Aptamer biosensors

Aptamers are single stranded nucleic acids, typically composed of between twenty to eighty nucleotides in length, capable of binding selectively to non-nucleic acid ligands. Aptamers are selected through a combinatorial chemistry process called Systematic Evolution of Ligands by Exponential enrichment (SELEX), which is composed of successive cycles of selection based on target affinity, followed by amplification. This results in the Darwinian evolution of the nucleic acid library resulting in increasing library homogeneity and target affinity over time. Aptamers have been extensively investigated for potential application as sensing molecules, with roles similar to those traditionally occupied by antibodies. Aptamers and monoclonal antibodies have similar sensitivity in the pico to micro molar range. However aptamers have a number of advantages over protein antibodies, such as greater thermal stability, ease of chemical amplification, and amenability to modification, especially at the 5’ and 3’ prime ends. The work performed in this Thesis is divided into three categories. The first section describes the development of voltammetric Kanamycin and Tetracycline biosensors based on electrode immobilized, redox label bioconjugated nucleotide molecular beacons. These sensors relies on the target-aptamer binding induced spatial displacement of the redox label towards or away from the electrode surface as a means of signal generation. Further study was conducted to test the feasibility of this sensor design under likely field operation environments such as in soil sample analysis for microbial product discovery and in agricultural effluence for regulatory purposes. The biosensor was also enhanced by gel encapsulation for defense against nuclease degradation. Negative control was performed against structurally similar antibiotics of the same family in order to prove the specificity of the biosensor. Lastly, the sensor was moved onto an automated platform in a multichannel format in order to improve the utility of the sensor. The second section describes the development of a voltammetric biosensor based on Enzyme-Linked Oligonucleotide Assay (ELONA) technology. Two sub-types of ELONA-like biosensors were originally envisioned, based respectively on direct and indirect ELONA. Both sub-types depend on the mass of redox label rich Gold Nanoparticles (GNP) at the electrode surface as a means of signal generation. Negative controls was performed against globular proteins Bovine Serum Albumin and Lysozyme, the former since it is the most ubiquitous protein component of serum (the most likely biosensor operational environment), the latter as a worst case scenario for non-specific false positive results due to its positive charge. The last section describes an attempt to develop an automated SELEX device based on mesofluidic flow channels. It was hoped that by using flow channels of a millimeter scale it would be possible to retain both the advantages of the conventional auto sampler based SELEX protocols (large library and sequence variation), while also gaining the primary advantages of microfluidic SELEX (reduced contamination risk, low initial cost and maintenance). Essential components of the SELEX device, such as thermal cycler, liquid handling, electronics infrastructure, and software control were designed, tested and integrated. Lastly an attempt was made to perform automated SELEX against Lysozyme targets using the device, though no nucleic acid with high affinity to target had yet been successfully isolated by the end of this study.Open Acces

An efficient algorithm to perform local concerted movements of a chain molecule

The devising of efficient concerted rotation moves that modify only selected local portions of chain molecules is a long studied problem. Possible applications range from speeding the uncorrelated sampling of polymeric dense systems to loop reconstruction and structure refinement in protein modeling. Here, we propose and validate, on a few pedagogical examples, a novel numerical strategy that generalizes the notion of concerted rotation. The usage of the Denavit-Hartenberg parameters for chain description allows all possible choices for the subset of degrees of freedom to be modified in the move. They can be arbitrarily distributed along the chain and can be distanced between consecutive monomers as well. The efficiency of the methodology capitalizes on the inherent geometrical structure of the manifold defined by all chain configurations compatible with the fixed degrees of freedom. The chain portion to be moved is first opened along a direction chosen in the tangent space to the manifold, and then closed in the orthogonal space. As a consequence, in Monte Carlo simulations detailed balance is easily enforced without the need of using Jacobian reweighting. Moreover, the relative fluctuations of the degrees of freedom involved in the move can be easily tuned. We show different applications: the manifold of possible configurations is explored in a very efficient way for a protein fragment and for a cyclic molecule; the "local backbone volume", related to the volume spanned by the manifold, reproduces the mobility profile of all-α helical proteins; the refinement of small protein fragments with different secondary structures is addressed. The presented results suggest our methodology as a valuable exploration and sampling tool in the context of bio-molecular simulations

