On Control Systems of the Brain: A Study of Their Connections, Activations, and Interactions

Implementation of daily functions in humans crucially relies on both the bottom-up moment-to- moment processing of relevant input and output information as well as the top-down controls that instantiate and regulate goal-directed strategies. The current dissertation focuses on different systems of brain regions related to task control. We are interested in investigating, in detail, some of the basic activity patterns that different control systems carry during simple tasks, and how differences in activity patterns may shed new insight onto the distinctions among the systems\u27 functional roles. In addition, carefully coordinated interactions between brain regions specialized for control-related activity and regions specialized for bottom-up information processing are essential for humans to adeptly undertake various goal-directed tasks. Hence, another goal is to explore how the relationships among regions related to control and regions related to processing will change as result of top-down control signals during tasks. In Chapter 2, we applied the graph theory method of link communities onto the brain\u27s resting-state intrinsic connectivity structure to identify possible points of interactions among the previously defined functional systems, including various control systems. In Chapter 3, we conducted a meta-analysis of tasks to examine the distinct functional characteristics of control systems in task activation. Using a data-driven clustering analysis, we identified two distinct trial-related response profiles that divided the regions of control systems into a right frontoparietal and cinguloopercular cluster, which may be engaged in fine-tuning task parameters and evaluating performance, and a left frontoparietal and dorsal attention cluster, which may be involved in timely updates of trial-wise parameters as well as information processing. In Chapter 4, we explored the changes in functional relationships among selected systems during individual trials of a goal-direct task and found the presence of complex and dynamic relationships that suggest changes among the various functional systems across a trial reflect both continuous as well as momentary effects of top-down signals. Collectively, the studies presented here both contributed to as well as challenged previous frameworks of task control in an effort to build better understanding of the basic organization and interactions among the brain\u27s functional systems

Opinion Optimization in Directed Social Networks

Shifting social opinions has far-reaching implications in various aspects, such as public health campaigns, product marketing, and political candidates. In this paper, we study a problem of opinion optimization based on the popular Friedkin-Johnsen (FJ) model for opinion dynamics in an unweighted directed social network with $n$ nodes and $m$ edges. In the FJ model, the internal opinion of every node lies in the closed interval $[0, 1]$, with 0 and 1 being polar opposites of opinions about a certain issue. Concretely, we focus on the problem of selecting a small number of $k\ll n$ nodes and changing their internal opinions to 0, in order to minimize the average opinion at equilibrium. We then design an algorithm that returns the optimal solution to the problem in $O(n^3)$ time. To speed up the computation, we further develop a fast algorithm by sampling spanning forests, the time complexity of which is $O(ln)$, with $l$ being the number of samplings. Finally, we execute extensive experiments on various real directed networks, which show that the effectiveness of our two algorithms is similar to each other, both of which outperform several baseline strategies of node selection. Moreover, our fast algorithm is more efficient than the first one, which is scalable to massive graphs with more than twenty million nodes

Friedkin-Johnsen Model for Opinion Dynamics on Signed Graphs

A signed graph offers richer information than an unsigned graph, since it describes both collaborative and competitive relationships in social networks. In this paper, we study opinion dynamics on a signed graph, based on the Friedkin-Johnsen model. We first interpret the equilibrium opinion in terms of a defined random walk on an augmented signed graph, by representing the equilibrium opinion of every node as a combination of all nodes\u27 internal opinions, with the coefficient of the internal opinion for each node being the difference of two absorbing probabilities. We then quantify some relevant social phenomena and express them in terms of the $\ell_2$ norms of vectors. We also design a nearly-linear time signed Laplacian solver for assessing these quantities, by establishing a connection between the absorbing probability of random walks on a signed graph and that on an associated unsigned graph. We further study the opinion optimization problem by changing the initial opinions of a fixed number of nodes, which can be optimally solved in cubic time. We provide a nearly-linear time algorithm with error guarantee to approximately solve the problem. Finally, we execute extensive experiments on sixteen real-life signed networks, which show that both of our algorithms are effective and efficient, and are scalable to massive graphs with over 20 million nodes

