Approximate dynamic programming based solutions for fixed-final-time optimal control and optimal switching

Optimal solutions with neural networks (NN) based on an approximate dynamic programming (ADP) framework for new classes of engineering and non-engineering problems and associated difficulties and challenges are investigated in this dissertation. In the enclosed eight papers, the ADP framework is utilized for solving fixed-final-time problems (also called terminal control problems) and problems with switching nature. An ADP based algorithm is proposed in Paper 1 for solving fixed-final-time problems with soft terminal constraint, in which, a single neural network with a single set of weights is utilized. Paper 2 investigates fixed-final-time problems with hard terminal constraints. The optimality analysis of the ADP based algorithm for fixed-final-time problems is the subject of Paper 3, in which, it is shown that the proposed algorithm leads to the global optimal solution providing certain conditions hold. Afterwards, the developments in Papers 1 to 3 are used to tackle a more challenging class of problems, namely, optimal control of switching systems. This class of problems is divided into problems with fixed mode sequence (Papers 4 and 5) and problems with free mode sequence (Papers 6 and 7). Each of these two classes is further divided into problems with autonomous subsystems (Papers 4 and 6) and problems with controlled subsystems (Papers 5 and 7). Different ADP-based algorithms are developed and proofs of convergence of the proposed iterative algorithms are presented. Moreover, an extension to the developments is provided for online learning of the optimal switching solution for problems with modeling uncertainty in Paper 8. Each of the theoretical developments is numerically analyzed using different real-world or benchmark problems --Abstract, page v

Operation Moonshot: rapid translation of a SARS-CoV-2 targeted peptide immunoaffinity liquid chromatography-tandem mass spectrometry test from research into routine clinical use

OBJECTIVES: During 2020, the UK's Department of Health and Social Care (DHSC) established the Moonshot programme to fund various diagnostic approaches for the detection of SARS-CoV-2, the pathogen behind the COVID-19 pandemic. Mass spectrometry was one of the technologies proposed to increase testing capacity. METHODS: Moonshot funded a multi-phase development programme, bringing together experts from academia, industry and the NHS to develop a state-of-the-art targeted protein assay utilising enrichment and liquid chromatography tandem mass spectrometry (LC-MS/MS) to capture and detect low levels of tryptic peptides derived from SARS-CoV-2 virus. The assay relies on detection of target peptides, ADETQALPQRK (ADE) and AYNVTQAFGR (AYN), derived from the nucleocapsid protein of SARS-CoV-2, measurement of which allowed the specific, sensitive, and robust detection of the virus from nasopharyngeal (NP) swabs. The diagnostic sensitivity and specificity of LC-MS/MS was compared with reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) via a prospective study. RESULTS: Analysis of NP swabs (n=361) with a median RT-qPCR quantification cycle (Cq) of 27 (range 16.7-39.1) demonstrated diagnostic sensitivity of 92.4% (87.4-95.5), specificity of 97.4% (94.0-98.9) and near total concordance with RT-qPCR (Cohen's Kappa 0.90). Excluding Cq>32 samples, sensitivity was 97.9% (94.1-99.3), specificity 97.4% (94.0-98.9) and Cohen's Kappa 0.95. CONCLUSIONS: This unique collaboration between academia, industry and the NHS enabled development, translation, and validation of a SARS-CoV-2 method in NP swabs to be achieved in 5 months. This pilot provides a model and pipeline for future accelerated development and implementation of LC-MS/MS protein/peptide assays into the routine clinical laboratory

Induced intra- and intermolecular template switching as a therapeutic mechanism against RNA viruses

Viral RNA-dependent RNA polymerases (RdRps) are a target for broad-spectrum antiviral therapeutic agents. Recently, we demonstrated that incorporation of the T-1106 triphosphate, a pyrazine-carboxamide ribonucleotide, into nascent RNA increases pausing and backtracking by the poliovirus RdRp. Here, by monitoring enterovirus A-71 RdRp dynamics during RNA synthesis using magnetic tweezers, we identify the ‘‘backtracked’’ state as an intermediate used by the RdRp for copy-back RNA synthesis and homologous recombination. Cell-based assays and RNA sequencing (RNA-seq) experiments further demonstrate that the pyrazine-carboxamide ribonucleotide stimulates these processes during infection. These results suggest that pyrazine-carboxamide ribonucleotides do not induce lethal mutagenesis or chain termination but function by promoting template switching and formation of defective viral genomes. We conclude that RdRp-catalyzed intra- and intermolecular template switching can be induced by pyrazine-carboxamide ribonucleotides, defining an additional mechanistic class of antiviral ribonucleotides with potential for broad-spectrum activity