Computational Methods for Cognitive and Cooperative Robotics

In the last decades design methods in control engineering made substantial progress in the areas of robotics and computer animation. Nowadays these methods incorporate the newest developments in machine learning and artificial intelligence. But the problems of flexible and online-adaptive combinations of motor behaviors remain challenging for human-like animations and for humanoid robotics. In this context, biologically-motivated methods for the analysis and re-synthesis of human motor programs provide new insights in and models for the anticipatory motion synthesis. This thesis presents the author’s achievements in the areas of cognitive and developmental robotics, cooperative and humanoid robotics and intelligent and machine learning methods in computer graphics. The first part of the thesis in the chapter “Goal-directed Imitation for Robots” considers imitation learning in cognitive and developmental robotics. The work presented here details the author’s progress in the development of hierarchical motion recognition and planning inspired by recent discoveries of the functions of mirror-neuron cortical circuits in primates. The overall architecture is capable of ‘learning for imitation’ and ‘learning by imitation’. The complete system includes a low-level real-time capable path planning subsystem for obstacle avoidance during arm reaching. The learning-based path planning subsystem is universal for all types of anthropomorphic robot arms, and is capable of knowledge transfer at the level of individual motor acts. Next, the problems of learning and synthesis of motor synergies, the spatial and spatio-temporal combinations of motor features in sequential multi-action behavior, and the problems of task-related action transitions are considered in the second part of the thesis “Kinematic Motion Synthesis for Computer Graphics and Robotics”. In this part, a new approach of modeling complex full-body human actions by mixtures of time-shift invariant motor primitives in presented. The online-capable full-body motion generation architecture based on dynamic movement primitives driving the time-shift invariant motor synergies was implemented as an online-reactive adaptive motion synthesis for computer graphics and robotics applications. The last chapter of the thesis entitled “Contraction Theory and Self-organized Scenarios in Computer Graphics and Robotics” is dedicated to optimal control strategies in multi-agent scenarios of large crowds of agents expressing highly nonlinear behaviors. This last part presents new mathematical tools for stability analysis and synthesis of multi-agent cooperative scenarios.In den letzten Jahrzehnten hat die Forschung in den Bereichen der Steuerung und Regelung komplexer Systeme erhebliche Fortschritte gemacht, insbesondere in den Bereichen Robotik und Computeranimation. Die Entwicklung solcher Systeme verwendet heutzutage neueste Methoden und Entwicklungen im Bereich des maschinellen Lernens und der künstlichen Intelligenz. Die flexible und echtzeitfähige Kombination von motorischen Verhaltensweisen ist eine wesentliche Herausforderung für die Generierung menschenähnlicher Animationen und in der humanoiden Robotik. In diesem Zusammenhang liefern biologisch motivierte Methoden zur Analyse und Resynthese menschlicher motorischer Programme neue Erkenntnisse und Modelle für die antizipatorische Bewegungssynthese. Diese Dissertation präsentiert die Ergebnisse der Arbeiten des Autors im Gebiet der kognitiven und Entwicklungsrobotik, kooperativer und humanoider Robotersysteme sowie intelligenter und maschineller Lernmethoden in der Computergrafik. Der erste Teil der Dissertation im Kapitel “Zielgerichtete Nachahmung für Roboter” behandelt das Imitationslernen in der kognitiven und Entwicklungsrobotik. Die vorgestellten Arbeiten beschreiben neue Methoden für die hierarchische Bewegungserkennung und -planung, die durch Erkenntnisse zur Funktion der kortikalen Spiegelneuronen-Schaltkreise bei Primaten inspiriert wurden. Die entwickelte Architektur ist in der Lage, ‘durch Imitation zu lernen’ und ‘zu lernen zu imitieren’. Das komplette entwickelte System enthält ein echtzeitfähiges Pfadplanungssubsystem zur Hindernisvermeidung während der Durchführung von Armbewegungen. Das lernbasierte Pfadplanungssubsystem ist universell und für alle Arten von anthropomorphen Roboterarmen in der Lage, Wissen auf der Ebene einzelner motorischer Handlungen zu übertragen. Im zweiten Teil der Arbeit “Kinematische Bewegungssynthese für Computergrafik und Robotik” werden die Probleme des Lernens und der Synthese motorischer Synergien, d.h. von räumlichen und räumlich-zeitlichen Kombinationen motorischer Bewegungselemente bei Bewegungssequenzen und bei aufgabenbezogenen Handlungs übergängen behandelt. Es wird ein neuer Ansatz zur Modellierung komplexer menschlicher Ganzkörperaktionen durch Mischungen von zeitverschiebungsinvarianten Motorprimitiven vorgestellt. Zudem wurde ein online-fähiger Synthesealgorithmus für Ganzköperbewegungen entwickelt, der auf dynamischen Bewegungsprimitiven basiert, die wiederum auf der Basis der gelernten verschiebungsinvarianten Primitive konstruiert werden. Dieser Algorithmus wurde für verschiedene Probleme der Bewegungssynthese für die Computergrafik- und Roboteranwendungen implementiert. Das letzte Kapitel der Dissertation mit dem Titel “Kontraktionstheorie und selbstorganisierte Szenarien in der Computergrafik und Robotik” widmet sich optimalen Kontrollstrategien in Multi-Agenten-Szenarien, wobei die Agenten durch eine hochgradig nichtlineare Kinematik gekennzeichnet sind. Dieser letzte Teil präsentiert neue mathematische Werkzeuge für die Stabilitätsanalyse und Synthese von kooperativen Multi-Agenten-Szenarien