Gold-Sensitized Silicon/ZnO Core/Shell Nanowire Array for Solar Water Splitting

Solar water splitting represents one of the most promising strategies in the quest for clean and renewable energy. However, low conversion efficiency, use of sacrificial agents, and external bias for current water splitting system limit its practical application. Here, a gold-sensitized Si/ZnOcore/shell nanowire photoelectrochemical (PEC) cell is reported for efficient solar water oxidation. We demonstrated gold-sensitized n-Si/n-ZnO nanowire arrays exhibited higher energy conversion efficiency than gold-sensitized p-Si/n-ZnO nanowire arrays due to the favorable energy-band alignment characteristics. Without any assistance from an external electrical source and sacrificial reagents, gold-sensitized n-Si/n-ZnO core/shell nanowire array photoanode achieved unbiased water splitting under simulated solar light illumination. This method opens a promising venue to cost-efficient production of solar fuels

Surface Plasmon Enhanced Light Trapping in Metal/Silicon Nanobowl Arrays for Thin Film Photovoltaics

Enhancing the light absorption in thin film silicon solar cells with nanophotonic and plasmonic structures is important for the realization of high efficiency solar cells with significant cost reduction. In this work, we investigate periodic arrays of conformal metal/silicon nanobowl arrays (MSNBs) for light trapping applications in silicon solar cells. They exhibited excellent light-harvesting ability across a wide range of wavelengths up to infrared regimes. The optimized structure (MSNBsH) covered by SiO2 passivation layer and hemisphere Ag back reflection layer has a maximal short-circuit density (Jsc) 25.5 mA/cm2, which is about 88.8% higher than flat structure counterpart, and the light-conversion efficiency (η) is increased two times from 6.3% to 12.6%. The double-side textures offer a promising approach to high efficiency ultrathin silicon solar cells

Large field-of-view pine wilt disease tree detection based on improved YOLO v4 model with UAV images

IntroductionPine wilt disease spreads rapidly, leading to the death of a large number of pine trees. Exploring the corresponding prevention and control measures for different stages of pine wilt disease is of great significance for its prevention and control.MethodsTo address the issue of rapid detection of pine wilt in a large field of view, we used a drone to collect multiple sets of diseased tree samples at different times of the year, which made the model trained by deep learning more generalizable. This research improved the YOLO v4(You Only Look Once version 4) network for detecting pine wilt disease, and the channel attention mechanism module was used to improve the learning ability of the neural network.ResultsThe ablation experiment found that adding the attention mechanism SENet module combined with the self-designed feature enhancement module based on the feature pyramid had the best improvement effect, and the mAP of the improved model was 79.91%.DiscussionComparing the improved YOLO v4 model with SSD, Faster RCNN, YOLO v3, and YOLO v5, it was found that the mAP of the improved YOLO v4 model was significantly higher than the other four models, which provided an efficient solution for intelligent diagnosis of pine wood nematode disease. The improved YOLO v4 model enables precise location and identification of pine wilt trees under changing light conditions. Deployment of the model on a UAV enables large-scale detection of pine wilt disease and helps to solve the challenges of rapid detection and prevention of pine wilt disease

Chemical approaches targeting the hurdles of hepatocyte transplantation: mechanisms, applications, and advances

Hepatocyte transplantation (HTx) has been a novel cell-based therapy for severe liver diseases, as the donor livers for orthotopic liver transplantation are of great shortage. However, HTx has been confronted with two main hurdles: limited high-quality hepatocyte sources and low cell engraftment and repopulation rate. To cope with, researchers have investigated on various strategies, including small molecule drugs with unique advantages. Small molecules are promising chemical tools to modulate cell fate and function for generating high quality hepatocyte sources. In addition, endothelial barrier, immune responses, and low proliferative efficiency of donor hepatocytes mainly contributes to low cell engraftment and repopulation rate. Interfering these biological processes with small molecules is beneficial for improving cell engraftment and repopulation. In this review, we will discuss the applications and advances of small molecules in modulating cell differentiation and reprogramming for hepatocyte resources and in improving cell engraftment and repopulation as well as its underlying mechanisms

Light/ultrasound enhance peroxidase activity of BaTiO3/graphdiyne/Au nanozyme for colorimetric detection of E. coli O157:H7

