Objective： In our previous work, we incorporated complete blood count （CBC） into TNM stage to develop a new prognostic score model, which was validated to improve prediction efficiency of TNM stage for nasopharynge...Objective： In our previous work, we incorporated complete blood count （CBC） into TNM stage to develop a new prognostic score model, which was validated to improve prediction efficiency of TNM stage for nasopharyngeal carcinoma （NPC）. The purpose of this study was to revalidate the accuracy of the model, and its superiority to TNM stage, through data from a prospective study.Methods： CBC of 249 eligible patients from the 863 Program No. 2006AA02Z4B4 was evaluated. Prognostic index （PI） of each patient was calculated according to the score model. Then they were divided by the PI into three categories： the low-, intermediate-and high-risk patients. The 5-year disease-specific survival （DSS） of the three categories was compared by a log-rank test. The model and TNM stage （Tth edition） were compared on efficiency for predicting the 5-year DSS, through comparison of the area under curve （AUC） of their receiver-operating characteristic curves.Results： The 5-year DSS of the low-, intermediate- and high-risk patients were 96.0%, 79.1% and 62.2%, respectively. The low- and intermediate-risk patients had better DSS than the high-risk patients （P〈0.001 and P〈0.005, respectively）. And there was a trend of better DSS in the low-risk patients, compared with the intermediate-risk patients （P=0.049）. The AUC of the model was larger than that of TNM stage （0.726 vs. 0.661, P：0.023）. Conclusions： A CBC-based prognostic score model was revalidated to be accurate and superior to TNM stage on predicting 5-year DSS of NPC.展开更多
The objective of this study was to analyze the relationship of somatic cell count （SCC） with milk yield, fat and protein percentage, fat and protein yield using analysis of variance and correlation analysis in Chine...The objective of this study was to analyze the relationship of somatic cell count （SCC） with milk yield, fat and protein percentage, fat and protein yield using analysis of variance and correlation analysis in Chinese Holstein population. The 10 524 test-day records of 568 Chinese Holstein Cattle were obtained from 2 commercial herds in Xi＇an region of China during February 2002 to March 2009. Milk yield, fat percentage, fat and protein yield initially increased and then dropped down with parity, whereas protein percentage decreased and SCC increased. Analysis of variance showed highly significant effects of different subclasses SCC on milk yield and composition （P〈 0.01）. Compared with milk yield with SCC ≤ 200 000 cells mL-1, milk yield losses with SCC of 200 000-500 000 cells mL-1, 501000-1 000 000 cells mL-1, ≥ 1 000 000 cells mL-1 were 0.387, 0.961 and 2.351 kg, respectively. The highly significant negative correlation coefficient between somatic cell score （SCS） and milk and protein yield, milk yield and fat and protein percentage, protein percentage and fat yield were -0.084, -0.037, -0.061, -0.168, and -0.088, respectively （P〈 0.01）. The highly significant positive correlation coefficients between SCS and fat yield and fat and protein percentage, milk yield and fat and protein yield, fat percentage and protein percentage and fat yield, protein yield and protein percentage and fat yield were 0.041, 0.177, 0.105, 0.771, 0.865, 0.122, 0.568, 0.318, and 0.695, respectively （P〈 0.01）. There was no significant relationship between fat percentage and protein yield （P 〉 0.05）. The results of the present study first time provide the relevant base-line data for assessing milk production at Xi＇an region of China.展开更多
Frequent counting is a very so often required operation in machine learning algorithms. A typical machine learning task, learning the structure of Bayesian network （BN） based on metric scoring, is introduced as an e...Frequent counting is a very so often required operation in machine learning algorithms. A typical machine learning task, learning the structure of Bayesian network （BN） based on metric scoring, is introduced as an example that heavily relies on frequent counting. A fast calculation method for frequent counting enhanced with two cache layers is then presented for learning BN. The main contribution of our approach is to eliminate comparison operations for frequent counting by introducing a multi-radix number system calculation. Both mathematical analysis and empirical comparison between our method and state-of-the-art solution are conducted. The results show that our method is dominantly superior to state-of-the-art solution in solving the problem of learning BN.展开更多
