A Tent L\'evy Flying Sparrow Search Algorithm for Feature Selection: A COVID-19 Case Study

The "Curse of Dimensionality" induced by the rapid development of information science, might have a negative impact when dealing with big datasets. In this paper, we propose a variant of the sparrow search algorithm (SSA), called Tent L\'evy flying sparrow search algorithm (TFSSA), and use it to select the best subset of features in the packing pattern for classification purposes. SSA is a recently proposed algorithm that has not been systematically applied to feature selection problems. After verification by the CEC2020 benchmark function, TFSSA is used to select the best feature combination to maximize classification accuracy and minimize the number of selected features. The proposed TFSSA is compared with nine algorithms in the literature. Nine evaluation metrics are used to properly evaluate and compare the performance of these algorithms on twenty-one datasets from the UCI repository. Furthermore, the approach is applied to the coronavirus disease (COVID-19) dataset, yielding the best average classification accuracy and the average number of feature selections, respectively, of 93.47% and 2.1. Experimental results confirm the advantages of the proposed algorithm in improving classification accuracy and reducing the number of selected features compared to other wrapper-based algorithms

Structure-based virtual screening for novel p38 MAPK inhibitors and a biological evaluation

Mitogen-activated protein kinases (MAPKs) are a group of serine-threonine protein kinases that can be activated by extracellular stimuli. MAPK14 (p38α) affects major disease processes, while inhibition of p38α has been shown to have potential therapeutic effects. Many inhibitors targeting p38α have entered clinical trials but have a long development cycle and severe side effects. We developed a multi-step receptor structure-based virtual screening method to screen potential bioactive molecules from SPECS and our MCDB libraries. Compound 10 was identified as a promising p38α inhibitor that may be used in the treatment of p38αMAPK pathway-related diseases, but corollary studies are warranted

Progression Cognition Reinforcement Learning with Prioritized Experience for Multi-Vehicle Pursuit

Multi-vehicle pursuit (MVP) such as autonomous police vehicles pursuing suspects is important but very challenging due to its mission and safety-critical nature. While multi-agent reinforcement learning (MARL) algorithms have been proposed for MVP in structured grid-pattern roads, the existing algorithms use random training samples in centralized learning, which leads to homogeneous agents showing low collaboration performance. For the more challenging problem of pursuing multiple evaders, these algorithms typically select a fixed target evader for pursuers without considering dynamic traffic situation, which significantly reduces pursuing success rate. To address the above problems, this paper proposes a Progression Cognition Reinforcement Learning with Prioritized Experience for MVP (PEPCRL-MVP) in urban multi-intersection dynamic traffic scenes. PEPCRL-MVP uses a prioritization network to assess the transitions in the global experience replay buffer according to each MARL agent’s parameters. With the personalized and prioritized experience set selected via the prioritization network, diversity is introduced to the MARL learning process, which can improve collaboration and task-related performance. Furthermore, PEPCRL-MVP employs an attention module to extract critical features from dynamic urban traffic environments. These features are used to develop a progression cognition method to adaptively group pursuing vehicles. Each group efficiently targets one evading vehicle. Extensive experiments conducted with a simulator over unstructured roads of an urban area show that PEPCRL-MVP is superior to other state-of-the-art methods. Specifically, PEPCRL-MVP improves pursuing efficiency by 3.95 % over Twin Delayed Deep Deterministic policy gradient-Decentralized Multi-Agent Pursuit and its success rate is 34.78 % higher than that of Multi-Agent Deep Deterministic Policy Gradient. Codes are open-sourced

Fast Green FCF Attenuates Lipopolysaccharide-Induced Depressive-Like Behavior and Downregulates TLR4/Myd88/NF-κB Signal Pathway in the Mouse Hippocampus

