109 protein in semen for higher cryopreservability and fertility by sequestration or neutralization of PDC-109 by its antibodies(anti-PDC-109 antibodies)in bubaline speci...Objective:To optimize the concentration of PDC-109 protein in semen for higher cryopreservability and fertility by sequestration or neutralization of PDC-109 by its antibodies(anti-PDC-109 antibodies)in bubaline species.Methods:PDC-109 protein was purified by applying two-step chromatography procedures.Purified protein was injected in rabbits to raise antibodies.These raised anti-PDC-109 antibodies were used in neutralization or sequestration of PDC-109 in in-vitro model.Ejaculates were collected from buffaloes and splited for four groups.Group 1 received egg yolk Tris glycerol extender,without anti-PDC-109 antibodies,while group 2 to 4 received anti-PDC-109 antibodies 266μg/mL,80μg/mL,and 26μg/mL in Tris-fructose-citrate buffer,respectively.Semen quality parameters viz.,forward progressive motility,viability,total morphological abnormality,acrosomal integrity,plasma membrane integrity,cryoinjury and in-vitro zona binding index were evaluated.Results:Semen quality parameters of neat semen were within the normal range of bubaline species.Sperm motility,livability,acrosomal integrity,plasma membrane integrity,and cholesterol content of sperm were decreased and total sperm abnormality was increased significantly in post-thaw semen compared to those in pre-freeze and fresh semen(P<0.05).Semen in group 2 had higher sperm motility,livability,acrosomal integrity,plasma membrane integrity,and cholesterol content of sperm and lower total sperm abnormality significantly compared to those in group 1,3 and 4 at pre-freeze and post-thaw stages(P<0.05).Conclusions:Sequestration or neutralization of PDC-109 by its antibodies significantly improves pre-freeze,and post-thaw semen quality parameters and in-vitro zona binding index with simultaneously reducing cryoinjury or cryodamage in the sperm of bubaline species.展开更多
In the discipline of Music Information Retrieval(MIR),categorizing musicfiles according to their genre is a difficult process.Music genre classifica-tion is an important multimedia research domain for classification of mu...In the discipline of Music Information Retrieval(MIR),categorizing musicfiles according to their genre is a difficult process.Music genre classifica-tion is an important multimedia research domain for classification of music data-bases.In the proposed method music genre classification using features obtained from audio data is proposed.The classification is done using features extracted from the audio data of popular online repository namely GTZAN,ISMIR 2004 and Latin Music Dataset(LMD).The features highlight the differences between different musical styles.In the proposed method,feature selection is per-formed using an African Buffalo Optimization(ABO),and the resulting features are employed to classify the audio using Back Propagation Neural Networks(BPNN),Support Vector Machine(SVM),Naïve Bayes,decision tree and kNN classifiers.Performance evaluation reveals that,ABO based feature selection strategy achieves an average accuracy of 82%with mean square error(MSE)of 0.003 when used with neural network classifier.展开更多
基金the Researchers Supporting Project Number(RSP2023 R157),King Saud University,Riyadh,Saudi Arabia.
文摘An illness known as pneumonia causes inflammation in the lungs.Since there is so much information available fromvarious X-ray images,diagnosing pneumonia has typically proven challenging.To improve image quality and speed up the diagnosis of pneumonia,numerous approaches have been devised.To date,several methods have been employed to identify pneumonia.The Convolutional Neural Network(CNN)has achieved outstanding success in identifying and diagnosing diseases in the fields of medicine and radiology.However,these methods are complex,inefficient,and imprecise to analyze a big number of datasets.In this paper,a new hybrid method for the automatic classification and identification of Pneumonia from chest X-ray images is proposed.The proposed method(ABOCNN)utilized theAfrican BuffaloOptimization(ABO)algorithmto enhanceCNNperformance and accuracy.The Weinmed filter is employed for pre-processing to eliminate unwanted noises from chest X-ray images,followed by feature extraction using the Grey Level Co-Occurrence Matrix(GLCM)approach.Relevant features are then selected from the dataset using the ABO algorithm,and ultimately,high-performance deep learning using the CNN approach is introduced for the classification and identification of Pneumonia.Experimental results on various datasets showed that,when contrasted to other approaches,the ABO-CNN outperforms them all for the classification tasks.The proposed method exhibits superior values like 96.95%,88%,86%,and 86%for accuracy,precision,recall,and F1-score,respectively.
文摘A cross-sectional study was conducted to determine the Seropositivity of Toxoplasma gondii in water buffalo in three Iraqi governorates (Baghdad, Dhi Qar, and Maysan) and to estimate the risk aspects related to infestation throughout the period from January to December 2019. A total of 430 serum samples were inspected with a commercial ELISA (Enzyme linked immunosorbent assay) kit. Indirect multi-species kit. The overall Seropositivity of T. gondii in the examined local buffalo was 7.4%, and the highest rate (9.3%) was in Baghdad Governorate. A multivariate regression analysis revealed that adult buffalo (OR = 7.10;95% CI: 0.87-57.68;P = 0.067) and young herds (OR = 8.42;95% CI: 1.07-66.02;P = 0.043) were more subject to infestation from young buffalo and large herds. Furthermore, the hazard of toxoplasmosis was increased in winter especially among animals in contact with cats. It is therefore requisite to determine risk aspects to evaluate which mitigation, control, prevention and procedures should be carried out to diminish, control and prevent infestation with T. gondii and its propagation.
