BACKGROUND AND PURPOSE:Locally advanced rectal cancer (LARC) patients showing pathological good response (pGR) of down-staging to ypT0-1N0 after neoadjuvant chemoradiotherapy (nCRT) may receive organ-preserving treatment instead of total mesorectal excision (TME). In the current study, quantitative analysis of diffusion weighted imaging (DWI) is conducted to predict pGR patients in order to provide decision support for organ-preserving strategies.MATERIALS AND METHODS:222 LARC patients receiving nCRT and TME are enrolled from Beijing Cancer Hospital and allocated into training (152) and validation (70) set. Three pGR prediction models are constructed in the training set, including DWI prediction model based on quantitative DWI features, clinical prediction model based on clinical characteristics, and combined prediction model integrating DWI and clinical predictors. Prediction performances are assessed by area under receiver operating characteristic curve (AUC), classification accuracy (ACC), positive and negative predictive values (PPV and NPV).RESULTS:The DWI (AUC = 0.866, ACC = 91.43%) and combined (AUC = 0.890, ACC = 90%) prediction model obtains good prediction performance in the independent validation set. Nevertheless, the clinical prediction model performs worse than the other two models (AUC = 0.631, ACC = 75.71% in validation set). Calibration analysis indicates that the pGR probability predicted by the combined prediction model is close to perfect prediction. Decision curve analysis reveals that the LARC patients will acquire clinical benefit if receiving organ-preserving strategy according to combined prediction model.CONCLUSION:Combination of quantitative DWI analysis and clinical characteristics holds great potential in identifying the pGR patients and providing decision support for organ-preserving strategies after nCRT treatment.

The Locally advanced rectal cancer (LARC) patients were routinely treated with neoadjuvant chemoradiotherapy (CRT) firstly and received total excision afterwards. While, the LARC patients might relieve to T1N0M0/T0N0M0 stage after the CRT, which would enable the patients be qualified for local excision. However, accurate pathological TNM stage could only be obtained by the pathological examination after surgery. We aimed to conduct a Radiomics analysis of Diffusion weighted Imaging (DWI) data to identify the patients in T1N0M0/T0N0M0 stages before surgery, in hope of providing clinical surgery decision support. 223 routinely treated LARC patients in Beijing Cancer Hospital were enrolled in current study. DWI data and clinical characteristics were collected after CRT. According to the pathological TNM stage, the patients of T1N0M0 and T0N0M0 stages were labelled as 1 and the other patients were labelled as 0. The first 123 patients in chronological order were used as training set, and the rest patients as validation set. 563 image features extracted from the DWI data and clinical characteristics were used as features. Two-sample T test was conducted to pre-select the top 50% discriminating features. Least absolute shrinkage and selection operator (Lasso)-Logistic regression model was conducted to further select features and construct the classification model. Based on the 14 selected image features, the area under the Receiver Operating Characteristic (ROC) curve (AUC) of 0.8781, classification Accuracy (ACC) of 0.8432 were achieved in the training set. In the validation set, AUC of 0.8707, ACC (ACC) of 0.84 were observed.

Crowdsourced mobile video streaming enables nearby mobile video users to aggregate network resources to improve their video streaming performances. However, users are often selfish and may not be willing to cooperate without proper incentives. Designing an incentive mechanism for such a scenario is challenging due to the users’ asynchronous downloading behaviors and their private valuations for multi-bitrate encoded videos. In this paper, we propose both the single-object and multi-object multi-dimensional auction mechanisms, through which users sell the opportunities for downloading single and multiple video segments with multiple bitrates, respectively. Both the auction mechanisms can achieve truthfulness (i.e., truthful private information revelation) and efficiency (i.e., social welfare maximization). Simulations with real traces show that crowdsourced mobile streaming facilitated by the auction mechanisms outperforms noncooperative streaming by 48.6% (on average) in terms of social welfare. To evaluate the real-world performance, we also construct a demo system for crowdsourced mobile streaming and implement our proposed auction mechanism. Experiments over the demo show that those users who provide resources to others and those users who receive help can increase their welfares by 15.5% and 35.4% (on average) via cooperation, respectively.

Crowdsourced mobile video streaming enables nearby mobile video users to aggregate their network resources to improve the video streaming performance. However, users are often selfish and may not be willing to cooperate without proper incentives. Designing an incentive mechanism for such a scenario is challenging due to the users' asynchronous downloading behaviors as well as their private valuations for multi-bitrate encoded videos. In this work, we propose a multi-object multi-dimensional auction-based incentive framework, through which users can download multiple video segments with different bitrates for multiple nearby users (and themselves). Based on this incentive framework, we propose a Vickrey-score auction, which is the first multi-object multi-dimensional auction that achieves both truthfulness and efficiency. Simulations with real traces show that crowdsourced mobile streaming outperforms noncooperative streaming by 48.6% (on average) in terms of social welfare. We further implement our proposed auction mechanism in a demostration system, and show that the crowdsourced framework together with the auction mechanism can substantially increase mobile user's welfare and video service stability.

Social TV allows people to meet the demand of social experience while watching videos. Existing solutions cannot balance interest among group and provide good viewing quality for all members with different network bandwidth and devices. Realizing that people in group will be more tolerant, trusting and willing to help each other, we developed a cloud based social TV for online group video service. We emphasize group behavior in our application and introduce the concept of tolerance and trust between users to balance their interest. Group Recommendation results we show in our system can obtain overall high satisfaction while provides diverse content. We also design a collaborative video distribution technology to optimize network resource utility for fluency and high quality viewing experience. The entire operation of the system emphasizes on simplicity, collaboration and smooth, providing excellent social TV experience.

Social TV allows people to meet the demand of social experience while watching videos. Existing solutions cannot balance interest among group and provide good viewing quality for all members with different network bandwidth and devices. Realizing that people in group will be more tolerant, trusting and willing to help each other, we developed a cloud based social TV for online group video service. We emphasize group behavior in our application and introduce the concept of tolerance and trust between users to balance their interest. Group Recommendation results we show in our system can obtain overall high satisfaction while provides diverse content. We also design a collaborative video distribution technology to optimize network resource utility for fluency and high quality viewing experience. The entire operation of the system emphasizes on simplicity, collaboration and smooth, providing excellent social TV experience.

Professor Wang Shou,who was engaged in agricultural education all life,once developed excellent soybean variety Jin Da 332,and advocated using biostatistics in field experiment,is one of the most famous soybean breeding specialist in China.He is noted for his meticulous scholarship,rigorous scientific research,modest learning,simple life style,upright behaviour and sincere with people.He enjoyed the love and esteem by numerous teachers and students.Professor Wang was the director of Shanxi Agricultural College from 1958,and took charge in scientific research in soybean breeding.By his guidance,all staff insisted that the science must serve for production,and the theory must be linked with practice,obtained the great achievement in soybean breeding and soybean biological theory.Jin Dou No 1,No 2 and No 3 soybean varieties,and the papers on soybean biological theory,acquired the reward in National Science Conference in 1978,and the second prize of Shanxi Science and Technology Achievement in 1979,respectively.Recently(May 19th,2008),professor Miao Guoyuan found a precious posthumous paper manuscript of Wang Shou,entrusted Professor Li Guiquan,director of the teaching and research section of crop breeding and genetics in College of Agriculture,Shanxi Agricultural University,to take a comment,and submitted to Journal of Shanxi Agricultural University for publication.To commemorate the fifty years soybean breeding of Shanxi Agricultural University,and to express our cherish memory of this great predecessor.