Finite-Time Stabilization of Uncertain Switched Positive Linear Systems with Time-Varying Delays

This paper is concerned with finite-time stabilization (FTS) analysis for a class of uncertain switched positive linear systems with time-varying delays. First, a new definition of finite-time boundedness (FTB) is introduced for switched positive system. This definition can simplify FTS analysis. Taking interval and polytopic uncertainties into account, a robust state feedback controller is built such that the switched positive linear system is finite-time bounded. Finally, an example is employed to illustrate the validities of obtained results

Nonlinear Systems

Open Mathematics is a challenging notion for theoretical modeling, technical analysis, and numerical simulation in physics and mathematics, as well as in many other fields, as highly correlated nonlinear phenomena, evolving over a large range of time scales and length scales, control the underlying systems and processes in their spatiotemporal evolution. Indeed, available data, be they physical, biological, or financial, and technologically complex systems and stochastic systems, such as mechanical or electronic devices, can be managed from the same conceptual approach, both analytically and through computer simulation, using effective nonlinear dynamics methods. The aim of this Special Issue is to highlight papers that show the dynamics, control, optimization and applications of nonlinear systems. This has recently become an increasingly popular subject, with impressive growth concerning applications in engineering, economics, biology, and medicine, and can be considered a veritable contribution to the literature. Original papers relating to the objective presented above are especially welcome subjects. Potential topics include, but are not limited to: Stability analysis of discrete and continuous dynamical systems; Nonlinear dynamics in biological complex systems; Stability and stabilization of stochastic systems; Mathematical models in statistics and probability; Synchronization of oscillators and chaotic systems; Optimization methods of complex systems; Reliability modeling and system optimization; Computation and control over networked systems

A classification-based approach to the optimal control of affine switched systems

This paper deals with the optimal control of discrete–time switched systems, characterized by a finite set of operating modes, each one associated with given affine dynamics. The objective is the design of the switching law so as to minimize an infinite–horizon expected cost, that penalizes frequent switchings. The optimal switching law is computed off–line, which allows an efficient online operation of the control via a state feedback policy. The latter associates a mode to each state and, as such, can be viewed as a classifier. In order to train such classifier–type controller one needs first to generate a set of training data in the form of optimal state–mode pairs. In the considered setting, this involves solving a Mixed Integer Quadratic Programming (MIQP) problem for each pair. A key feature of the proposed approach is the use of a classification method that provides guarantees on the generalization properties of the classifier. The approach is tested on a multi–room heating control problem

The Mechanism and Regulation of Bacteriophage DNA Packaging Motors

Many double-stranded DNA viruses use a packaging motor during maturation to recognize and transport genetic material into the capsid. In terminase motors, the TerS complex recognizes DNA, while the TerL motor packages the DNA into the capsid shell. Although there are several models for DNA recognition and translocation, how the motor components assemble and power DNA translocation is unknown. Using the thermophilic P74-26 bacteriophage model system, we discover that TerL uses a trans-activated ATP hydrolysis mechanism. Additionally, we identify critical residues for TerL ATP hydrolysis and DNA binding. With a combination of x-ray crystallography, SAXS, and molecular docking, we build a structural model for TerL pentamer assembly. Apo and ATP analog-bound TerL ATPase domain crystal structures show ligand-dependent conformational changes, which we propose power DNA translocation. Together, we assimilate these findings to build models for both motor assembly and DNA translocation. Additionally, with the P76-26 system, we identify the TerS protein as gp83. I find that P74-26 TerS is a nonameric ring that stimulates TerL ATPase activity while inhibiting TerL nuclease activity. Using cryoEM, I solve 3.8 Å and 4.8 Å resolution symmetric and asymmetric reconstructions of the TerS ring. I observe in P74-26 TerS, the conserved C-terminal beta-barrel is absent, and instead the region is flexible or unstructured. Furthermore, the helix-turn-helix motifs of P74-26 TerS are positioned differently than those of known TerS structures, suggesting P74-26 uses an alternative mechanism to recognize DNA