Model-Assisted Online Optimization of Gain-Scheduled PID Control Using NSGA-II Iterative Genetic Algorithm

In the practical control of nonlinear valve systems, PID control, as a model-free method, continues to play a crucial role thanks to its simple structure and performance-oriented tuning process. To improve the control performance, advanced gain-scheduling methods are used to schedule the PID control gains based on the operating conditions and/or tracking error. However, determining the scheduled gain is a major challenge, as PID control gains need to be determined at each operating condition. In this paper, a model-assisted online optimization method is proposed based on the modified Non-Dominated Sorting Genetic Algorithms-II (NSGA-II) to obtain the optimal gain-scheduled PID controller. Model-assisted offline optimization through computer-in-the-loop simulation provides the initial scheduled gains for an online algorithm, which then uses the iterative NSGA-II algorithm to automatically schedule and tune PID gains by online searching of the parameter space. As a summary, the proposed approach presents a PID controller optimized through both model-assisted learning based on prior model knowledge and model-free online learning. The proposed approach is demonstrated in the case of a nonlinear valve system able to obtain optimal PID control gains with a given scheduled gain structure. The performance improvement of the optimized gain-scheduled PID control is demonstrated by comparing it with fixed-gain controllers under multiple operating conditions

SARS-CoV-2 spike-reactive naïve B cells and pre-existing memory B cells contribute to antibody responses in unexposed individuals after vaccination

IntroductionSince December 2019, the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19) has presented considerable public health challenges. Multiple vaccines have been used to induce neutralizing antibodies (nAbs) and memory B-cell responses against the viral spike (S) glycoprotein, and many essential epitopes have been defined. Previous reports have identified severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike-reactive naïve B cells and preexisting memory B cells in unexposed individuals. However, the role of these spike-reactive B cells in vaccine-induced immunity remains unknown.MethodsTo elucidate the characteristics of preexisting SARS-CoV-2 S-reactive B cells as well as their maturation after antigen encounter, we assessed the relationship of spike-reactive B cells before and after vaccination in unexposed human individuals. We further characterized the sequence identity, targeting domain, broad-spectrum binding activity and neutralizing activity of these SARS-CoV-2 S-reactive B cells by isolating monoclonal antibodies (mAbs) from these B cells.ResultsThe frequencies of both spike-reactive naïve B cells and preexisting memory B cells before vaccination correlated with the frequencies of spike-reactive memory B cells after vaccination. Isolated mAbs from spike-reactive naïve B cells before vaccination had fewer somatic hypermutations (SHMs) than mAbs isolated from spike-reactive memory B cells before and after vaccination, but bound SARS-CoV-2 spike in vitro. Intriguingly, these germline-like mAbs possessed broad binding profiles for SARS-CoV-2 and its variants, although with low or no neutralizing capacity. According to tracking of the evolution of IGHV4-4/IGKV3-20 lineage antibodies from a single donor, the lineage underwent SHMs and developed increased binding activity after vaccination.DiscussionOur findings suggest that spike-reactive naïve B cells can be expanded and matured by vaccination and cocontribute to vaccine-elicited antibody responses with preexisting memory B cells. Selectively and precisely targeting spike-reactive B cells by rational antigen design may provide a novel strategy for next-generation SARS-CoV-2 vaccine development

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Reconstruction of Fast Acquisition MRI with Under-sampled K-space Data by Using Massive-Training Artificial Neural Networks (MTANNs)

identifier:oai:t2r2.star.titech.ac.jp:5068481

Feature Map Visualization for Explaining Black-Box Deep Learning Model in Liver Tumor Segmentation

identifier:oai:t2r2.star.titech.ac.jp:5068482

Explaining Massive-Training Artificial Neural Networks in Medical Image Analysis Task Through Visualizing Functions Within the Models

identifier:oai:t2r2.star.titech.ac.jp:5067884