Showing papers by "University of Missouri published in 2022"

Incremental Twisting Fault Tolerant Control for Hypersonic Vehicles With Partial Model Knowledge

Abstract: A passive fault tolerant control scheme is proposed for the full reentry trajectory tracking of a hypersonic vehicle in the presence of modeling uncertainties, external disturbances, and actuator faults. To achieve this goal, the attitude error dynamics with relative degree two is formulated first by ignoring the nonlinearities induced by the translational motions. Then, a multivariable twisting controller is developed as a benchmark to ensure the precise tracking task. Theoretical analysis with the Lyapunov method proves that the attitude tracking error and its first-order derivative can simultaneously converge to the origin exponentially. To depend less on the model knowledge and reduce the system uncertainties, an incremental twisting fault tolerant controller is derived based on the incremental nonlinear dynamic inversion control and the predesigned twisting controller. In this article, it is shown that not only the benefits of both incremental control and twisting control are inherited, but also their side effects are reduced. Notably, the proposed controller is user friendly in that only fixed gains and partial model knowledge are required. Numerical simulations in various cases and comparison studies are conducted to verify the effectiveness of the proposed method.

Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies

Abstract: Antibodies have the capacity to bind a diverse set of antigens, and they have become critical therapeutics and diagnostic molecules. The binding of antibodies is facilitated by a set of six hypervariable loops that are diversified through genetic recombination and mutation. Even with recent advances, accurate structural prediction of these loops remains a challenge. Here, we present IgFold, a fast deep learning method for antibody structure prediction. IgFold consists of a pre-trained language model trained on 558M natural antibody sequences followed by graph networks that directly predict backbone atom coordinates. IgFold predicts structures of similar or better quality than alternative methods (including AlphaFold) in significantly less time (under one minute). Accurate structure prediction on this timescale makes possible avenues of investigation that were previously infeasible. As a demonstration of IgFold’s capabilities, we predicted structures for 105K paired antibody sequences, expanding the observed antibody structural space by over 40 fold.

Peptide fusion improves prime editing efficiency

Abstract: Prime editing enables search-and-replace genome editing but is limited by low editing efficiency. We present a high-throughput approach, the Peptide Self-Editing sequencing assay (PepSEq), to measure how fusion of 12,000 85-amino acid peptides influences prime editing efficiency. We show that peptide fusion can enhance prime editing, prime-enhancing peptides combine productively, and a top dual peptide-prime editor increases prime editing significantly in multiple cell lines across dozens of target sites. Top prime-enhancing peptides function by increasing translation efficiency and serve as broadly useful tools to improve prime editing efficiency.

Co‐Delivery of Precisely Prescribed Multi‐Prodrug Combination by an Engineered Nanocarrier enables Efficient Individualized Cancer Chemotherapy

Abstract: The limited anticancer drug library and the frequent occurrence of drug resistance have driven monotherapy-based cancer therapy into a difficult situation. Considering the formidable process of new drug discovery, combination therapy using currently available drugs is a potential alternative. Nevertheless, the barrier between in vitro combination screening and precise in vivo delivery remains insurmountable in the current free-drug- or nanoparticle (NP)-based combination therapy, which substantially hinders the application of combination therapy. Herein, a novel, precise drug delivery strategy to realize efficient and individualized combination therapy is proposed. Nanomedicine (NM) is engineered using a microfluidics-based mixer by combining rationally designed polymeric prodrugs of three commercial chemotherapeutics and a cascade-responsive block copolymer; the NM possesses ratiometric drug loading and synchronized drug release. In addition to quantitative drug loading and precisely controlled drug combination, consistent nanoproperties of these NPs make their in vivo fate predictable. Consequently, tumor growth and metastasis can be effectively inhibited by precisely prescribed NPs derived from in vitro combination screening. This proof-of-concept study clearly reveals the feasibility of overcoming the current drug-library limitations through precise delivery of any predetermined drug combination, facilitating translational research of individualized combination therapy.