基金funded by the National Natural Science Foundation of China (31972996 and 32160790)Guangxi Bagui Scholar ProgramGuangxi Innovation-Driven Development Project (AA17204051)
文摘Background During mammalian pre-implantation embryonic development(PED),the process of maternal-to-zygote transition(MZT)is well orchestrated by epigenetic modification and gene sequential expression,and it is related to the embryonic genome activation(EGA).During MZT,the embryos are sensitive to the environment and easy to arrest at this stage in vitro.However,the timing and regulation mechanism of EGA in buffaloes remain obscure.Results Buffalo pre-implantation embryos were subjected to trace cell based RNA-seq and whole-genome bisulfite sequencing(WGBS)to draw landscapes of transcription and DNA-methylation.Four typical developmental steps were classified during buffalo PED.Buffalo major EGA was identified at the 16-cell stage by the comprehensive analy-sis of gene expression and DNA methylation dynamics.By weighted gene co-expression network analysis,stage-spe-cific modules were identified during buffalo maternal-to-zygotic transition,and key signaling pathways and biological process events were further revealed.Programmed and continuous activation of these pathways was necessary for success of buffalo EGA.In addition,the hub gene,CDK1,was identified to play a critical role in buffalo EGA.Conclusions Our study provides a landscape of transcription and DNA methylation in buffalo PED and reveals deeply the molecular mechanism of the buffalo EGA and genetic programming during buffalo MZT.It will lay a foundation for improving the in vitro development of buffalo embryos.
基金This research is funded by the Deanship of Scientific Research at Umm Al-Qura University,Grant Code:22UQU4281755DSR02.
文摘Software needs modifications and requires revisions regularly.Owing to these revisions,retesting software becomes essential to ensure that the enhancements made,have not affected its bug-free functioning.The time and cost incurred in this process,need to be reduced by the method of test case selection and prioritization.It is observed that many nature-inspired techniques are applied in this area.African Buffalo Optimization is one such approach,applied to regression test selection and prioritization.In this paper,the proposed work explains and proves the applicability of the African Buffalo Optimization approach to test case selection and prioritization.The proposed algorithm converges in polynomial time(O(n^(2))).In this paper,the empirical evaluation of applying African Buffalo Optimization for test case prioritization is done on sample data set with multiple iterations.An astounding 62.5%drop in size and a 48.57%drop in the runtime of the original test suite were recorded.The obtained results are compared with Ant Colony Optimization.The comparative analysis indicates that African Buffalo Optimization and Ant Colony Optimization exhibit similar fault detection capabilities(80%),and a reduction in the overall execution time and size of the resultant test suite.The results and analysis,hence,advocate and encourages the use of African Buffalo Optimization in the area of test case selection and prioritization.
文摘Objective:To optimize the concentration of PDC-109 protein in semen for higher cryopreservability and fertility by sequestration or neutralization of PDC-109 by its antibodies(anti-PDC-109 antibodies)in bubaline species.Methods:PDC-109 protein was purified by applying two-step chromatography procedures.Purified protein was injected in rabbits to raise antibodies.These raised anti-PDC-109 antibodies were used in neutralization or sequestration of PDC-109 in in-vitro model.Ejaculates were collected from buffaloes and splited for four groups.Group 1 received egg yolk Tris glycerol extender,without anti-PDC-109 antibodies,while group 2 to 4 received anti-PDC-109 antibodies 266μg/mL,80μg/mL,and 26μg/mL in Tris-fructose-citrate buffer,respectively.Semen quality parameters viz.,forward progressive motility,viability,total morphological abnormality,acrosomal integrity,plasma membrane integrity,cryoinjury and in-vitro zona binding index were evaluated.Results:Semen quality parameters of neat semen were within the normal range of bubaline species.Sperm motility,livability,acrosomal integrity,plasma membrane integrity,and cholesterol content of sperm were decreased and total sperm abnormality was increased significantly in post-thaw semen compared to those in pre-freeze and fresh semen(P<0.05).Semen in group 2 had higher sperm motility,livability,acrosomal integrity,plasma membrane integrity,and cholesterol content of sperm and lower total sperm abnormality significantly compared to those in group 1,3 and 4 at pre-freeze and post-thaw stages(P<0.05).Conclusions:Sequestration or neutralization of PDC-109 by its antibodies significantly improves pre-freeze,and post-thaw semen quality parameters and in-vitro zona binding index with simultaneously reducing cryoinjury or cryodamage in the sperm of bubaline species.
文摘In the discipline of Music Information Retrieval(MIR),categorizing musicfiles according to their genre is a difficult process.Music genre classifica-tion is an important multimedia research domain for classification of music data-bases.In the proposed method music genre classification using features obtained from audio data is proposed.The classification is done using features extracted from the audio data of popular online repository namely GTZAN,ISMIR 2004 and Latin Music Dataset(LMD).The features highlight the differences between different musical styles.In the proposed method,feature selection is per-formed using an African Buffalo Optimization(ABO),and the resulting features are employed to classify the audio using Back Propagation Neural Networks(BPNN),Support Vector Machine(SVM),Naïve Bayes,decision tree and kNN classifiers.Performance evaluation reveals that,ABO based feature selection strategy achieves an average accuracy of 82%with mean square error(MSE)of 0.003 when used with neural network classifier.
