Vertebrate glycoproteins and glycolipids are synthesized in complex biosynthetic pathways localized predominantly within membrane compartments of the secretory pathway. The enzymes that catalyze these reactions are exquisitely specific, yet few have been extensively characterized due to challenges associated with their recombinant expression as functional products. We used a modular approach to create an expression vector library encoding all known human glycosyltransferases, glycoside hydrolases, sulfotransferases, and other glycan modifying enzymes. We then expressed the enzymes as secreted catalytic domain fusion proteins in mammalian and insect cell hosts, purified and characterized a subset of the enzymes, and determined the structure of one, the sialyltransferase ST6GALNAC2. Many enzymes were produced at high yields and similar levels in both hosts, but individual protein expression levels varied widely. This expression vector library will be a transformative resource for recombinant enzyme production, broadly enabling structure-function studies and expanding applications of these enzymes in glycochemistry and glycobiology.

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Wireless Sensor Networks (WSNs) consist of hundreds or thousands of sensor nodes with limited processing, storage, and battery capabilities. There are several strategies to reduce the power consumption of WSN nodes (by increasing the network lifetime) and increase the reliability of the network (by improving the WSN Quality of Service). However, there is an inherent conflict between power consumption and reliability: an increase in reliability usually leads to an increase in power consumption. For example, routing algorithms can send the same packet though different paths (multipath strategy), which it is important for reliability, but they significantly increase the WSN power consumption. In this context, this paper proposes a model for evaluating the reliability of WSNs considering the battery level as a key factor. Moreover, this model is based on routing algorithms used by WSNs. In order to evaluate the proposed models, three scenarios were considered to show the impact of the power consumption on the reliability of WSNs.

The miniaturization of hardware components has lead to the development of Wireless Sensor Networks (WSN) and networked-applications over them. Meanwhile, middleware systems have also been proposed in order to both facilitating the development of these applications and providing common application services. The development of middleware for sensor networks, however, places new challenges to middleware developers due to the low availability of resources and processing capacity of the sensor nodes. In this context, this paper presents a middleware for WSN named Mires. Mires incorporates characteristics of message-oriented middleware by allowing applications communicate in a publish/subscribe way. In order to illustrate the proposed middleware, we implement an aggregation middleware service for an environment-monitoring application.

Power consumption is the main concern in developing Wireless Sensor Network (WSN) applications. Consequently, several strategies have been proposed for investigating the power consumption of this kind of application. These strategies can help to predict the WSN lifetime, provide recommendations to application developers and may optimize the energy consumed by the WSN applications. While measurement is a known and precise strategy for power consumption evaluation, it is very costly, tedious and may be unfeasible considering the (usual) large number of WSN nodes. Furthermore, due to the inherent dynamism of WSNs, the instrumentation required by measurement techniques makes difficult their use in several different scenarios. In this context, this paper presents an approach for evaluating the power consumption of WSN applications by using simulation models along with a set of tools to automate the proposed approach. Starting from a programming language code, we automatically generate consumption models used to predict the power consumption of WSN applications. In order to evaluate the proposed approach, we compare the results obtained by using the generated models against ones obtained by measurement.