TASK-BASED OPTIMIZATION OF MULTI-ARM SPACE ROBOTICS

There are many benefits to using multi-arm systems over a single arm system including higher flexibility in planning, better payload handling capacity, and reduction of joint torques. However, multi-arm systems are inherently more complex. This complexity does not necessarily translate to ``bigger" and ``heavier". This research seeks to answer the question of whether or not a multi-arm system can have lower mass than a single arm system. Using a task-based methodology, Independent single-arm and cooperative dual-arm manipulator systems are designed. A task defines the payload's motion and thus the manipulator's trajectory. Utilizing linear programming, a new method is developed in order to optimize the distribution of forces among the multiple arms in order to guarantee a minimum system mass. The mass of the motors and gears are estimated based on the required torque and speed, obtained from the trajectory and force-distribution. This study shows that a well-designed multi-arm system can in fact have a lower mass than a single-arm system. Further optimization demonstrates that a multi-arm system, when designed as a complete system rather than individual parts, can significantly reduce the total system mass

Development and Application of Virtual Screening Methods for G Protein-Coupled Receptors

G protein-coupled receptors (GPCR) constitute one of the largest family of transmembrane proteins that have been implicated in a multitude of diseases, including cancer and diabetes, and have been an important target in drug deve lopment. While experiment-based high-throughput screening for the unearthing of novel chemical compounds remains the de facto standard for drug discovery, virtual screening has been gaining acceptance as an important complementary method due to its high speed and low cost, which instead employs computers. This dissertation is aimed at the development of virtual screening algorithms as applied to GPCR’s, in addition to the construction of GPCR-related databases (GPCR-EXP, GLASS). MAGELLAN is a ligand-based virtual screening algorithm that makes inferences about what a GPCR would potentially bind based on sequence- and structure-based alignments. Building on top of this work, a sequential virtual screening pipeline combining MAGELLAN with AutoDock Vina was constructed for the discovery of novel, bifunctional opioids with mu opioid receptor (MOR) agonist and delta opioid receptor (DOR) antagonist activity. In the process of developing the virtual screening algorithms, two GPCR-related databases were constructed to provide necessary data for the study. GPCR-EXP is a database of experimentally-validated and predicted GPCR structures. Important features include semi-manual curation of data, weekly updates, a user-friendly web interface, and high-resolution structure models with GPCR-I-TASSER, which many of the other GPCR-related databases lack. Additionally, GLASS database was developed in response to the absence of databases dedicated to GPCR experimental data. As a result, pharmacological data was pooled and integrated into a single source, resulting in over 500,000 unique GPCR-ligand associations; this made it the most comprehensive database of its kind thus far, providing the community with an accessible web interface, freely-available data, and ligands ready for docking. MAGELLAN utilized pharmacological data from GLASS to infer from the ligands of sequence- and structure-based homologues what a target GPCR would bind. It was tested on two public virtual screening databases (DUD-E and GPCR-Bench) and achieved an average EF of 9.75 and 13.70, respectively, which compared favorably with AutoDock Vina (1.48/3.16), DOCK 6 (2.12/3.47), and PoLi (2.2). Lastly, case studies with the mu opioid and motilin receptors demonstrated its applicability to virtual screening in general, as well as GPCR de-orphanization. Subsequently, MAGELLAN was combined with AutoDock Vina into a novel, sequential virtual screen pipeline against both MOR and DOR to compensate for the weaknesses of each algorithm. Retrospective virtual screens against both MAGELLAN and AutoDock Vina were established for both receptors, and both methods were reported to have over-random discrimination between actives and decoys using the GPCR-Bench dataset. In conclusion, structure (GPCR-EXP) and pharmacological data (GLASS) databases were constructed to provide users with a comprehensive source of GPCR data. Moreover, GLASS made it possible for MAGELLAN to be developed, providing it a rich source of experimental data. In return, this resulted in greater performance than competing algorithms. Lastly, a prospective sequential virtual screening pipeline was established for the discovery of novel bifunctional opioids, in which the models for both methods were validated to perform well. In future studies, cAMP and β-arrestin assays will be run on a subset of compounds from a prospective virtual screen in the hopes of discovering a novel opioid with reduced tolerance and withdrawal.PHDBiological ChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147623/1/wallakin_1.pd

Kinematics and Robot Design I, KaRD2018

This volume collects the papers published on the Special Issue “Kinematics and Robot Design I, KaRD2018” (https://www.mdpi.com/journal/robotics/special_issues/KARD), which is the first issue of the KaRD Special Issue series, hosted by the open access journal “MDPI Robotics”. The KaRD series aims at creating an open environment where researchers can present their works and discuss all the topics focused on the many aspects that involve kinematics in the design of robotic/automatic systems. Kinematics is so intimately related to the design of robotic/automatic systems that the admitted topics of the KaRD series practically cover all the subjects normally present in well-established international conferences on “mechanisms and robotics”. KaRD2018 received 22 papers and, after the peer-review process, accepted only 14 papers. The accepted papers cover some theoretical and many design/applicative aspects

Physical Diagnosis and Rehabilitation Technologies

The book focuses on the diagnosis, evaluation, and assistance of gait disorders; all the papers have been contributed by research groups related to assistive robotics, instrumentations, and augmentative devices

Development of a face recognition system and its intelligent lighting compensation method for dark-field application

