Prospect Theory Based Individual Irrationality Modelling and Behavior Inducement in Pandemic Control

It is critical to understand and model the behavior of individuals in a pandemic, as well as identify effective ways to guide people's behavior in order to better control the epidemic spread. However, current research fails to account for the impact of users' irrationality in decision-making, which is a prevalent factor in real-life scenarios. Additionally, existing disease control methods rely on measures such as mandatory isolation and assume that individuals will fully comply with these policies, which may not be true in reality. Thus, it is critical to find effective ways to guide people's behavior during an epidemic. To address these gaps, we propose a Prospect Theory-based theoretical framework to model individuals' decision-making process in an epidemic and analyze the impact of irrationality on the co-evolution of user behavior and the epidemic. Our analysis shows that irrationality can lead individuals to be more conservative when the risk of being infected is small, while irrationality tends to make users be more risk-seeking when the risk of being infected is high. We then propose a behavior inducement algorithm to guide user behavior and control the spread of disease. Simulations and real user tests validate our proposed model and analysis, and simulation results show that our proposed behavior inducement algorithm can effectively guide users' behavior

Modeling and Analysis of the Epidemic-Behavior Co-evolution Dynamics with User Irrationality

During a public health crisis like COVID-19, individuals' adoption of protective behaviors, such as self-isolation and wearing masks, can significantly impact the spread of the disease. In the meanwhile, the spread of the disease can also influence individuals' behavioral choices. Moreover, when facing uncertain losses, individuals' decisions tend to be irrational. Therefore, it is critical to study individuals' irrational behavior choices in the context of a pandemic. In this paper, we propose an epidemic-behavior co-evolution model that captures the dynamic interplay between individual decision-making and disease spread. To account for irrational decision-making, we incorporate the Prospect Theory in our individual behavior modeling. We conduct a theoretical analysis of the model, examining the steady states that emerge from the co-evolutionary process. We use simulations to validate our theoretical findings and gain further insights. This investigation aims to enhance our understanding of the complex dynamics between individual behavior and disease spread during a pandemic

The Activation of Macrophage and Upregulation of CD40 Costimulatory Molecule in Lipopolysaccharide-Induced Acute Lung Injury

To study the activation of macrophage and upregulation of costimulatory molecule of CD40 in lipopolysaccharide- (LPS-) induced acute lung injury (ALI) model, and to investigate the pathogenecy of ALI, mice were randomly divided into two groups. ALI model was created by injecting 0.2 mg/kg LPS in phosphate saline (PBS) in trachea. The pathologic changes of mice lungs were observed by HE staining at 24 and 48 hours after LPS treatment, then the alveolar septum damage, abnormal contraction, alveolar space hyperemia, and neutrophils or other inflammatory cells infiltration in the LPS group, but not in the control group, were observed. The expression of CD40 mRNA and CD40 protein molecules were higher in LPS group as compared to the control group by Northern blot and flow cytometry, respectively. Expression of Toll-like receptor-4 (TLR4) in activated macrophage (AMΦ) was higher in LPS group as compared to the control group by RT-PCR. The activation of NF-κB binding to NF-κB consensus oligos increased in LPS group by EMSA in macrophage. The concentrations of TNF-α, MIP-2, and IL-1β cytokines from bronchoalveolar lavage fluid (BALF) were increased significantly in LPS group as compared to the control group by ELISA. The activation of AM and upregulation of costimulatory molecule CD40 induced all kinds of inflammatory cytokines releasing, then led to ALI. Therefore, both of them played vital role in the process of development of ALI

Optimizing functional near-infrared spectroscopy (fNIRS) channels for schizophrenic identification during a verbal fluency task using metaheuristic algorithms

ObjectiveWe aimed to reduce the complexity of the 52-channel functional near-infrared spectroscopy (fNIRS) system to facilitate its usage in discriminating schizophrenia during a verbal fluency task (VFT).MethodsOxygenated hemoglobin signals obtained using 52-channel fNIRS from 100 patients with schizophrenia and 100 healthy controls during a VFT were collected and processed. Three features frequently used in the analysis of fNIRS signals, namely time average, functional connectivity, and wavelet, were extracted and optimized using various metaheuristic operators, i.e., genetic algorithm (GA), particle swarm optimization (PSO), and their parallel and serial hybrid algorithms. Support vector machine (SVM) was used as the classifier, and the performance was evaluated by ten-fold cross-validation.ResultsGA and GA-dominant algorithms achieved higher accuracy compared to PSO and PSO-dominant algorithms. An optimal accuracy of 87.00% using 16 channels was obtained by GA and wavelet analysis. A parallel hybrid algorithm (the best 50% individuals assigned to GA) achieved an accuracy of 86.50% with 8 channels on the time-domain feature, comparable to the reported accuracy obtained using 52 channels.ConclusionThe fNIRS system can be greatly simplified while retaining accuracy comparable to that of the 52-channel system, thus promoting its applications in the diagnosis of schizophrenia in low-resource environments. Evolutionary algorithm-dominant optimization of time-domain features is promising in this regard

The diagnostic significance of the ZNF gene family in pancreatic cancer: a bioinformatics and experimental study

Background: Pancreatic adenocarcinoma (PAAD) is among the most devastating of all cancers with a poor survival rate. Therefore, we established a zinc finger (ZNF) protein-based prognostic prediction model for PAAD patients.Methods: The RNA–seq data for PAAD were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Differentially expressed ZNF protein genes (DE-ZNFs) in PAAD and normal control tissues were screened using the “lemma” package in R. An optimal risk model and an independent prognostic value were established by univariate and multivariate Cox regression analyses. Survival analyses were performed to assess the prognostic ability of the model.Results: We constructed a ZNF family genes-related risk score model that is based on the 10 DE-ZNFs (ZNF185, PRKCI, RTP4, SERTAD2, DEF8, ZMAT1, SP110, U2AF1L4, CXXC1, and RMND5B). The risk score was found to be a significant independent prognostic factor for PAAD patients. Seven significantly differentially expressed immune cells were identified between the high- and low-risk patients. Then, based on the prognostic genes, we constructed a ceRNA regulatory network that includes 5 prognostic genes, 7 miRNAs and 35 lncRNAs. Expression analysis showed ZNF185, PRKCI and RTP4 were significantly upregulated, while ZMAT1 and CXXC1 were significantly downregulated in the PAAD samples in all TCGA - PAAD, GSE28735 and GSE15471 datasets. Moreover, the upregulation of RTP4, SERTAD2, and SP110 were verified by the cell experiments.Conclusion: We established and validated a novel, Zinc finger protein family - related prognostic risk model for patients with PAAD, that has the potential to inform patient management