Depression is a common neuropsychiatric disorder and new anti-depressive treatments are still in urgent demand. Fast Green FCF, a safe biocompatible color additive, has been suggested to mitigate chronic pain. However, Fast green FCF’s effect on depression is unknown. We aimed to investigate Fast green FCF’s effect on lipopolysaccharide (LPS)-induced depressive-like behavior and the underlying mechanisms. Pretreatment of Fast green FCF (100 mg/kg, i.p. daily for 7 days) alleviated depressive-like behavior in LPS-treated mice. Fast green FCF suppressed the LPS-induced microglial and astrocyte activation in the hippocampus. Fast green FCF decreased the mRNA and protein levels of Toll-like receptor 4 (TLR4) and Myeloid differentiation primary response 88 (Myd88) and suppressed the phosphorylation of nuclear factor-κB (NF-κB) in the hippocampus of LPS-treated mice. Fast green FCF also downregulated hippocampal tumor necrosis factor (TNF)-α, interleukin (IL)-1β, and IL-6, but did not alter the level of the brain-derived neurotrophic factor (BDNF) in the hippocampus of LPS-treated mice. The molecular docking simulation predicts that Fast green FCF may interact with TLR4 and interrupt the formation of the TLR4-MD2 complex. In conclusion, the anti-depressive action of Fast green FCF in LPS-treated mice may involve the suppression of neuroinflammation and the downregulation of TLR4/Myd88/NF-κB signal pathway in mouse hippocampus. Our findings indicate the potential of Fast green FCF for controlling depressive symptoms

LATE-NC staging in routine neuropathologic diagnosis : an update

An international consensus report in 2019 recommended a classification system for limbic-predominant age-related TDP-43 encephalopathy neuropathologic changes (LATE-NC). The suggested neuropathologic staging system and nomenclature have proven useful for autopsy practice and dementia research. However, some issues remain unresolved, such as cases with unusual features that do not fit with current diagnostic categories. The goal of this report is to update the neuropathologic criteria for the diagnosis and staging of LATE-NC, based primarily on published data. We provide practical suggestions about how to integrate available genetic information and comorbid pathologies [e.g., Alzheimer's disease neuropathologic changes (ADNC) and Lewy body disease]. We also describe recent research findings that have enabled more precise guidance on how to differentiate LATE-NC from other subtypes of TDP-43 pathology [e.g., frontotemporal lobar degeneration (FTLD) and amyotrophic lateral sclerosis (ALS)], and how to render diagnoses in unusual situations in which TDP-43 pathology does not follow the staging scheme proposed in 2019. Specific recommendations are also made on when not to apply this diagnostic term based on current knowledge. Neuroanatomical regions of interest in LATE-NC are described in detail and the implications for TDP-43 immunohistochemical results are specified more precisely. We also highlight questions that remain unresolved and areas needing additional study. In summary, the current work lays out a number of recommendations to improve the precision of LATE-NC staging based on published reports and diagnostic experience.Peer reviewe

Metamodeling methods and their direct methanol fuel cell applications

Direct methanol fuel cells (DMFCs) have emerged in recent years as potential power sources for portable electronic devices due to the high energy density of methanol and low power requirements of the portable electronic devices. Fuel cell system modeling plays an important role in the design of DMFC systems. Despite the progress in modeling of DMFCs, most of these models considered only some of the key operating parameters with overly simplified geometric shapes. In addition, since extensive simulations are usually required in design and control of DMFC systems, advanced modeling tools with high computation quality and efficiency are expected. This research focuses on study of adaptive metamodeling methods and applications of these methods in modeling and design of DMFC systems. A semi-empirical model is developed to build the relationships between all important operating parameters and DMFC performance measures. Coefficients of this semi-empirical model are obtained through experiments and data fitting. The semi-empirical model provides a basis to identify the optimal operating parameters of the DMFC system considering different power requirements. In addition, adaptive metamodeling has been employed to describe the electrochemical relationships in a computational fluid dynamics (CFD) based DMFC model to study influences of both geometric parameters and operating parameters on DMFC performance. The CFD-based DMFC model can be used in optimal design of geometric parameters and optimal control of operating parameters. Metamodeling methods, which were initially developed as “surrogates” of the expensive simulation process, can be used to model the relationship between input and output parameters in DMFC systems. Influences of two factors, noise level and initial sample size, on quality of adaptive metamodeling considering different metamodel schemes and test functions are studied in this work. Guidelines have been developed for selection of the proper adaptive metamodeling methods. In addition, a new sampling method namely weighted sequential sampling (WSS) method is introduced in this research to improve the accuracy of adaptive metamodeling considering influences of sample quality measures in both input and output parameter spaces. Quality of the global optimization can be improved based on the metamodel built using the WSS method

