Modeling, identification, and application of multilayer polypyrrole conducting polymer actuators

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2007.Includes bibliographical references.Experiments were performed using commercially available, self-contained, multilayer polypyrrole (PPy) actuators to develop low-order lumped parameter models of actuator electrical, mechanical, and electromechanical behavior. Experimental data were processed using system identification techniques. Both grey box and black box models were identified. The grey box model consisted of a first order electrical network that was linearly and algebraically coupled to a second order viscoelastic model. The black box model incorporated a third order Box-Jenkins structure and achieved model to data residues comparable to the grey box model. When utilizing validation data, the grey box model showed very good performance for loads in the range of 0.5 to 3 N. Overall, the results of system identification experiments suggested that low order, lumped parameter models were adequate to describe the gross behavior of multilayer actuators. An online identification scheme was developed for monitoring polymer electrical impedance and thereby monitoring the degradation state of an actuator. This identification was performed successfully using recursive least squares and least squares for a discrete impedance model.(cont.) Experimental validation data, spanning more than 5 hours of continuous operation, were collected and analyzed. A final contribution of this research was the application PPy linear actuators to a custom-designed humanoid foot. Four linear conducting polymer actuators were used to obtain multifunctional behavior of the overall foot. Jacobian analysis of stiffness and damping was performed for the design. Simulations illustrated that PPy actuators through the use of appropriate electrical excitation can modulate their stiffness characteristics as a function of time to match a desired force versus length relationship.by Thomas W. Secord.S.M

Design and application of a cellular, piezoelectric, artificial muscle actuator for biorobotic systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 219-227).One of the foremost challenges in robotics is the development of muscle-like actuators that have the capability to reproduce the smooth motions observed in animals. Biological muscles have a unique cellular structure that departs from traditional electromechanical actuators in several ways. A muscle consists of a vast number of muscle fibers and, more fundamentally, sarcomeres that act as cellular units or building blocks. A muscle's output force and displacement are the aggregate effect of the individual building blocks. Thus, without using gearing or transmissions, muscles can be tailored to a range of loads, satisfying specific force and displacement requirements. These natural actuators are desirable for biorobotic applications, but many of their characteristics have been difficult to reproduce artificially. This thesis develops and applies a new artificial muscle actuator based on piezoelectric technology. The essential approach is to use a subdivided, cellular architecture inspired by natural muscle. The primary contributions of this work stem from three sequential aims. The first aim is to develop the operating principles and design of the actuator cellular units. The basic operating principle of the actuator involves nested flexural amplifiers applied to piezoelectric stacks thereby creating an output length strain commensurate with natural muscle. The second aim is to further improve performance of the actuator design by imparting tunable stiffness and resonance capabilities. This work demonstrates a previously unavailable level of tunability in both stiffness and resonance. The final aim is to showcase the capabilities of the actuator design by developing an underwater biorobotic fish system that utilizes the actuators for resonance-based locomotion. Each aspect of this thesis is supported by rigorous analysis and functional prototypes that augment broadly applicable design concepts.by Thomas William Secord.Ph.D

Neptunism and transformism:Robert Jameson and other evolutionary theorists in early nineteenth-century Scotland

This paper sheds new light on the prevalence of evolutionary ideas in Scotland in the early nineteenth century and establish what connections existed between the espousal of evolutionary theories and adherence to the directional history of the earth proposed by Abraham Gottlob Werner and his Scottish disciples. A possible connection between Wernerian geology and theories of the transmutation of species in Edinburgh in the period when Charles Darwin was a medical student in the city was suggested in an important 1991 paper by James Secord. This study aims to deepen our knowledge of this important episode in the history of evolutionary ideas and explore the relationship between these geological and evolutionary discourses. To do this it focuses on the circle of natural historians around Robert Jameson, Wernerian geologist and professor of natural history at the University of Edinburgh from 1804 to 1854. From the evidence gathered here there emerges a clear confirmation that the Wernerian model of geohistory facilitated the acceptance of evolutionary explanations of the history of life in early nineteenth-century Scotland. As Edinburgh was at this time the most important center of medical education in the English-speaking world, this almost certainly influenced the reception and development of evolutionary ideas in the decades that followed.</p

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation