Selection of catalytically active biotin ligase and trypsin mutants by phage display

Phage display has been shown to facilitate greatly the selectionof polypeptides with desired properties by establishing a directlink between the polypeptide and the gene that encodes it. However,selection for catalytic activities displayed on phage remainsa challenge, since reaction products diffuse away from the enzymeand make it difficult to recover catalytically active phage–enzymes.We have recently described a selection methodology in whichthe reaction substrate (and eventually the reaction product)is anchored on calmodulin-tagged phage–enzymes by meansof a calmodulin binding peptide. Phage displaying a catalyticactivity are physically isolated by means of affinity reagentsspecific for the product of reaction. In this study, we investigatedthe efficiency of selection for catalysis by phage display,using a ligase (the Escherichia coli biotin ligase BirA) andan endopeptidase (the rat trypsin His57     

Web Filtering: Where,How and Why: Control of Internet use: some considerations about the implications of this type of control in the light of the Italian experience.

A recent study¹ has determined that the average amount of time users spend on the Internet at their workstations is on the order of five and a half hours per week. Italy is in second place after the United Kingdom in use of the Internet from the workplace. When this type of access is available there is also the potential for its abuse. And so it was found that, out of a sample of 200 interviewees, 51% admitted to browsing sites not pertinent to their business during work hours, and, this is how three of the five and half hours are spent. Where exactly? The type of websites visited when one should be working vary from leisure time activities, to shopping, to pornography.So how do companies respond to this problem? In Italy, 55% of the sample admit that they have not yet undertaken any definite actions with regard to this issue, 17% have implemented policies, 7% carry out active system monitoring and 14% use content and surfing filters.     

Biomimetic flexible/compliant sensors for a soft-body lamprey-like robot

This paper focuses on the development of biomimetic sensory abilities for an undulatory soft-body lamprey-like robot, which has been designed to replicate the locomotion mechanisms of living lamprey in order to address useful applications wherever locomotion in unstructured environments is required. The compliant sensory elements are piezo-resistive and made from compliant material called a quantum tunneling composite by mixing micro/nano carbon black particles into a highly soft silicone rubber matrix. The relationship of the composite strip between piezo-resistivity and mechanical strain is investigated by a universal strain–stress tensiometer through resistivity monitoring. Increasing and decreasing resitivity both appears during elongation and retraction, suggesting that piezo-resistivity in the composite material is governed by both percolation theory and quantum tunneling effects. Dynamic tests on the bench shows that the compliant stretch receptor is able to follow the stimulations under fairly wide frequency ranges, exceeding initial estimations. In addition, the compliant artificial stretch receptor has enough sensitivity to detect the local actuations and the actions generated by neighboring actuators in the segmented robotic structure. Finally, the experiments on biomimetic cilia-based cupula receptor show that a low cost flexible/compliant biomimetic sensor is able to detect medium fluidic speeds ranging from 0.05 m/s to 0.6 m/s.     

Proactive thermal management in green datacenters

The increasing demand for faster computing and high storage capacity has resulted in an increase in energy consumption and heat generation in datacenters. Because of the increase in heat generation, cooling requirements have become a critical concern, both in terms of growing operating costs as well as their environmental and societal impacts. Presently, thermal management techniques make an effort to thermally profile and control datacenters’ cooling equipment to increase their efficiency. In conventional thermal management techniques, cooling systems are triggered by the temperature crossing predefined thresholds. Such reactive approaches result in delayed response as the temperature may already be too high, which can result in performance degradation of hardware.     

Learning and Evolution

In the last few years several researchers have resorted to artificial evolution (e.g., genetic algorithms) and learning techniques (e.g., neural networks) for studying the interaction between learning and evolution. These studies have been conducted for two different purposes: (a) looking at the performance advantages obtained by combining these two adaptive techniques; (b) understanding the role of the interaction between learning and evolution in biological organisms. In this paper we describe some of the most representative experiments conducted in this area and point out their implications for both perspectives outlined above. Understanding the interaction between learning and evolution is probably one of the best examples in which computational studies have shed light on problems that are difficult to study with the research tools employed by evolutionary biology and biology in general. From an engineering point of view, the most relevant results are those showing that adaptation in dynamic environments gains a significant advantage by the combination of evolution and learning. These studies also show that the interaction between learning and evolution deeply alters the evolutionary and the learning process themselves, offering new perspectives from a biological point of view. The study of learning within an evolutionary perspective is still in its infancy and in the forthcoming years it will produce an enormous impact on our understanding of how learning and evolution operate.     

Autoregressive model-based fuzzy clustering and its application for detecting information redundancy in air pollution monitoring networks

Fuzzy clustering enables the simultaneous membership of objects in two or more clusters. This is particularly pertinent where time series are concerned, because very often patterns of time series change over time. Thus, a time series might belong to different clusters over different periods of time, in which case, crisp clustering is unable to capture this multi-cluster membership. In this paper, we adopt a Fuzzy C-Medoids approach to clustering time series based on autoregressive estimates of models fitted to the time series. We illustrate very good performance of this approach in a range of simulation studies. By means of two applications, we also show the usefulness of this clustering approach in the air pollution monitoring, by considering air pollution time series, i.e., CO time series, CO₂ time series and NO time series monitored on world and urban scales. In particular, we show that, by considering in the clustering process, the autoregressive representation of these air pollution time series, we are able to detect possible information redundancy in the monitoring networks and then, decreasing the number of monitoring stations, to reduce the monitoring costs and then to increase the monitoring efficiency of the networks.     

MATTER: A tool for generating end-to-end IoT test scripts

Software Quality Journal - In the last few years, Internet of Things (IoT) systems have drastically increased their relevance in many fundamental sectors. For this reason, assuring their quality is...