Computational Materials Design for Molecular Machinery: From Nanoporous Crystals to Nanoscale Racecars

Over billions of years of evolution, Nature mastered molecular nanotechology, manipulating atoms and molecules with high precision. Among them are machines that perform tasks such as protein synthesis (ribosomes), gene replication (DNA and RNA polymerases), transporting molecular cargo (kinesin and dynein), and locomotion (flagella). Today these machines serve as a source of inspiration for the design of artificial molecular machines (AMMs). We are now exploring molecular motors, actuators, and logic gates at the nanoscale just as we did at the macroscale in the 19th century with electric motors and combustion engines. Inspired by the pursuit of AMMs, this dissertation describes my research on developing computational methods to aid in the design of AMMs with targeted geometry and functionality. We first focused on the design of nanoporous crystals, developing a novel algorithm that can test whether two given crystalline structures can interpenetrate each other. Using this algorithm, we screened a database of ~6000 metal-organic frameworks (MOFs) and identified 18 hetero-interpenetrating MOF candidates. We then found that interpenetration enhances thermal conductivity which is important for various applications such as adsorbent gas storage. Later, we developed tools to study nanoscale racecars, which are large organic molecules (~200-2000 Da) designed to diffuse quickly on atomically smooth surfaces. Here we developed both computational strategies to study their surface diffusion and tools to rapidly build hypothetical nanocars and assess their surface diffusion performance. We found that the surface diffusion gets slower with higher molecular weight and stronger molecule-surface interaction energy. We also suggested a geometric parameter, i.e. elevation weighted density, which we found to be useful for quickly ranking diffusion of different molecular designs. Our study suggests that by careful design of the molecular structure and selection of the appropriate surface, molecular diffusion can be tailored. In summary, we show that by developing tools and using appropriate methods we can design and study properties of both static and dynamic molecular machines. We hope that these studies, and the tools developed, will collectively help to push the frontier of knowledge (even if incrementally) towards the eventual building of useful AMMs

Industrial Policy and Technology Diffusion: Evidence from Paper Making Machinery in Indonesia

technology diffusion, Indonesia

Diffusion Component Analysis: Unraveling Functional Topology in Biological Networks

Complex biological systems have been successfully modeled by biochemical and genetic interaction networks, typically gathered from high-throughput (HTP) data. These networks can be used to infer functional relationships between genes or proteins. Using the intuition that the topological role of a gene in a network relates to its biological function, local or diffusion based "guilt-by-association" and graph-theoretic methods have had success in inferring gene functions. Here we seek to improve function prediction by integrating diffusion-based methods with a novel dimensionality reduction technique to overcome the incomplete and noisy nature of network data. In this paper, we introduce diffusion component analysis (DCA), a framework that plugs in a diffusion model and learns a low-dimensional vector representation of each node to encode the topological properties of a network. As a proof of concept, we demonstrate DCA's substantial improvement over state-of-the-art diffusion-based approaches in predicting protein function from molecular interaction networks. Moreover, our DCA framework can integrate multiple networks from heterogeneous sources, consisting of genomic information, biochemical experiments and other resources, to even further improve function prediction. Yet another layer of performance gain is achieved by integrating the DCA framework with support vector machines that take our node vector representations as features. Overall, our DCA framework provides a novel representation of nodes in a network that can be used as a plug-in architecture to other machine learning algorithms to decipher topological properties of and obtain novel insights into interactomes.Comment: RECOMB 201

Intelligent systems for monitoring and preventing in healthcare information systems

Nowadays the interoperability in Healthcare Information Systems (HIS) is a fundamental requirement. The Agency for Integration, Diffusion and Archive of Medical Information (AIDA) is an interoperability healthcare platform that ensures these demands and it is implemented in Centro Hospitalar do Porto (CHP), a major healthcare unit in Portugal. Therefore, the overall performance of CHP HIS depends on the success of AIDA functioning. This paper presents monitoring and prevention systems implemented in the CHP, which aim to improve the system integrity and high availability. These systems allow the monitoring and the detection of situations conducive to failure in the AIDA main components: database, machines and intelligent agents. Through the monitoring systems, it was found that the database most critical period is between 11:00 and 12:00 and the resources are well balanced. The prevention systems detected abnormal situations that were reported to the administrators that took preventive actions, avoiding damage to AIDA workflow

Classifying Network Data with Deep Kernel Machines

Inspired by a growing interest in analyzing network data, we study the problem of node classification on graphs, focusing on approaches based on kernel machines. Conventionally, kernel machines are linear classifiers in the implicit feature space. We argue that linear classification in the feature space of kernels commonly used for graphs is often not enough to produce good results. When this is the case, one naturally considers nonlinear classifiers in the feature space. We show that repeating this process produces something we call "deep kernel machines." We provide some examples where deep kernel machines can make a big difference in classification performance, and point out some connections to various recent literature on deep architectures in artificial intelligence and machine learning

Parallelizing Windowed Stream Joins in a Shared-Nothing Cluster

The availability of large number of processing nodes in a parallel and distributed computing environment enables sophisticated real time processing over high speed data streams, as required by many emerging applications. Sliding window stream joins are among the most important operators in a stream processing system. In this paper, we consider the issue of parallelizing a sliding window stream join operator over a shared nothing cluster. We propose a framework, based on fixed or predefined communication pattern, to distribute the join processing loads over the shared-nothing cluster. We consider various overheads while scaling over a large number of nodes, and propose solution methodologies to cope with the issues. We implement the algorithm over a cluster using a message passing system, and present the experimental results showing the effectiveness of the join processing algorithm.Comment: 11 page

The evolution of retail banking services in United Kingdom: a retrospective analysis

The purpose of this paper is to assess the sequence of technological changes occurred in the retail banking sector of the United Kingdom against the emergence of customer services by developing an evolutionary argument. The historical paradigm of Information Technology provides useful insights into the ‘learning opportunities’ that opened the way to endogenous changes in the banking activity such as the reconfiguration of its organizational structure and the diversification of the product line. The central idea of this paper is that innovation never occurs without simultaneous structural change. Thus, a defining property of the banking activity is the diachronic adaptation of formal and informal practices to an evolving technological dimension reflecting the extent to which the diffusion of innovation (re)generates variety of micro level processes and induces industry evolution.Information Technology; Retail Banking; History of Technology; Innovation Systems.