Supervised vs. Unsupervised Learning for Operator State Modeling in Unmanned Vehicle Settings

In this paper, we model operator states using hidden Markov models applied to human supervisory control behaviors. More specifically, we model the behavior of an operator of multiple heterogeneous unmanned vehicle systems. The hidden Markov model framework allows the inference of higher operator states from observable operator interaction with a computer interface. For example, a sequence of operator actions can be used to compute a probability distribution of possible operator states. Such models are capable of detecting deviations from expected operator behavior as learned by the model.The difficulty with parametric inference models such as hidden Markov models is that a large number of parameters must either be specified by hand or learned from example data.We compare the behavioral models obtained with two different supervised learning techniques and an unsupervised hidden Markov model training technique. The results suggest that the best models of human supervisory control behavior are obtained through unsupervised learning. We conclude by presenting further extensions to this work.This research was supported by Boeing Phantom Works

Predictive models of human supervisory control behavioral patterns using hidden semi-Markov models

Behavioral models of human operators engaged in complex,time-critical high-risk domains, such as those typical in Human Supervisory Control (HSC) settings, are of great value because of the high cost of operator failure. We propose that Hidden Semi-Markov Models (HSMMs) can be employed to model behaviors of operators in HSC settings where there is some intermittent human interaction with a system via a set of external controls. While regular Hidden Markov Models (HMMs) can be used to model operator behavior, HSMMs are particularly suited to time-critical supervisory control domains due to their explicit representation of state duration. Using HSMMs,we demonstrate in an unmanned vehicle supervisory control environment that such models can accurately predict future operator behavior both in terms of states and durations.This research was sponsored by the Boeing Research and Technology and the Office of Naval Research

Kinome rewiring reveals AURKA limits PI3K-pathway inhibitor efficacy in breast cancer.

Dysregulation of the PI3K-AKT-mTOR signaling network is a prominent feature of breast cancers. However, clinical responses to drugs targeting this pathway have been modest, possibly because of dynamic changes in cellular signaling that drive resistance and limit drug efficacy. Using a quantitative chemoproteomics approach, we mapped kinome dynamics in response to inhibitors of this pathway and identified signaling changes that correlate with drug sensitivity. Maintenance of AURKA after drug treatment was associated with resistance in breast cancer models. Incomplete inhibition of AURKA was a common source of therapy failure, and combinations of PI3K, AKT or mTOR inhibitors with the AURKA inhibitor MLN8237 were highly synergistic and durably suppressed mTOR signaling, resulting in apoptosis and tumor regression in vivo. This signaling map identifies survival factors whose presence limits the efficacy of targeted therapies and reveals new drug combinations that may unlock the full potential of PI3K-AKT-mTOR pathway inhibitors in breast cancer

The malignant phenotype in breast cancer is driven by eIF4A1-mediated changes in the translational landscape

Human mRNA DeXD/H-box helicases are ubiquitous molecular motors that are required for the majority of cellular processes that involve RNA metabolism. One of the most abundant is eIF4A, which is required during the initiation phase of protein synthesis to unwind regions of highly structured mRNA that would otherwise impede the scanning ribosome. Dysregulation of protein synthesis is associated with tumorigenesis, but little is known about the detailed relationships between RNA helicase function and the malignant phenotype in solid malignancies. Therefore, immunohistochemical analysis was performed on over 3000 breast tumors to investigate the relationship among expression of eIF4A1, the helicase-modulating proteins eIF4B, eIF4E and PDCD4, and clinical outcome. We found eIF4A1, eIF4B and eIF4E to be independent predictors of poor outcome in ER-negative disease, while in contrast, the eIF4A1 inhibitor PDCD4 was related to improved outcome in ER-positive breast cancer. Consistent with these data, modulation of eIF4A1, eIF4B and PCDC4 expression in cultured MCF7 cells all restricted breast cancer cell growth and cycling. The eIF4A1-dependent translatome of MCF7 cells was defined by polysome profiling, and was shown to be highly enriched for several classes of oncogenic genes, including G-protein constituents, cyclins and protein kinases, and for mRNAs with G/C-rich 5′UTRs with potential to form G-quadruplexes and with 3′UTRs containing microRNA target sites. Overall, our data show that dysregulation of mRNA unwinding contributes to the malignant phenotype in breast cancer via preferential translation of a class of genes involved in pro-oncogenic signaling at numerous levels. Furthermore, immunohistochemical tests are promising biomarkers for tumors sensitive to anti-helicase therapies

Electrochemical methods for speciation of trace elements in marine waters. Dynamic aspects

The contribution of electrochemical methods to the knowledge of dynamic speciation of toxic trace elements in marine waters is critically reviewed. Due to the importance of dynamic considerations in the interpretation of the electrochemical signal, the principles and recent developments of kinetic features in the interconversion of metal complex species will be presented. As dynamic electrochemical methods, only stripping techniques (anodic stripping voltammetry and stripping chronopotentiometry) will be used because they are the most important for the determination of trace elements. Competitive ligand ex- change-adsorptive cathodic stripping voltammetry, which should be considered an equilibrium technique rather than a dynamic method, will be also discussed because the complexing parameters may be affected by some kinetic limitations if equilibrium before analysis is not attained and/or the flux of the adsorbed complex is in fluenced by the lability of the natural complexes in the water sample. For a correct data interpretation and system characterization the comparison of results obtained from different techniques seems essential in the articulation of a serious discussion of their meaning