Ineslatic analysis of geometrically exact rods

In this work, a formulation for rod structures, able to consider coupled geometric and constitutive sources of nonlinearity in both the static and the dynamic range, is developed. It is extended for allowing the inclusion of passive energy dissipating elements as a special rod element and geometric irregularities as a full three-dimensional body connected to the framed structure by means of a two-scale model. The proposed formulation is based on the Reissner-Simo geometrically exact formulation for rods considering an initially curved reference configuration and extended to include arbitrary distribution of composite materials in the cross sections. Each material point of the cross section is assumed to be composed of several simple materials with their own thermodynamically consistent constitutive laws. The simple mixing rule is used for treating the resulting composite. Cross sections are meshed into a grid of quadrilaterals, each of them corresponding to a fiber directed along the axis of the beam. A mesh independent response is obtained by means of the regularization of the energy dissipated at constitutive level considering the characteristic length of the mesh and the fracture energy of the materials. Local and global damage indices have been developed based on the ratio between the visco-elastic and nonlinear stresses. The consistent linearization of the weak form of the momentum balance equations is performed considering the effects of rate dependent inelasticity. Due to the fact that the deformation map belongs to a nonlinear manifold, an appropriated version of Newmark's scheme and of the iterative updating procedure of the involved variables is developed. The space discretization of the linearized problem is performed using the standard Galerkin finite element approach. A Newton-Raphson type of iterative scheme is used for the step-by-step solution of the discrete problem. A specific element for energy dissipating devices is developed, based on the rod model but releasing the rotational degrees of freedom. Appropriated constitutive relations are given for a wide variety of possible dissipative mechanisms. Several numerical examples have been included for the validation of the proposed formulation. The examples include elastic and inelastic finite deformation response of framed structures with initially straight and curved beams. Comparisons with existing literature is performed for the case of plasticity and new results are presented for degrading and composite materials. Those examples show how the present formulation is able to capture different complex mechanical phenomena such as the uncoupling of the dynamic response from resonance due to inelastic incursions and suppression of the high frequency content. The study of realistic flexible pre-cast and cast in place reinforced concrete framed structures subjected to static and dynamic actions is also carried out. Detailed studies regarding to the evolution of local damage indices, energy dissipation and ductility demands are presented. The studies include the seismic response of concrete structures with energy dissipating devices. Advantages of the use of passive control are verified

Manifold-Based Sensorimotor Representations for Bootstrapping of Mobile Agents

Subject of this thesis is the development of a domain-independent algorithm that allows an autonomous system to process sequences of the sensorimotor interaction with its environment and to assign a geometric interpretation to its motor capabilities. We utilize Lie groups, smooth manifolds endowed with a group structure, that allow for an elegant representation of geometric operations as a central foundation for such a sensorimotor representation. Expressing motor controls with respect to the manifold structure allows us to transform the sensorimotor interaction sequence into a specific set of data points. Finding a manifold and a transformation that minimizes an intrinsic conflict function corresponds to finding a topological structure that is the best fit for expressing the sensorimotor space the entity resides in. Experiments in a virtual environment are conducted that show the applicability of the approach with respect to different sensor and motor configurations

