Pharmacotherapy of Patients with Mild Persistent Asthma: Strategies and Unresolved Issues

In studies comparing regular versus on-demand treatment for patients with mild persistent asthma, on-demand treatment seems to have a similar efficacy on clinical and functional outcomes, but it does not suppress chronic airway inflammation or airway hyper-responsiveness (AHR) associated with asthma. Data on the efficacy of a continuous treatment with inhaled corticosteroids (ICS) in preventing the progression of asthma are conflicting. There is the possibility that patients without a regular treatment with ICS may develop a more severe asthma associated with airway structural changes (remodeling) and a progressive loss of lung function. However, the possible clinical and functional consequences of persistent, not controlled, airway inflammation in patients with asthma have to be established. Assessment of asthma control should include inflammatory outcomes, such as fraction of exhaled nitric oxide and sputum eosinophil counts. Until the relationships between symptoms, lung function tests, AHR, airway inflammation, exacerbations, and airway remodeling are clarified, regular treatment seems to be generally more appropriate than on-demand treatment to warrant a greater control of asthma. Select subgroups of patients with mild asthma who are well controlled by regular treatment might adopt the on-demand treatment plan as an intermediate step toward the suspension of controller medication. The increasing evidence for heterogeneity of asthma, the growing emphasis on asthma subphenotypes, including molecular phenotypes identified by omics technologies, and their possible implications for different asthma severity and progression and therapeutic response, are changing the paradigm of treating patients with asthma only based on classification of their disease severity to a pharmacological strategy more focused on the individual asthmatic patient. Pharmacological treatment of asthma is going toward a personalized approach

New and Emerging Topics in Computing

This installment of Computer’s series highlighting the work published in IEEE Computer Society journals comes from IEEE Transactions on Emerging Topics in Computing

An algorithmic and architectural study on Montgomery exponentiation in RNS

The modular exponentiation on large numbers is computationally intensive. An effective way for performing this operation consists in using Montgomery exponentiation in the Residue Number System (RNS). This paper presents an algorithmic and architectural study of such exponentiation approach. From the algorithmic point of view, new and state-of-the-art opportunities that come from the reorganization of operations and precomputations are considered. From the architectural perspective, the design opportunities offered by well-known computer arithmetic techniques are studied, with the aim of developing an efficient arithmetic cell architecture. Furthermore, since the use of efficient RNS bases with a low Hamming weight are being considered with ever more interest, four additional cell architectures specifically tailored to these bases are developed and the tradeoff between benefits and drawbacks is carefully explored. An overall comparison among all the considered algorithmic approaches and cell architectures is presented, with the aim of providing the reader with an extensive overview of the Montgomery exponentiation opportunities in RNS

Is immersive virtual reality the ultimate interface for 3D animators?

Creating computer animations is a labor-intensive task. Existing virtual reality (VR)-based animation solutions offer only heterogeneous subsets of traditional tools' functionalities. We present an add-on for the Blender animation suite that enables users to switch between native and immersive VR-based interfaces and employ the latter to perform a representative set of tasks

Mixed marker-based/marker-less visual odometry system for mobile robots

When moving in generic indoor environments, robotic platforms generally rely solely on information provided by onboard sensors to determine their position and orientation. However, the lack of absolute references often leads to the introduction of severe drifts in estimates computed, making autonomous operations really hard to accomplish. This paper proposes a solution to alleviate the impact of the above issues by combining two vision‐based pose estimation techniques working on relative and absolute coordinate systems, respectively. In particular, the unknown ground features in the images that are captured by the vertical camera of a mobile platform are processed by a vision‐based odometry algorithm, which is capable of estimating the relative frame‐to‐frame movements. Then, errors accumulated in the above step are corrected using artificial markers displaced at known positions in the environment. The markers are framed from time to time, which allows the robot to maintain the drifts bounded by additionally providing it with the navigation commands needed for autonomous flight. Accuracy and robustness of the designed technique are demonstrated using an off‐the‐shelf quadrotor via extensive experimental test

Thinking BigData: Motivation, Results and a Few Recipes for a Balanced Growth of High Performance Computing in Academia

Big Data is today both an emerging research area and a real present and future demand. High Performance Computing (HPC) Centers cannot neglect this fact and have to be reshaped to fulfill this need. In this paper we share our experience of upgrading a HPC Center at Politecnico di Torino, originally designed and deployed in 2010. We believe that this issue could be common to some other existing "general purpose" HPC centers where, at least in the short term, the possibility to start from scratch a new Big Data HPC center cannot be afforded but a balanced upgrade of the existing system has to be preferre