Mesenchymal Stem Cell-Derived Exosomes for Myocardial Infarction Treatment

Myocardial infarction (MI) is a major cause of morbidity and mortality in modern society. Over the past decades, mesenchymal stem cell (MSCs)-based therapy has shown promising results in the treatment of MI due to their unique properties of multi-differentiation ability, immune-privileged phenotype and paracrine activity. Recently, MSC-derived exosomes (MSC-EXO) have been proposed as a promising therapeutic strategy for MI with their ability to inhibit cardiomyocyte apoptosis and stimulate vascular angiogenesis. They also aid immunoregulation and rejuvenation of cardiomyocyte senescence by transporting their unique content such as proteins, lipids, and miRNAs. Compared with MSC transplantation, MSC-EXO administration has shown several advantages, including lower toxicity and immunogenicity and no risk of tumor formation. Nonetheless the potential mechanisms underlying MSC-EXO-based therapy for MI are not fully understood. In addition, lack of modification of MSC-EXOs can impact therapeutic efficacy. It is vital to optimize MSC-EXO and enhance their therapeutic efficacy for MI. We summarize the recent advances regarding biological characteristics, therapeutic potential and mechanisms, and optimal approaches to the use of MSC-EXOs in the treatment of MI

A compendium of genetic regulatory effects across pig tissues

The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.</p

A Novel Improved Genetic Algorithm for Multi-Period Fractional Programming Portfolio Optimization Model in Fuzzy Environment

The complexity of historical data in financial markets and the uncertainty of the future, as well as the idea that investors always expect the least risk and the greatest return. This study presents a multi-period fractional portfolio model in a fuzzy environment, taking into account the limitations of asset quantity, asset position, transaction cost, and inter-period investment. This is a mixed integer programming NP-hard problem. To overcome the problem, an improved genetic algorithm (IGA) is presented. The IGA contribution mostly involves the following three points: (i) A cardinal constraint processing approach is presented for the cardinal constraint conditions in the model; (ii) Logistic chaotic mapping was implemented to boost the initial population diversity; (iii) An adaptive golden section variation probability formula is developed to strike the right balance between exploration and development. To test the model’s logic and the performance of the proposed algorithm, this study picks stock data from the Shanghai Stock Exchange 50 for simulated investing and examines portfolio strategies under various limitations. In addition, the numerical results of simulated investment are compared and analyzed, and the results show that the established models are in line with the actual market situation and the designed algorithm is effective, and the probability of obtaining the optimal value is more than 37.5% higher than other optimization algorithms

A Hybrid PSO-DE Intelligent Algorithm for Solving Constrained Optimization Problems Based on Feasibility Rules

In this paper, we study swarm intelligence computation for constrained optimization problems and propose a new hybrid PSO-DE algorithm based on feasibility rules. Establishing individual feasibility rules as a way to determine whether the position of an individual satisfies the constraint or violates the degree of the constraint, which will determine the choice of the individual optimal position and the global optimal position in the particle population. First, particle swarm optimization (PSO) is used to act on the top 50% of individuals with higher degree of constraint violation to update their velocity and position. Second, Differential Evolution (DE) is applied to act on the individual optimal position of each individual to form a new population. The current individual optimal position and the global optimal position are updated using the feasibility rules, thus forming a hybrid PSO-DE intelligent algorithm. Analyzing the convergence and complexity of PSO-DE. Finally, the performance of the PSO-DE algorithm is tested with 12 benchmark functions of constrained optimization and 57 engineering optimization problems, the numerical results show that the proposed algorithm has good accuracy, effectiveness and robustness

The pro-environmental behavioral intention of villagers in rural tourist destinations under China’s environmental remediation policy

