Browsing by Author "Hu, Z."
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Item Quantitative liver-specific protein fingerprint in blood: A signature for hepatotoxicity(2014-01-14) Hu, Z.; Lausted, C.; Yoo, Hyuntae; Yan, X.; Brightman, A.; Chen, J.; Wang, W.; Bu, X.; Hood, L.We discuss here a new approach to detecting hepatotoxicity by employing concentration changes of liver-specific blood proteins during disease progression. These proteins are capable of assessing the behaviors of their cognate liver biological networks for toxicity or disease perturbations. Blood biomarkers are highly desirable diagnostics as blood is easily accessible and baths virtually all organs. Fifteen liver-specific blood proteins were identified as markers of acetaminophen (APAP)-induced hepatotoxicity using three proteomic technologies: label-free antibody microarrays, quantitative immunoblotting, and targeted iTRAQ mass spectrometry. Liver-specific blood proteins produced a toxicity signature of eleven elevated and four attenuated blood protein levels. These blood protein perturbations begin to provide a systems view of key mechanistic features of APAP-induced liver injury relating to glutathione and S-adenosyl-L-methionine (SAMe) depletion, mitochondrial dysfunction, and liver responses to the stress. Two markers, elevated membrane- bound catechol-O-methyltransferase (MB-COMT) and attenuated retinol binding protein 4 (RBP4), report hepatic injury significantly earlier than the current gold standard liver biomarker, alanine transaminase (ALT). These biomarkers were perturbed prior to onset of irreversible liver injury. Ideal markers should be applicable for both rodent model studies and human clinical trials. Five of these mouse liver-specific blood markers had human orthologs that were also found to be responsive to human hepatotoxicity. This panel of liver-specific proteins has the potential to effectively identify the early toxicity onset, the nature and extent of liver injury and report on some of the APAP-perturbed liver networks.Item Realization of the First GaN Based Tunnel Field-Effect Transistor(Institute of Electrical and Electronics Engineers Inc.) Chaney, A.; Turski, H.; Nomoto, K.; Wang, Qingxiao; Hu, Z.; Kim, Moon J.; Xing, H. G.; Jena, D.; Wang, Qingxiao; Kim, Moon J.Tunnel field-effect transistors (TFETs) offer the means to surpass the subthreshold swing (SS) limit of 60 mV/dec that limits MOSFETs. While MOSFETs rely on modulating a potential barrier, which is subject to a Boltzmann tail in the density of states (DOS), interband tunneling in TFETs enables a sharp turn off of the DOS because the transport is no longer governed by an exponential tail of carriers. These devices have been investigated in Si III-V material systems¹, achieving SS's as low as 20 mV/dec ². GaN is advantageous to these other material systems because its large bandgap is ideal for suppressing leakage current. Unfortunately impurity doping in GaN alone is not enough to achieve the internal fields required to promote interband tunneling[Fig l(a)]. However, by taking advantage of the difference in polarization fields between InGaN and GaN, a device structure favoring interband tunneling can be made [Fig l(b)]. Li et. al.³ have theoretically predicted that a GaN heterojunction TFET could obtain an SS of 15 mV/dec and a peak current of 1× 10⁻⁴ A/µm. For the work being presented, GaN TFETs were fabricated using a surrounding gate (SG) architecture utilizing both nanowires and fins formed from a top-down approach. © 2018 IEEE.Item Stochastic Optimal Power Flow Based on Data-Driven Distributionally Robust Optimization(Institute of Electrical and Electronics Engineers Inc.) Guo, Yi; Baker, K.; Dall'Anese, E.; Hu, Z.; Summers, Tyler H.; Guo, Yi; Summers, Tyler H.We propose a data-driven method to solve a stochastic optimal power flow (OPF) problem based on limited information about forecast error distributions. The objective is to determine power schedules for controllable devices in a power network to balance operational cost and conditional value-at-risk (CVaR) of device and network constraint violations. These decisions include scheduled power output adjustments and reserve policies, which specify planned reactions to forecast errors in order to accommodate fluctuating renewable energy sources. Instead of assuming the uncertainties across the networks follow prescribed probability distributions, we assume the distributions are only observable through a finite training dataset. By utilizing the Wasserstein metric to quantify differences between the empirical data-based distribution and the real data-generating distribution, we formulate a distributionally robust optimization OPF problem to search for power schedules and reserve policies that are robust to sampling errors inherent in the dataset. A multi-stage closed-loop control strategy based on model predictive control (MPC) is also discussed. A simpIe numerical example illustrates inherent tradeoffs between operational cost and risk of constraint violation, and we show how our proposed method offers a data-driven framework to balance these objectives.