Fruit cracking is a phenomenon in which the peel cracks during grape berry development,which seriously affects the yield and quality of the fruit.However,there are few studies on the mining of candidate genes related ...Fruit cracking is a phenomenon in which the peel cracks during grape berry development,which seriously affects the yield and quality of the fruit.However,there are few studies on the mining of candidate genes related to berry cracking.In order to better understand the genetic basis of berry cracking,we used the results of previous quantitative trait locus(QTL)mapping,combined with field surveys of berry-cracking types and the berry-cracking rate,to mine candidate berry-cracking genes.The results showed that three identical QTL loci were detected in two years(2019 and 2020);and three candidate genes were annotated in the QTL interval.In mature berries,the expressions of the candidate genes were more abundant in the cracking-susceptible parent(‘Crimson Seedless’)than in the cracking-resistant parent(‘Muscat Hamburg’).Grape berry cracking is a complex trait controlled by multiple genes,mainly including genes encoding cellulose synthase–like protein H1,glucan endo-1,3-beta-glucosidase 12,and brassinosteroid insensitive 1-associated receptor kinase 1.The high expression of the candidate berry-cracking genes may promote the occurrence of berry cracking.This study helps elucidate the genetic mechanism of grape berry cracking.展开更多
In today’s growing modern world environment,as human food activities are changing,it is affecting human health,thus leading to diseases like cancer.Cancer is a complex disease with many subtypes that affect human hea...In today’s growing modern world environment,as human food activities are changing,it is affecting human health,thus leading to diseases like cancer.Cancer is a complex disease with many subtypes that affect human health without premature treatment and cause death.So the analysis of early diagnosis and prognosis of cancer studies can improve clinical management by analyzing various features of observa-tion,which has become necessary to classify the type in cancer research.The research needs importance to organize the risk of the cancer patients based on data analysis to predict the result of premature treatment.This paper introduces a Maximal Region-Based Candidate Feature Selection(MRCFS)for early risk diagnosing using Soft-Max Feed Forward Neural Classification(SMF2NC)to solve the above pro-blem.The predictive model is based on a different relational feature learning model,which is possessed to candidate selection to reduce the dimensionality.The redundant features are processed marginal weight rates for observing similar features’variants and the absolute value.Softmax neural hidden layers are trained using the Sigmoid Activation Function(SAF)to create the logical condition for feed-forward layers.Further,the maximal features are introduced to invite a deep neural network con-structed on the Feed Forward Recurrent Neural Network(FFRNN).The classifier produces higher classification accuracy than the previous methods and observes the cancer detection,which is recommended for early diagnosis.展开更多
目的了解东莞地区中学生乙型肝炎病毒(hepatitis B virus,HBV)的感染现状,为HBV感染的防治提供依据。方法采用酶联免疫吸附试验方法(ELISA法)分别检测2001-2007年高考体检学生共50 387人的HBsAg,对检测结果统计分析,比较不同性别和城乡...目的了解东莞地区中学生乙型肝炎病毒(hepatitis B virus,HBV)的感染现状,为HBV感染的防治提供依据。方法采用酶联免疫吸附试验方法(ELISA法)分别检测2001-2007年高考体检学生共50 387人的HBsAg,对检测结果统计分析,比较不同性别和城乡间的HBsAg感染的差异。结果2001-2007年高考学生HBsAg的携带率分别为23.69%、18.58%、16.22%、14.88%、10.98%、6.68%、4.04%,呈逐年下降趋势(P<0.01);乡镇学生HBsAg平均阳性率为12.65%,显著高于城镇学生的平均阳性率8.75%(P<0.01);男性的HBsAg阳性率为12.86%,显著高于女性的阳性率9.2%(P<0.01)。结论被调查的东莞市2001-2007年高考学生的HBV感染率(平均感染率11.14%)高于全国的HBV平均感染率(10%),HBsAg的携带率呈逐年下降趋势;乡镇学生HBV感染率明显高于城镇学生,男生高于女生。展开更多
基金financial support from the Highlevel Scientific Reuter Foundation of Qingdao Agricultural University(Grant Nos.665/1118011,665/1119002)China Agriculture Research System of MOF and MARA(Grant No.CARS-29-yc-1)Crop Resources Protection Program of Ministry of Agriculture and Rural Affairs of China(Grant No.2130135-34).
文摘Fruit cracking is a phenomenon in which the peel cracks during grape berry development,which seriously affects the yield and quality of the fruit.However,there are few studies on the mining of candidate genes related to berry cracking.In order to better understand the genetic basis of berry cracking,we used the results of previous quantitative trait locus(QTL)mapping,combined with field surveys of berry-cracking types and the berry-cracking rate,to mine candidate berry-cracking genes.The results showed that three identical QTL loci were detected in two years(2019 and 2020);and three candidate genes were annotated in the QTL interval.In mature berries,the expressions of the candidate genes were more abundant in the cracking-susceptible parent(‘Crimson Seedless’)than in the cracking-resistant parent(‘Muscat Hamburg’).Grape berry cracking is a complex trait controlled by multiple genes,mainly including genes encoding cellulose synthase–like protein H1,glucan endo-1,3-beta-glucosidase 12,and brassinosteroid insensitive 1-associated receptor kinase 1.The high expression of the candidate berry-cracking genes may promote the occurrence of berry cracking.This study helps elucidate the genetic mechanism of grape berry cracking.
文摘In today’s growing modern world environment,as human food activities are changing,it is affecting human health,thus leading to diseases like cancer.Cancer is a complex disease with many subtypes that affect human health without premature treatment and cause death.So the analysis of early diagnosis and prognosis of cancer studies can improve clinical management by analyzing various features of observa-tion,which has become necessary to classify the type in cancer research.The research needs importance to organize the risk of the cancer patients based on data analysis to predict the result of premature treatment.This paper introduces a Maximal Region-Based Candidate Feature Selection(MRCFS)for early risk diagnosing using Soft-Max Feed Forward Neural Classification(SMF2NC)to solve the above pro-blem.The predictive model is based on a different relational feature learning model,which is possessed to candidate selection to reduce the dimensionality.The redundant features are processed marginal weight rates for observing similar features’variants and the absolute value.Softmax neural hidden layers are trained using the Sigmoid Activation Function(SAF)to create the logical condition for feed-forward layers.Further,the maximal features are introduced to invite a deep neural network con-structed on the Feed Forward Recurrent Neural Network(FFRNN).The classifier produces higher classification accuracy than the previous methods and observes the cancer detection,which is recommended for early diagnosis.
文摘目的了解东莞地区中学生乙型肝炎病毒(hepatitis B virus,HBV)的感染现状,为HBV感染的防治提供依据。方法采用酶联免疫吸附试验方法(ELISA法)分别检测2001-2007年高考体检学生共50 387人的HBsAg,对检测结果统计分析,比较不同性别和城乡间的HBsAg感染的差异。结果2001-2007年高考学生HBsAg的携带率分别为23.69%、18.58%、16.22%、14.88%、10.98%、6.68%、4.04%,呈逐年下降趋势(P<0.01);乡镇学生HBsAg平均阳性率为12.65%,显著高于城镇学生的平均阳性率8.75%(P<0.01);男性的HBsAg阳性率为12.86%,显著高于女性的阳性率9.2%(P<0.01)。结论被调查的东莞市2001-2007年高考学生的HBV感染率(平均感染率11.14%)高于全国的HBV平均感染率(10%),HBsAg的携带率呈逐年下降趋势;乡镇学生HBV感染率明显高于城镇学生,男生高于女生。