基金supported by Hi-Tech Research and Development Program of China (863 Program) (No.2006AA02Z4B4)
文摘Objective： In our previous work, we incorporated complete blood count （CBC） into TNM stage to develop a new prognostic score model, which was validated to improve prediction efficiency of TNM stage for nasopharyngeal carcinoma （NPC）. The purpose of this study was to revalidate the accuracy of the model, and its superiority to TNM stage, through data from a prospective study.Methods： CBC of 249 eligible patients from the 863 Program No. 2006AA02Z4B4 was evaluated. Prognostic index （PI） of each patient was calculated according to the score model. Then they were divided by the PI into three categories： the low-, intermediate-and high-risk patients. The 5-year disease-specific survival （DSS） of the three categories was compared by a log-rank test. The model and TNM stage （Tth edition） were compared on efficiency for predicting the 5-year DSS, through comparison of the area under curve （AUC） of their receiver-operating characteristic curves.Results： The 5-year DSS of the low-, intermediate- and high-risk patients were 96.0%, 79.1% and 62.2%, respectively. The low- and intermediate-risk patients had better DSS than the high-risk patients （P〈0.001 and P〈0.005, respectively）. And there was a trend of better DSS in the low-risk patients, compared with the intermediate-risk patients （P=0.049）. The AUC of the model was larger than that of TNM stage （0.726 vs. 0.661, P：0.023）. Conclusions： A CBC-based prognostic score model was revalidated to be accurate and superior to TNM stage on predicting 5-year DSS of NPC.
基金National Natural Science Foundation of China(Nos.61702094 and 62301142)“Chenguang Program”Supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission,China(No.18CG38)。
基金supported by the National 863 Program of China(2008AA10Z144)"13115"Sci-Tech Innovation Program of Shaanxi Province(2008ZDKG-11)
文摘The objective of this study was to analyze the relationship of somatic cell count （SCC） with milk yield, fat and protein percentage, fat and protein yield using analysis of variance and correlation analysis in Chinese Holstein population. The 10 524 test-day records of 568 Chinese Holstein Cattle were obtained from 2 commercial herds in Xi＇an region of China during February 2002 to March 2009. Milk yield, fat percentage, fat and protein yield initially increased and then dropped down with parity, whereas protein percentage decreased and SCC increased. Analysis of variance showed highly significant effects of different subclasses SCC on milk yield and composition （P〈 0.01）. Compared with milk yield with SCC ≤ 200 000 cells mL-1, milk yield losses with SCC of 200 000-500 000 cells mL-1, 501000-1 000 000 cells mL-1, ≥ 1 000 000 cells mL-1 were 0.387, 0.961 and 2.351 kg, respectively. The highly significant negative correlation coefficient between somatic cell score （SCS） and milk and protein yield, milk yield and fat and protein percentage, protein percentage and fat yield were -0.084, -0.037, -0.061, -0.168, and -0.088, respectively （P〈 0.01）. The highly significant positive correlation coefficients between SCS and fat yield and fat and protein percentage, milk yield and fat and protein yield, fat percentage and protein percentage and fat yield, protein yield and protein percentage and fat yield were 0.041, 0.177, 0.105, 0.771, 0.865, 0.122, 0.568, 0.318, and 0.695, respectively （P〈 0.01）. There was no significant relationship between fat percentage and protein yield （P 〉 0.05）. The results of the present study first time provide the relevant base-line data for assessing milk production at Xi＇an region of China.
基金supported by National Natural Science Foundation of China (No.60970055)
文摘Frequent counting is a very so often required operation in machine learning algorithms. A typical machine learning task, learning the structure of Bayesian network （BN） based on metric scoring, is introduced as an example that heavily relies on frequent counting. A fast calculation method for frequent counting enhanced with two cache layers is then presented for learning BN. The main contribution of our approach is to eliminate comparison operations for frequent counting by introducing a multi-radix number system calculation. Both mathematical analysis and empirical comparison between our method and state-of-the-art solution are conducted. The results show that our method is dominantly superior to state-of-the-art solution in solving the problem of learning BN.
