Numerical simulations on fluid dynamics problems primarily rely on spatially or/and temporally discretization of the governing equation using polynomials into a finite-dimensional algebraic system. Due to the multi-scale nature of the physics and sensitivity from meshing a complicated geometry, such process can be computational prohibitive for most realtime applications (e.g., clinical diagnosis and surgery planning) and many-query analyses (e.g., optimization design and uncertainty quantification). Therefore, developing a costeffective surrogate model is of great practical significance. Deep learning (DL) has shown new promises for surrogate modeling due to its capability of handling strong nonlinearity and high dimensionality. However, the off-the-shelf DL architectures, success of which heav-* Corresponding author. of internal flows relevant to hemodynamics applications, and the forward propagation of uncertainties in fluid properties and domain geometry is studied as well. The results show excellent agreement on the flow field and forward-propagated uncertainties between the DL surrogate approximations and the first-principle numerical simulations.

Social recommendation leverages social information to solve data sparsity and cold-start problems in traditional collaborative ltering methods. However, most existing models assume that social e ects from friend users are static and under the forms of constant weights or xed constraints. To relax this strong assumption, in this paper, we propose dual graph a ention networks to collaboratively learn representations for two-fold social e ects, where one is modeled by a user-speci c a ention weight and the other is modeled by a dynamic and context-aware a ention weight. We also extend the social e ects in user domain to item domain, so that information from related items can be leveraged to further alleviate the data sparsity problem. Furthermore, considering that di erent social e ects in two domains could interact with each other and jointly inuence users' preferences for items, we propose a new policy-based fusion strategy based on contextual multi-armed bandit to weigh interactions of various social e ects. Experiments on one benchmark dataset and a commercial dataset verify the e cacy of the key components in our model. e results show that our model achieves great improvement for recommendation accuracy compared with other state-of-the-art social recommendation methods.

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Abstract:The aim of this study is to investigate the effects of leucine (Leu) and histidine (His) on the expression of both the mammalian target of rapamycin (mTOR) signaling pathway-related proteins and caseins in immortalized bovine mammary epithelial cells (CMEC-H), using a single supplement through Western blotting. The Earle's balanced salt solution (EBSS) was set as the control group and other treatment groups, based on the EBSS, were added with different concentrations of Leu or His, respectively. The results showed that, compared with the control group, the expression of caseins and the phosphorylation of mTOR (Ser Similarly, the His supplementation from 0.15 to 9.60 mmol/L increased the expression of αs2-casein, β-casein, κ-casein, P-mTOR (Ser 2481 ), P-Raptor (Ser 792 ), P-S6K1 (Thr 389 ), P-4EBP1 (Thr 37 ), P-eIF4E (Ser 209 ), and P-eEF2 (Thr 56 ) (P<0.01) in CMEC-H, whereas the αs1-casein expression was only reduced at 9.60 mmol/L His, G protein β subunit-like protein (GβL) at 0.15 and 9.60 mmol/L His, and P-RPS6 at 4.80 to 9.60 mmol/L His. Our linear regression model assay suggested that the αs1-casein expression was positively correlated with P-mTOR (P<0.01), P-S6K1 (P<0.01), and P-eEF2 (P<0.01) for the addition of Leu, while the expressions of β-casein (P<0.01) and κ-casein (P<0.01) were positively correlated with P-eEF2 for the addition of His. In conclusion, the milk protein synthesis was up-regulated through activation of the mTOR pathway with the addition of Leu and His in CMEC-H.