AIM: To investigate the protein and mRNA expression of semaphorin 6D in gastric carcinoma and its significance. METHODS: The protein and mRNA expression of semaphorin 6D was detected by semi-quantitative rever- se tra...AIM: To investigate the protein and mRNA expression of semaphorin 6D in gastric carcinoma and its significance. METHODS: The protein and mRNA expression of semaphorin 6D was detected by semi-quantitative rever- se transcription PCR and Western blotting respectively in 30 cases of gastric carcinoma and normal gastric mucosa. RESULTS: The protein and mRNA expression of semaphorin 6D in gastric carcinoma was significantly higher than that in normal gastric mucosa (0.24 ± 0.06 vs 0.19 ± 0.07, 0.26 ± 0.09 vs 0.20 ± 0.10, P < 0.05). CONCLUSION: Semaphorin 6D may play an important role in the occurrence and development of gastric carcinoma, and is related to tumor angiogenesis.展开更多
General noise cost functions have been recently proposed for support vector regression(SVR). When applied to tasks whose underlying noise distribution is similar to the one assumed for the cost function, these models ...General noise cost functions have been recently proposed for support vector regression(SVR). When applied to tasks whose underlying noise distribution is similar to the one assumed for the cost function, these models should perform better than classical -SVR. On the other hand, uncertainty estimates for SVR have received a somewhat limited attention in the literature until now and still have unaddressed problems. Keeping this in mind,three main goals are addressed here. First, we propose a framework that uses a combination of general noise SVR models with naive online R minimization algorithm(NORMA) as optimization method, and then gives nonconstant error intervals dependent upon input data aided by the use of clustering techniques. We give theoretical details required to implement this framework for Laplace, Gaussian, Beta, Weibull and Marshall–Olkin generalized exponential distributions. Second, we test the proposed framework in two real-world regression problems using data of two public competitions about solar energy. Results show the validity of our models and an improvement over classical -SVR. Finally, in accordance with the principle of reproducible research, we make sure that data and model implementations used for the experiments are easily and publicly accessible.展开更多
文摘AIM: To investigate the protein and mRNA expression of semaphorin 6D in gastric carcinoma and its significance. METHODS: The protein and mRNA expression of semaphorin 6D was detected by semi-quantitative rever- se transcription PCR and Western blotting respectively in 30 cases of gastric carcinoma and normal gastric mucosa. RESULTS: The protein and mRNA expression of semaphorin 6D in gastric carcinoma was significantly higher than that in normal gastric mucosa (0.24 ± 0.06 vs 0.19 ± 0.07, 0.26 ± 0.09 vs 0.20 ± 0.10, P < 0.05). CONCLUSION: Semaphorin 6D may play an important role in the occurrence and development of gastric carcinoma, and is related to tumor angiogenesis.
基金With partial support from Spain’s grants TIN2013-42351-P, TIN2016-76406-P, TIN2015-70308-REDT, as well as S2013/ICE-2845 CASI-CAM-CMsupported also by project FACIL–Ayudas Fundación BBVA a Equipos de Investigación Científica 2016
文摘General noise cost functions have been recently proposed for support vector regression(SVR). When applied to tasks whose underlying noise distribution is similar to the one assumed for the cost function, these models should perform better than classical -SVR. On the other hand, uncertainty estimates for SVR have received a somewhat limited attention in the literature until now and still have unaddressed problems. Keeping this in mind,three main goals are addressed here. First, we propose a framework that uses a combination of general noise SVR models with naive online R minimization algorithm(NORMA) as optimization method, and then gives nonconstant error intervals dependent upon input data aided by the use of clustering techniques. We give theoretical details required to implement this framework for Laplace, Gaussian, Beta, Weibull and Marshall–Olkin generalized exponential distributions. Second, we test the proposed framework in two real-world regression problems using data of two public competitions about solar energy. Results show the validity of our models and an improvement over classical -SVR. Finally, in accordance with the principle of reproducible research, we make sure that data and model implementations used for the experiments are easily and publicly accessible.
