On the Computation Power of Name Parameterization in Higher-order Processes

Parameterization extends higher-order processes with the capability of abstraction (akin to that in lambda-calculus), and is known to be able to enhance the expressiveness. This paper focuses on the parameterization of names, i.e. a construct that maps a name to a process, in the higher-order setting. We provide two results concerning its computation capacity. First, name parameterization brings up a complete model, in the sense that it can express an elementary interactive model with built-in recursive functions. Second, we compare name parameterization with the well-known pi-calculus, and provide two encodings between them.Comment: In Proceedings ICE 2015, arXiv:1508.0459

Expression of syndecan-1 in the retina of diabetes

AIM:To observe the expression of syndecan-1 on epiretinal membranes obtained from patients with proliferative diabetic retinopathy and retina of diabetic rats. METHODS:SD rats were given intraperitoneal injection of streptozotocin to induce diabetes. Ink perfusion and stretched retina were used to observe the retinal blood vessels. Immunohistochemistry staining was used to detect syndecan-1 protein in the rat retina and epiretinal membranes of proliferative diabetic retinopathy. RESULTS:In the diabetic rats of 9-week, peripheral retinal blood vessels shaped tortuously, capillary network was significantly reduced, and parts of the capillary perfusion was bad. In the control group, strong positive staining of syndecan-1 was detected in the nerve fiber layer and ganglion cells, moderate positive staining was observed in the inner plexiform layer and outer segments of photoreceptor, and weak positive staining was detected in the outer plexiform layer. In the retina of diabetic rats, syndecan-1 decreased. Moderate positive staining of syndecan-1 was observed in the nerve fiber layer, ganglion cells and outer segments of photoreceptor, weak positive staining was detected in the inner plexiform layer and outer plexiform layer. Among 13 specimens of epiretinal membranes, weak positive staining of syndecan-1 was detected in 8 cases(61.5%)and negative staining was observed in 5 cases(38.5%). CONCLUSION:Syndecan-1 is down-regulated in the retina of diabetic rats and epiretinal membranes of proliferative diabetic retinopathy

Mathematical Modeling of Cytotoxic Lymphocyte-Mediated Immune Response to Hepatitis B Virus Infection

Nowak's model of the human immunodeficiency virus (HIV) infection has been extensively and successfully used to simulate the interaction between HIV and cytotoxic lymphocyte- (CTL-) mediated immune response. However, this model is not available for hepatitis B virus (HBV) infection. As the enhanced recruitment of virus-specific CTLs into the liver has been an important novel concept in the pathogenesis of hepatitis B, we develop a specific mathematical model analyzing the relationship between HBV and the CTL-mediated immune response, and the indicator of the liver cell damage, alanine aminotransferase (ALT). The stability condition of the complete recovery equilibrium point at which HBV will be eliminated entirely from the body is discussed. A different set of parameters is used in the simulation, and the results show that the model can interpret the wide variety of clinical manifestations of HBV infection. The model suggests that a rapid and vigorous CTL response is required for resolution of HBV infection

Safety-aware Semi-end-to-end Coordinated Decision Model for Voltage Regulation in Active Distribution Network

Prediction plays a vital role in the active distribution network voltage regulation under the high penetration of photovoltaics. Current prediction models aim at minimizing individual prediction errors but overlook their collective impacts on downstream decision-making. Hence, this paper proposes a safety-aware semi-end-to-end coordinated decision model to bridge the gap from the downstream voltage regulation to the upstream multiple prediction models in a coordinated differential way. The semi-end-to-end model maps the input features to the optimal var decisions via prediction, decision-making, and decision-evaluating layers. It leverages the neural network and the second-order cone program (SOCP) to formulate the stochastic PV/load predictions and the var decision-making/evaluating separately. Then the var decision quality is evaluated via the weighted sum of the power loss for economy and the voltage violation penalty for safety, denoted by regulation loss. Based on the regulation loss and prediction errors, this paper proposes the hybrid loss and hybrid stochastic gradient descent algorithm to back-propagate the gradients of the hybrid loss with respect to multiple predictions for enhancing decision quality. Case studies verify the effectiveness of the proposed model with lower power loss for economy and lower voltage violation rate for safety awareness