This study shows that the TNF-α inhibitor infliximab induces anti-allergic effects by decreasing local and systemic Th2 cytokine (IL-4) production, total and OVA-specific IgE levels, adhesion molecule (E-selectin) expression, and eosinophil infiltration into the nasal mucosa in an allergic rhinitis model. Therefore, infliximab should be considered as a potential agent in treating allergic rhinitis.

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Current planning and design decision support systems show limitations in the integration of design, science, and computation. Planning support systems with manual design and postdesign evaluations impose major challenges in exploring huge design spaces. Generative design systems largely neglect the wicked nature of design problems and lack appropriate representation methods and simulation tools at the urban scale. To tackle those challenges, this research developed a Smart Design framework featuring urban design decision-making reinforced by artificial intelligence-aided design (AIAD). The Smart Design framework treats urban design as an emergent pattern formation processes with contextualized and dynamic objectives. The framework integrates design thinking, advanced artificial intelligence search techniques (e.g. genetic algorithms), urban scale performance simulations, and participation to better inform decisionmaking. Through four major stages, the framework combines the ideas of Science for Design and Design in Science. The significance and potential of the Smart Design framework are demonstrated in an urban design study of Gangnam superblocks in Seoul, South Korea. The study explores sustainable urban forms in the high-density, super-complex, and hyper-consumptive environment of Gangnam, which can also be found in many other Asian contexts. The case study illustrates how the framework identifies design solutions for sustainable city development in the process of participatory decision-making through the co-evolution of design problems and solutions.

There is a lack of building energy modeling in current planning support systems (PSS) while building energy efficiency is getting greater attention. This is due to the current limitations of energy modeling at the urban scale and the inconsistency between the available urban data and that required for modeling. The chapter seeks to fill this gap by developing a GIS-based urban building energy modeling system, using the Urban-EPC simulation engine, a modified Energy Performance Calculator engine. This modeling system is compatible with other planning tools, enhanced by the combination of physical and statistical modeling, and adjustable in its resolution, speed and accuracy. Through processing the Data Preparation, Pre-Simulation, Main Simulation and Visualization and Analysis models in this energy modeling system, the urban data related to the basic building information, mutual shading, microclimate and occupant behavior are collected, modified, and synthesized in the GIS platform and then used as the input of the Urban-EPC engine to get energy use of every building in a city, which could be further visualized and analyzed. The method is applied in Manhattan to show its potential as an important component in PSS to inform urban energy policy making.

Many cities around the world have reached a critical situation when it comes to energy and water supply, threatening the urban sustainable development. From an engineering and architecture perspective it is mandatory to design cities taking into account energy and water 1 The short version of the paper was presented at CUE2015 on Nov. 15-17, Fuzhou, China. This paper is a substantial extension of the short version with original paper title, "Spatial optimization of residential urban district-Energy and water perspectives" and Paper No. 167.

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