Development of Alternative Methods for Robot Kinematics

The problem of finding mathematical tools to represent rigid body motions in space has long been on the agenda of physicists and mathematicians and is considered to be a well-researched and well-understood problem. Robotics, computer vision, graphics, and other engineering disciplines require concise and efficient means of representing and applying generalized coordinate transformations in three dimensions. Robotics requires systematic ways to represent the relative position or orientation of a manipulator rigid links and objects. However, with the advent of high-speed computers and their application to the generation of animated graphical images and control of robot manipulators, new interest arose in identifying compact and computationally efficient representations of spatial transformations. The traditional methods for representing forward kinematics of manipulators have been the homogeneous matrix in line with the D-H algorithm. In robotics, this matrix is used to describe one coordinate system with respect to another one. However for online operation and manipulation of the robotic manipulator in a flexible manner the computational time plays an important role. Although this method is used extensively in kinematic analysis but it is relatively neglected in practical robotic systems due to some complications in dealing with the problem of orientation representation. On the other hand, such matrices are highly redundant to represent six independent degrees of freedom. This redundancy can introduce numerical problems in calculations, wastes storage, and often increases the computational cost of algorithms. Keeping these drawbacks in mind, alternative methods are being sought by various researchers for representing the same and reducing the computational time to make the system fast responsive in a flexible environment. Researchers in robot kinematics tried alternative methods in order to represent rigid body transformations based on concepts introduced by mathematicians and physicists such as Euler angle or Epsilon algebra. In the present work alternative representations, using quaternion algebra and lie algebra are proposed, tried and compared

Learning and leveraging kinematics for robot motion planning under uncertainty

Service robots that can assist humans in performing day-to-day tasks will need to be general-purpose robots that can perform a wide array of tasks without much supervision from end-users. As they will be operating in unstructured and ever-changing human environments, they will need to be capable of adapting to their work environments quickly and learning to perform novel tasks within a few trials. However, current robots fall short of these requirements as they are generally highly specialized, can only perform fixed, predefined tasks reliably, and need to operate in controlled environments. One of the main reasons behind this big gap is that the current robots require complete and accurate information about their surroundings to function effectively, whereas, in human environments, robots will only have access to limited information about their tasks and environments. With incomplete information about its surroundings, a robot using pre-programmed or pre-learned motion policies will fail to adapt to the novel situations encountered during operation and fall short in completing its tasks. Online motion generation methods that do not reason about the lack of information will not suffice either, as the developed policies may be unreliable under incomplete information. Reasoning about the lack of information becomes critical for manipulation tasks a service robot would have to perform. These tasks will often require interacting with multiple objects that make or break contacts during the task. A contact between objects can significantly alter their subsequent motion and lead to sudden transitions in their dynamics. Under these sudden transitions, even minor errors in estimating object poses can cause drastic deviations from the robot's initial motion plan for the task and lead the robot to failure in completing the tasks. Hence, service robots need methods that generate motion policies for manipulation tasks efficiently while accounting for the uncertainty due to incomplete or partial information. Partially Observable Markov Decision Processes (POMDPs) is one such mathematical framework that can model and plan for tasks where the agent lacks complete information about the task. However, POMDPs incur exponentially increasing computational costs with planning time horizon, which restricts the current POMDP-based planning methods to problems having short time horizons. Another challenge for planning-based approaches is that they require a state transition function for the world they are operating in to develop motion plans, which may not always be available to the robot. In control theory terms, a state transition function for the world is analogous to its system plant. In this dissertation, we propose to address these challenges by developing methods that can learn state transition functions for robot manipulation tasks directly from observations and later use them to generate long-horizon motion plans to complete the task under uncertainty. We first model the world state transition functions for robot manipulation tasks involving sudden transitions, such as due to contacts, using hybrid models and develop a novel hierarchical POMDP-planner that leverages the representational power of hybrid models to develop motion plans for long-horizon tasks under uncertainty. Next, we address the requirement of planning-based methods to have access to world state transition functions. We introduce three novel methods for learning kinematic models for articulated objects directly from observations and present an algorithm to construct the state transition functions from the learned kinematics models for manipulating these objects. We focus on learning models for articulated objects as they form one of the biggest sets of household objects that service robots will frequently interact with. The first method, MICAH, focuses on learning kinematic models for articulated objects that exhibit configuration-dependent articulation properties, such as a refrigerator door that stays closed magnetically, from unsegmented sequences of observations of object part poses. Next, we introduce ScrewNet, which removes the requirement of object pose estimation of MICAH and learns articulation properties of objects directly from raw sensory data available to the robot (depth images) without knowing their articulation model category a priori. Extending it further, we introduce DUST-net, which learns distributions over articulation model parameters for objects indicating the network's confidence over the estimated parameters directly from raw depth images. Combining these methods, in this dissertation, we introduce a unified framework that can enable a robot to learn state transition functions for manipulation tasks from observations and later use them to develop long-horizon plans even under uncertainty.Mechanical Engineerin