A face recognition system which uses 3D lighting estimation and optimal lighting compensation for dark-field application is proposed. To develop the proposed system, which can realize people identification in a near scene dark-field environment, a light-emitting diode (LED) overhead light, eight LED wall lights, a visible light binocular camera, and a control circuit are used. First, 68 facial landmarks are detected and their coordinates in both image as well as camera coordinate systems are computed. Second, a 3D morphable model (3DMM) is developed after considering facial shadows, and a transformation matrix between the 3DMM and camera coordinate systems is estimated. Third, to assess lighting uniformity, 30 evaluation points are selected from the face. Sequencing computations of LED radiation intensity, ray reflection luminance, camera response, and face lighting uniformity are then carried out. Ray occlusion is processed using a simplified 3D face model. Fourth, an optimal lighting compensation is realized: the overhead light is used for flood lighting, and the wall lights are employed as meticulous lighting. A genetic algorithm then is used to identify the optimal lighting of the wall lights. Finally, an Eigenface method is used for face recognition. The results show that our system and method can improve face recognition accuracy by >10% compared to traditional recognition methods

NASA SBIR abstracts of 1990 phase 1 projects

The research objectives of the 280 projects placed under contract in the National Aeronautics and Space Administration (NASA) 1990 Small Business Innovation Research (SBIR) Phase 1 program are described. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses in response to NASA's 1990 SBIR Phase 1 Program Solicitation. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 280, in order of its appearance in the body of the report. The document also includes Appendixes to provide additional information about the SBIR program and permit cross-reference in the 1990 Phase 1 projects by company name, location by state, principal investigator, NASA field center responsible for management of each project, and NASA contract number

Application of computer-aided drug design for identification of P. falciparum inhibitors

Malaria is a millennia-old disease with the first recorded cases dating back to 2700 BC found in Chinese medical records, and later in other civilizations. It has claimed human lives to such an extent that there are a notable associated socio-economic consequences. Currently, according to the World Health Organization (WHO), Africa holds the highest disease burden with 94% of deaths and 82% of cases with P. falciparum having ~100% prevalence. Chemotherapy, such as artemisinin combination therapy, has been and continues to be the work horse in the fight against the disease, together with seasonal malaria chemoprevention and the use of insecticides. Natural products such as quinine and artemisinin are particularly important in terms of their antimalarial activity. The emphasis in current chemotherapy research is the need for time and cost-effective workflows focussed on new mechanisms of action (MoAs) covering the target candidate profiles (TCPs). Despite a decline in cases over the past decades with, countries increasingly becoming certified malaria free, a stalling trend has been observed in the past five years resulting in missing the 2020 Global Technical Strategy (GTS) milestones. With no effective vaccine, a reduction in funding, slower drug approval than resistance emergence from resistant and invasive vectors, and threats in diagnosis with the pfhrp2/3 gene deletion, malaria remains a major health concern. Motivated by these reasons, the primary aim of this work was a contribution to the antimalarial pipeline through in silico approaches focusing on P. falciparum. We first intended an exploration of malarial targets through a proteome scale screening on 36 targets using multiple metrics to account for the multi-objective nature of drug discovery. The continuous growth of structural data offers the ideal scenario for mining new MoAs covering antimalarials TCPs. This was combined with a repurposing strategy using a set of orally available FDA approved drugs. Further, use was made of time- and cost-effective strategies combining QVina-W efficiency metrics that integrate molecular properties, GRIM rescoring for molecular interactions and a hydrogen mass repartitioning (HMR) molecular dynamics (MD) scheme for accelerated development of antimalarials in the context of resistance. This pipeline further integrates a complex ranking for better drug-target selectivity, and normalization strategies to overcome docking scoring function bias. The different metrics, ranking, normalization strategies and their combinations were first assessed using their mean ranking error (MRE). A version combining all metrics was used to select 36 unique protein-ligand complexes, assessed in MD, with the final retention of 25. From the 16 in vitro tested hits of the 25, fingolimod, abiraterone, prazosin, and terazosin showed antiplasmodial activity with IC50 2.21, 3.37, 16.67 and 34.72 μM respectively and of these, only fingolimod was found to be not safe with respect to human cell viability. These compounds were predicted active on different molecular targets, abiraterone was predicted to interact with a putative liver-stage essential target, hence promising as a transmission-blocking agent. The pipeline had a promising 25% hit rate considering the proteome-scale and use of cost-effective approaches. Secondly, we focused on Plasmodium falciparum 1-deoxy-D-xylulose-5-phosphate reductoisomerase (PfDXR) using a more extensive screening pipeline to overcome some of the current in silico screening limitations. Starting from the ZINC lead-like library of ~3M, hierarchical ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) approaches with molecular docking and re-scoring using eleven scoring functions (SFs) were used. Later ranking with an exponential consensus strategy was included. Selected hits were further assessed through Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA), advanced MD sampling in a ligand pulling simulations and (Weighted Histogram Analysis Method) WHAM analysis for umbrella sampling (US) to derive binding free energies. Four leads had better predicted affinities in US than LC5, a 280 nM potent PfDXR inhibitor with ZINC000050633276 showing a promising binding of -20.43 kcal/mol. As shown with fosmidomycin, DXR inhibition offers fast acting compounds fulfilling antimalarials TCP1. Yet, fosmidomycin has a high polarity causing its short half-life and hampering its clinical use. These leads scaffolds are different from fosmidomycin and hence may offer better pharmacokinetic and pharmacodynamic properties and may also be promising for lead optimization. A combined analysis of residues’ contributions to the free energy of binding in MM-PBSA and to steered molecular dynamics (SMD) Fmax indicated GLU233, CYS268, SER270, TRP296, and HIS341 as exploitable for compound optimization. Finally, we updated the SANCDB library with new NPs and their commercially available analogs as a solution to NP availability. The library is extended to 1005 compounds from its initial 600 compounds and the database is integrated to Mcule and Molport APIs for analogs automatic update. The new set may contribute to virtual screening and to antimalarials as the most effective ones have NP origin.Thesis (PhD) -- Faculty of Science, Biochemistry and Microbiology, 202