Collaborative Design for Uneven Physical Structures of Multi-Layers in PEMFC

A collaborative design for the uneven distributions of a flow channel, gas diffusion layer porosity and catalyst layer porosity are newly proposed to improve the utilization ratio of the membrane electrode assembly of the proton exchange membrane fuel cell. The effects of the uneven design of the rib width and of the uneven porosity parameters of the cathode and anode gas diffusion layer and catalyst layer on the fuel cell performance were studied in detail. Numerical simulations were designed and implemented for validation. The results show that the fuel cell performance could be improved through the collaborative design of uneven distributions for different layers. The rib width gradually decreasing and the porosity of the cathode gas diffusion layer and the cathode catalyst layer gradually increasing along the fluid flow direction would contribute to a better design compared to the regular even design. The new uneven design can make the fuel penetrate into the catalyst layer in time to participate in the reaction, improve the utilization rate of the membrane electrode assembly, and greatly improve the performance of the fuel cell

A Tent Lévy Flying Sparrow Search Algorithm for Wrapper-Based Feature Selection: A COVID-19 Case Study

The “Curse of Dimensionality” induced by the rapid development of information science might have a negative impact when dealing with big datasets, and it also makes the problems of symmetry and asymmetry increasingly prominent. Feature selection (FS) can eliminate irrelevant information in big data and improve accuracy. As a recently proposed algorithm, the Sparrow Search Algorithm (SSA) shows its advantages in the FS tasks because of its superior performance. However, SSA is more subject to the population’s poor diversity and falls into a local optimum. Regarding this issue, we propose a variant of the SSA called the Tent Lévy Flying Sparrow Search Algorithm (TFSSA) to select the best subset of features in the wrapper-based method for classification purposes. After the performance results are evaluated on the CEC2020 test suite, TFSSA is used to select the best feature combination to maximize classification accuracy and simultaneously minimize the number of selected features. To evaluate the proposed TFSSA, we have conducted experiments on twenty-one datasets from the UCI repository to compare with nine algorithms in the literature. Nine metrics are used to evaluate and compare these algorithms’ performance properly. Furthermore, the method is also used on the coronavirus disease (COVID-19) dataset, and its classification accuracy and the average number of feature selections are 93.47% and 2.1, respectively, reaching the best. The experimental results and comparison in all datasets demonstrate the effectiveness of our new algorithm, TFSSA, compared with other wrapper-based algorithms

A New Conformable Fractional-Order Time-Delay Grey Bernoulli Model with the Arithmetic Optimization Algorithm and Its Application in Rural Regional Economy

To further promote the development of the grey system theory, this paper develops a novel conformable fractional-order grey Bernoulli model with a time-delay effect, namely, the CFTDNGBM (1, 1) model. In addition, the arithmetic optimization algorithm (AOA) is incorporated into the system of the model to solve the hyperparameters existing in the model. Compared with the previous grey prediction models, the CFTDNGBM (1, 1) model with a conformable fractional-order accumulation operation (CFAO), time-delay factor, and Bernoulli parameter has stronger compatibility in structure. The proposed model and its nine competitive models with excellent performance are used to predict and analyze the consumption level and per capita consumption expenditure of rural residents in China to verify the feasibility of the proposed method. The case results show that in both cases, the seven descriptive indicators of the CFTDNGBM (1, 1) model are higher than those of its competing models. Therefore, the CFTDNGBM (1, 1) model has a certain application value