Abstract This study examined villagers’ intention of pro-environmental behavior while supporting the Landcare Policy in China. The research team conducted field surveys of villagers from four famous scenic spots of Cili, which is near the world natural heritage site of the Zhangjiajie natural landscape core area. This area has developed rural tourism, many local villagers rely on tourism to obtain their livelihood income. However, the area is now affected by the environmental remediation policy called Landcare Policy. Cultivated land near the tourist area needs to be repaired, which affects the tourism income of some local villagers. Therefore, local villagers are facing a contradiction between tourism development and environmental protection. The study chose the change in local villagers’ pro-environmental intention as the research content. Then we adopted an empirically validated norm activation model (NAM) from Schwartz, and merged the NAM with the expectancy theory of Vroom, based on 511 valid responses from the field questionnaire surveys, we aimed to develop a theoretical framework for researchers to understand the change in villagers' pro-environmental behaviors, concerning the balance between rural tourism livelihood benefits and environmental remediation behavior. Structural equation modeling was conducted for each index of the responses, the findings showed that the merged model had 76.46% better predictive accuracy of villagers’ pro-environmental intentions than applying Schwartz’s NAM independently. This study found that the motivational force of this new theory significantly influences environmental personal norms due to the joint impact of valence, instrumentality, and expectancy. Villagers with a positive pro-environmental behavior intention expect good tourism benefits and environmental living conditions under the impact of the Landcare policy in rural tourism destinations near the famous natural heritage site

A Relaxed and Bound Algorithm Based on Auxiliary Variables for Quadratically Constrained Quadratic Programming Problem

Quadratically constrained quadratic programs (QCQP), which often appear in engineering practice and management science, and other fields, are investigated in this paper. By introducing appropriate auxiliary variables, QCQP can be transformed into its equivalent problem (EP) with non-linear equality constraints. After these equality constraints are relaxed, a series of linear relaxation subproblems with auxiliary variables and bound constraints are generated, which can determine the effective lower bound of the global optimal value of QCQP. To enhance the compactness of sub-rectangles and improve the ability to remove sub-rectangles, two rectangle-reduction strategies are employed. Besides, two ϵ-subproblem deletion rules are introduced to improve the convergence speed of the algorithm. Therefore, a relaxation and bound algorithm based on auxiliary variables are proposed to solve QCQP. Numerical experiments show that this algorithm is effective and feasible

Multi-Identity Recognition of Darknet Vendors Based on Metric Learning

Dark web vendor identification can be seen as an authorship aliasing problem, aiming to determine whether different accounts on different markets belong to the same real-world vendor, in order to locate cybercriminals involved in dark web market transactions. Existing open-source datasets for dark web marketplaces are outdated and cannot simulate real-world situations, while data labeling methods are difficult and suffer from issues such as inaccurate labeling and limited cross-market research. The problem of identifying vendors’ multiple identities on the dark web involves a large number of categories and a limited number of samples, making it difficult to use traditional multiclass classification models. To address these issues, this paper proposes a metric learning-based method for dark web vendor identification, collecting product data from 21 currently active English dark web marketplaces and using a multi-dimensional feature extraction method based on product titles, descriptions, and images. Using pseudo-labeling technology combined with manual labeling improves data labeling accuracy compared to previous labeling methods. The proposed method uses a Siamese neural network with metric learning to learn the similarity between vendors and achieve the recognition of vendors’ multiple identities. This method achieved better performance with an average F1-score of 0.889 and an accuracy rate of 97.535% on the constructed dataset. The contributions of this paper lie in the proposed method for collecting and labeling data for dark web marketplaces and overcoming the limitations of traditional multiclass classifiers to achieve effective recognition of vendors’ multiple identities

Research on Extension Design of Emergency Cold Chain Logistics from the Perspective of Carbon Constraints

Extenics has unique advantages in solving contradictions by using formal models to explore the possibility of expanding things and the laws and methods of development and innovation. This paper studies the specific application of the extension strategy generation method in emergency cold chain logistics, in order to solve the problem that the emergency plan is difficult to cover in the face of an emergency. The purpose of this paper is to provide ideas for the generation of strategies to solve the contradictions of cold chain logistics in complex emergency scenarios. Giving full play to the unique advantages of extenics in solving contradictory problems, this paper analyzes the core problems, objectives and conditions of emergency cold chain logistics in four links with the case scenario of the COVID-19 pandemic outbreak, extends and generates 10 measures to form 36 schemes, and evaluates the combination schemes quantitatively and objectively using the dependent function and superiority evaluation formula. In addition, the consideration of carbon constraints is added to the selection of the scheme, and the specific plan of integrating e-commerce platform, expert guidance, establishing temporary cold storage transfer and contactless distribution is designed. The research results provide support for meeting the needs of emergency logistics schemes in different situations and optimizing the energy efficiency of the scheme while ensuring humanitarian support. At the same time, the application of extenics basic-element formal language also provides a reference for further applying artificial intelligence to the design of emergency logistics schemes