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ObjectiveThe aim of this study was to evaluate the left and right ventricular segmental and global myocardial function of normal fetuses using velocity vector imaging and explore the correlation between global myocardial function parameters and gestational age.MethodsA total of 127 normal fetuses were selected and divided into five groups according to gestational age for the measurement of their left and right ventricular segmental and global velocity, strain, and strain rate. This study also explored the change trend in the global myocardial function parameters at different gestational ages and analyzed its correlation with gestational age.ResultsThe peak velocities of the biventricular segments of the normal fetuses showed a decreasing trend from the basal to the middle to the apex segment, and the differences were statistically significant (P 0.05). The peak global velocity of both ventricles increased with the gestational age, and it was moderately correlated with gestational age; however, the correlation of strain and peak strain rate with gestational age was not statistically significant (P > 0.05).ConclusionIn normal fetuses, the peak myocardial velocity of the biventricular segments showed a decreasing trend from the basal to the apical segment. The global peak myocardial velocity was linearly correlated with gestational age; however, the global strain and peak strain rate did not change as gestational age increased, indicating that the myocardial deformability of the fetus' ventricles was constant in the middle and late trimesters.

ObjectiveThis study aimed to evaluate biventricular myocardial function and biventricular longitudinal global myocardial function of fetuses at different gestational weeks using ultrasonic velocity vector imaging.MethodsA total of 127 pregnant women were enrolled and divided into five groups according to the gestational age of their fetuses. The velocity, strain, and strain rate of the left and right ventricles were measured, and these biventricular parameters were compared between the groups. The global parameters of the biventricular myocardium were also compared.ResultsA pairwise comparison revealed that the differences in biventricular velocity and strain rate between groups in adjacent gestational weeks were not statistically significant (P > 0.05), but velocity increased with gestational age. A comparison of fetal longitudinal global myocardial parameters revealed that the global velocity, strain, and strain rate of the right ventricle were higher than those of the left ventricle, and the differences were statistically significant (P < 0.05) in all groups.ConclusionThe peak velocities of the fetal left and right ventricles increased with gestational age, but the global strain and strain rate did not, suggesting that fetal myocardial function is mature and constant in the middle and late stages of pregnancy and can more reliably reflect myocardial deformation. The peak systolic velocity, global strain, and peak strain rate of the right ventricle were higher than those of the left ventricle, suggesting that the right ventricle dominates longitudinal systolic movement from the second trimester of pregnancy.

In recent years,neural machine translation has made significant progress in translation quality,but it relies on parallel bilingual sentence pairs heavily during the training process.However,parallel resources are scarce for the e-commerce domain,in addition,cultural differences lead to stylistic differences in product information expression.To solve these two problems,a style-aware unsupervised domain adaptation algorithm is proposed,which makes full use of e-commerce monolingual data in the mutual training method,while introducing quasi knowledge distillation approach to deal with style differences.We construct non-parallel bilingual corpus by obtaining e-commerce product data information,and then carry out experiments based on the aforementioned corpus and Chinese and English news parallel corpus.The results show that the algorithm significantly improves translation qua-lity compared to various unsupervised domain adaptation methods,improves about 5 BLEU points compared with the strongest baseline system.In addition,the algorithm is further extended to Ted,Law and Medical OPUS data,all of which achieve better translation results.