In the past two decades, nanozymes have garnered increasing interest, however, their catalytic activity and efficacy still lag significantly behind that of natural enzymes, posing limitations on their utility in bioanalytical applications. In this study, we introduced a novel BaTiO3/graphdiyne/Au (BGA) nanozyme that leverages surface plasmon resonance and piezoelectric effects to concurrently respond to light and ultrasound (US) stimulation, resulting in a 3.8-fold enhancement in peroxidase-like activity. Theoretical and experimental findings suggest that US stimulation induces lattice distortion in BaTiO3, leading to the reversible conversion of C[tbnd]C bonds to C[dbnd]C bonds in graphdiyne. Consequently, the liberated electrons recombine with the hot holes produced by Au nanoparticles upon light excitation, thereby efficiently inhibiting the recombination of hot electron-hole pairs and substantially augmenting peroxidase-like activity. The BGA nanozyme was further configured as a detection platform for E. coli O157:H7. The sensor exhibited a broad linear range (1–107 CFU mL−1) and a low limit of detection of 7 CFU mL−1. Moreover, the sensor exhibited exceptional applicability in the analysis of various real samples such as milk and lemon juice. This study presents a novel research framework for constructing high-activity nanozyme sensors responsive to external fields, offering significant potential in biological analysis, environmental surveillance, and food safety applications.National Natural Science Foundation of ChinaThis work was supported by the National Natural Science Foundation of China (Grant No. 22375112), Natural Science Foundation of Shandong Province (Grant No. ZR2021MB111, ZR2020MB026, ZR2023ME076).Sensors and Actuators B: Chemica

Selective Pressure to Increase Charge in Immunodominant Epitopes of the H3 Hemagglutinin Influenza Protein

The evolutionary speed and the consequent immune escape of H3N2 influenza A virus make it an interesting evolutionary system. Charged amino acid residues are often significant contributors to the free energy of binding for protein–protein interactions, including antibody–antigen binding and ligand–receptor binding. We used Markov chain theory and maximum likelihood estimation to model the evolution of the number of charged amino acids on the dominant epitope in the hemagglutinin protein of circulating H3N2 virus strains. The number of charged amino acids increased in the dominant epitope B of the H3N2 virus since introduction in humans in 1968. When epitope A became dominant in 1989, the number of charged amino acids increased in epitope A and decreased in epitope B. Interestingly, the number of charged residues in the dominant epitope of the dominant circulating strain is never fewer than that in the vaccine strain. We propose these results indicate selective pressure for charged amino acids that increase the affinity of the virus epitope for water and decrease the affinity for host antibodies. The standard PAM model of generic protein evolution is unable to capture these trends. The reduced alphabet Markov model (RAMM) model we introduce captures the increased selective pressure for charged amino acids in the dominant epitope of hemagglutinin of H3N2 influenza (R2 > 0.98 between 1968 and 1988). The RAMM model calibrated to historical H3N2 influenza virus evolution in humans fit well to the H3N2/Wyoming virus evolution data from Guinea pig animal model studies

Isolation, characterization, and genomic analysis of a lytic bacteriophage, PQ43W, with the potential of controlling bacterial wilt

Bacterial wilt (BW) is a devastating plant disease caused by the soil-borne bacterium Ralstonia solanacearum species complex (Rssc). Numerous efforts have been exerted to control BW, but effective, economical, and environmentally friendly approaches are still not available. Bacteriophages are a promising resource for the control of bacterial diseases, including BW. So, in this study, a crop BW pathogen of lytic bacteriophage was isolated and named PQ43W. Biological characterization revealed PQ43W had a short latent period of 15 min, 74 PFU/cell of brust sizes, and good stability at a wide range temperatures and pH but a weak resistance against UV radiation. Sequencing revealed phage PQ43W contained a circular double-stranded DNA genome of 47,156 bp with 65 predicted open reading frames (ORFs) and genome annotation showed good environmental security for the PQ43W that no tRNA, antibiotic resistance, or virulence genes contained. Taxonomic classification showed PQ43W belongs to a novel genus of subfamily Kantovirinae under Caudoviricetes. Subsequently, a dose of PQ43W for phage therapy in controlling crop BW was determined: 108 PFU*20 mL per plant with non-invasive irrigation root application twice by pot experiment. Finally, a field experiment of PQ43W showed a significantly better control effect in crop BW than the conventional bactericide Zhongshengmycin. Therefore, bacteriophage PQ43W is an effective bio-control resource for controlling BW diseases, especially for crop cultivation