Novel agents for anti-platelet therapy

Anti-platelet therapy plays an important role in the treatment of patients with thrombotic diseases. The most commonly used anti-platelet drugs, namely, aspirin, ticlopidine, and clopidogrel, are effective in the prevention and treatment of cardio-cerebrovascular diseases. Glycoprotein IIb/IIIa antagonists (e.g., abciximab, eptifibatide and tirofiban) have demonstrated good clinical benefits and safety profiles in decreasing ischemic events in acute coronary syndrome. However, adverse events related to thrombosis or bleeding have been reported in cases of therapy with glycoprotein IIb/IIIa antagonists. Cilostazol is an anti-platelet agent used in the treatment of patients with peripheral ischemia, such as intermittent claudication. Presently, platelet adenosine diphosphate P2Y(12) receptor antagonists (e.g., clopidogrel, prasugrel, cangrelor, and ticagrelor) are being used in clinical settings for their pronounced protective effects. The new protease-activated receptor antagonists, vorapaxar and atopaxar, potentially decrease the risk of ischemic events without significantly increasing the rate of bleeding. Some other new anti-platelet drugs undergoing clinical trials have also been introduced. Indeed, the number of new anti-platelet drugs is increasing. Consequently, the efficacy of these anti-platelet agents in actual patients warrants scrutiny, especially in terms of the hemorrhagic risks. Hopefully, new selective platelet inhibitors with high anti-thrombotic efficiencies and low hemorrhagic side effects can be developed

Data-driven analysis on the subbase strain prediction:a deep data augmentation-based study

The service quality of the subbase may affect the overall road performance during its service life. Thus, monitoring and prediction of subbase strain development are of great importance for civil engineers. In this paper, a method based on the time-series augmentation was employed to predict the subbase strain development. The time-series generative adversarial network (TimeGAN) model was implemented to perform the augmentation of time-series data based on the original monitored data. The augmented data was trained through deep learning network to learn the feature correlation of the subbase strain. The effectiveness of TimeGAN on the prediction accuracy was evaluated through the Attention-Sequence to Sequence (Attention-Seq2seq) model, and temporal convolution network-adaptively parametric rectifier linear units (TCN-APReLU) model. Results indicated that the TimeGAN network could capture sufficient information from the time-series monitored data of subbase strain development so that the corresponding augmented data matches well with the original data, which improves the prediction accuracy. It is also discovered that the combination of TimeGAN and TCN-APReLU appropriately predict the subbase strain development based on the original monitored data

On the operators which do not belongs to FB2(Ω)FB_2(\Omega)

In this paper, a subclass of Cowen-Douglas operators of rank 2 case is introduced. Any unitarily intertwining between operators in this class would not be diagonal operator matrix. The unitarily classification theorem is given. As applications, we give a sufficient condition for the similarity of operators in $B_1(\Omega)$ involving the curvatures of their dilations in $B_2(\Omega)$.Comment: 15page

A kind of helicoidal surfaces in 3-dimensional Minkowski space

AbstractIn this paper we constructed a helicoidal surface with a light-like axis with prescribed mean curvature or Gauss curvature given by smooth function in 3-dimensional Minkowski space E13 and solved an open problem left by Beneki, Kaimakamis, and Papantoniou in [J. Math. Anal. Appl. 275 (2002) 586–614]