A robust credibility DEA model with fuzzy perturbation degree: An application to hospitals performance

Abstract: Performance evaluation enables decision makers (DMs) to have a better view about the weaknesses and strengths of leading units to improve efficiencies as a crucial goal. Data envelopment analysis (DEA) is the most popular technique to measure performance efficiency of decision making units (DMUs). However, conventional DEA is unable to consider uncertainty of input and output data in the evaluations. In this study, in order to address uncertainty in data, a robust credibility DEA (RCDEA) model has been introduced. First, a fuzzy credibility approach is used to construct fuzzy data. Then, a robust optimization approach is applied to consider uncertainty in constructing fuzzy sets. Moreover, perturbation level is considered as exact and fuzzy values. To illustrate the capability of the proposed model, 28 hospitals are evaluated in northwestern region of Iran and results are analyzed. According to the results, as perturbation degree increases, DMUs get normalized lower efficiencies and vise-versa.

Data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning

Abstract: In this paper, we present a novel data-driven design method for the human-robot interaction (HRI) system, where a given task is achieved by cooperation between the human and the robot. The presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller design. The task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop, while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the inner-loop. Data-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance parameters. In the inner-loop, a velocity-free filter is designed to avoid the requirement of end-effector velocity measurement. On this basis, an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task space. The simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.

New-Onset Dementia Among Survivors of Pneumonia Associated With Severe Acute Respiratory Syndrome Coronavirus 2 Infection

Abstract: Case series without control groups suggest that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may result in cognitive deficits and dementia in the postinfectious period.Adult pneumonia patients with SARS-CoV-2 infection (index hospitalization) and age-, gender-, and race/ethnicity-matched contemporary control pneumonia patients without SARS-CoV-2 infection were identified from 110 healthcare facilities in United States. The risk of new diagnosis of dementia following >30 days after the index hospitalization event without any previous history of dementia was identified using logistic regression analysis to adjust for potential confounders.Among 10 403 patients with pneumonia associated with SARS-CoV-2 infection, 312 patients (3% [95% confidence interval {CI}, 2.7%-3.4%]) developed new-onset dementia over a median period of 182 days (quartile 1 = 113 days, quartile 3 = 277 days). After adjustment for age, gender, race/ethnicity, hypertension, diabetes mellitus, hyperlipidemia, nicotine dependence/tobacco use, alcohol use/abuse, atrial fibrillation, previous stroke, and congestive heart failure, the risk of new-onset dementia was significantly higher with pneumonia associated with SARS-CoV-2 infection compared with pneumonia unrelated to SARS-CoV-2 infection (odds ratio [OR], 1.3 [95% CI, 1.1-1.5]). The association remained significant after further adjustment for occurrence of stroke, septic shock, and intubation/mechanical ventilation during index hospitalization (OR, 1.3 [95% CI, 1.1-1.5]).Approximately 3% of patients with pneumonia associated with SARS-CoV-2 infection developed new-onset dementia, which was significantly higher than the rate seen with other pneumonias.

Effect of Valproic Acid on the Metabolic Spectrum of Clozapine in Patients With Schizophrenia

Abstract: Valproic acid (VPA) is frequently used with clozapine (CLZ) as mood stabilizer and/or seizure prophylaxis. Valproic acid is known to reduce N-desmethylclozapine (N-DMC) but not CLZ levels. This leads to the hypothesis that VPA induces the CLZ metabolism via non-N-desmethylation pathways. Therefore, we aimed to investigate the effect of concurrent VPA use on the serum concentrations of a spectrum of CLZ metabolites in patients, adjusting for smoking.In total, 288 patients with an overall number of 737 serum concentration measurements of CLZ and metabolites concurrently using VPA (cases, n = 22) or no interacting drugs (controls, n = 266) were included from a routine therapeutic drug monitoring service. Linear mixed model analyses were performed to compare the dose-adjusted concentrations (C/D) of CLZ, N-DMC, CLZ 5N/N+-glucuronides, and metabolite-to-parent ratios in cases versus controls.After adjusting for covariates, the N-DMC (-40%, P < 0.001) and N+-glucuronide C/Ds (-78%, P < 0.001) were reduced in cases versus controls, while the CLZ C/D was unchanged (P > 0.7). In contrast, the 5N-glucuronide C/D (+250%, P < 0.001) and 5N-glucuronide-to-CLZ ratios (+120%, P = 0.01) were increased in cases versus controls.Our findings show that complex changes in CLZ metabolism underly the pharmacokinetic interaction with VPA. The lower levels of N-DMC seem to be caused by VPA-mediated induction of CLZ 5N-glucuronide formation, subsequently leading to reduced substrate availability for N-desmethylation. Whether the changes in CLZ metabolism caused by VPA affects the clinical outcome warrants further investigation.