An Empirical Analysis of Trust, Perceived Benefit, and Purchase Intention in C2C Electronic Commerce in China

With the popularity and development of the internet, China's consumer-to-consumer (C2C) electronic commerce (EC) system is favored by consumers. Therefore, understanding the relationship among consumers' trust, perceived benefits (PBs), and purchase intentions (PIs) is of great significance for studying this system. This article proposes the hypothesis of the interaction among the three and designs a questionnaire to explain the application of trust, PB, and PI in China's C2C EC system. The relationship between the sample structure and the variables is analyzed using reliability, validity, correlation, and regression analysis (RA). The experimental analysis results show that the questionnaire's reliability and validity values are higher than 0.8 and 0.75, respectively, indicating that the questionnaire design is qualified, and the data are valid. The rationality of the hypothesis proposed here is verified through correlation analysis and RA. This indicates a significant mutual influence relationship among trust, PB, and PI

Contrastive self-supervised representation learning without negative samples for multimodal human action recognition

Action recognition is an important component of human-computer interaction, and multimodal feature representation and learning methods can be used to improve recognition performance due to the interrelation and complementarity between different modalities. However, due to the lack of large-scale labeled samples, the performance of existing ConvNets-based methods are severely constrained. In this paper, a novel and effective multi-modal feature representation and contrastive self-supervised learning framework is proposed to improve the action recognition performance of models and the generalization ability of application scenarios. The proposed recognition framework employs weight sharing between two branches and does not require negative samples, which could effectively learn useful feature representations by using multimodal unlabeled data, e.g., skeleton sequence and inertial measurement unit signal (IMU). The extensive experiments are conducted on two benchmarks: UTD-MHAD and MMAct, and the results show that our proposed recognition framework outperforms both unimodal and multimodal baselines in action retrieval, semi-supervised learning, and zero-shot learning scenarios

Thyroid function and epilepsy: a two-sample Mendelian randomization study

BackgroundThyroid hormones (THs) play a crucial role in regulating various biological processes, particularly the normal development and functioning of the central nervous system (CNS). Epilepsy is a prevalent neurological disorder with multiple etiologies. Further in-depth research on the role of thyroid hormones in epilepsy is warranted.MethodsGenome-wide association study (GWAS) data for thyroid function and epilepsy were obtained from the ThyroidOmics Consortium and the International League Against Epilepsy (ILAE) Consortium cohort, respectively. A total of five indicators of thyroid function and ten types of epilepsy were included in the analysis. Two-sample Mendelian randomization (MR) analyses were conducted to investigate potential causal relations between thyroid functions and various epilepsies. Multiple testing correction was performed using Bonferroni correction. Heterogeneity was calculated with the Cochran’s Q statistic test. Horizontal pleiotropy was evaluated by the MR-Egger regression intercept. The sensitivity was also examined by leave-one-out strategy.ResultsThe findings indicated the absence of any causal relationship between abnormalities in thyroid hormone and various types of epilepsy. The study analyzed the odds ratio (OR) between thyroid hormones and various types of epilepsy in five scenarios, including free thyroxine (FT4) on focal epilepsy with hippocampal sclerosis (IVW, OR = 0.9838, p = 0.02223), hyperthyroidism on juvenile absence epilepsy (IVW, OR = 0.9952, p = 0.03777), hypothyroidism on focal epilepsy with hippocampal sclerosis (IVW, OR = 1.0075, p = 0.01951), autoimmune thyroid diseases (AITDs) on generalized epilepsy in all documented cases (weighted mode, OR = 1.0846, p = 0.0346) and on childhood absence epilepsy (IVW, OR = 1.0050, p = 0.04555). After Bonferroni correction, none of the above results showed statistically significant differences.ConclusionThis study indicates that there is no causal relationship between thyroid-related disorders and various types of epilepsy. Future research should aim to avoid potential confounding factors that might impact the study

Exploring the Fecal Microbial Composition and Metagenomic Functional Capacities Associated With Feed Efficiency in Commercial DLY Pigs

Gut microbiota has indispensable roles in nutrient digestion and energy harvesting, especially in processing the indigestible components of dietary polysaccharides. Searching for the microbial taxa and functional capacity of the gut microbiome associated with feed efficiency (FE) can provide important knowledge to increase profitability and sustainability of the swine industry. In the current study, we performed a comparative analysis of the fecal microbiota in 50 commercial Duroc × (Landrace × Yorkshire) (DLY) pigs with polarizing FE using 16S rRNA gene sequencing and shotgun metagenomic sequencing. There was a different microbial community structure in the fecal microbiota of pigs with different FE. Random forest analysis identified 24 operational taxonomic units (OTUs) as potential biomarkers to improve swine FE. Multiple comparison analysis detected 8 OTUs with a significant difference or tendency toward a difference between high- and low-FE pigs (P < 0.01, q < 0.1). The high-FE pigs had a greater abundance of OTUs that were from the Lachnospiraceae and Prevotellaceae families and the Escherichia-Shigella and Streptococcus genera than low-FE pigs. A sub-species Streptococcus gallolyticus subsp. gallolyticus could be an important candidate for improving FE. The functional capacity analysis found 18 KEGG pathways and CAZy EC activities that were different between high- and low-FE pigs. The fecal microbiota in high FE pigs have greater functional capacity to degrade dietary cellulose, polysaccharides, and protein and may have a greater abundance of microbes that can promote intestinal health. These results provided insights for improving porcine FE through modulating the gut microbiome

Fumarylacetoacetate Hydrolase Knock-out Rabbit Model for Hereditary Tyrosinemia Type 1.

Hereditary tyrosinemia type 1 (HT1) is a severe human autosomal recessive disorder caused by the deficiency of fumarylacetoacetate hydroxylase (FAH), an enzyme catalyzing the last step in the tyrosine degradation pathway. Lack of FAH causes accumulation of toxic metabolites (fumarylacetoacetate and succinylacetone) in blood and tissues, ultimately resulting in severe liver and kidney damage with onset that ranges from infancy to adolescence. This tissue damage is lethal but can be controlled by administration of 2-(2-nitro-4-trifluoromethylbenzoyl)-1,3-cyclohexanedione (NTBC), which inhibits tyrosine catabolism upstream of the generation of fumarylacetoacetate and succinylacetone. Notably, in animals lacking FAH, transient withdrawal of NTBC can be used to induce liver damage and a concomitant regenerative response that stimulates the growth of healthy hepatocytes. Among other things, this model has raised tremendous interest for the in vivo expansion of human primary hepatocytes inside these animals and for exploring experimental gene therapy and cell-based therapies. Here, we report the generation of FAH knock-out rabbits via pronuclear stage embryo microinjection of transcription activator-like effector nucleases. FAH-/- rabbits exhibit phenotypic features of HT1 including liver and kidney abnormalities but additionally develop frequent ocular manifestations likely caused by local accumulation of tyrosine upon NTBC administration. We also show that allogeneic transplantation of wild-type rabbit primary hepatocytes into FAH-/- rabbits enables highly efficient liver repopulation and prevents liver insufficiency and death. Because of significant advantages over rodents and their ease of breeding, maintenance, and manipulation compared with larger animals including pigs, FAH-/- rabbits are an attractive alternative for modeling the consequences of HT1.Wellcome Trus

Genome Editing of Pigs for Agriculture and Biomedicine

Pigs serve as an important agricultural resource and animal model in biomedical studies. Efficient and precise modification of pig genome by using recently developed gene editing tools has significantly broadened the application of pig models in various research areas. The three types of site-specific nucleases, namely, zinc-finger nucleases, transcription activator-like effector nucleases, and clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein, are the main gene editing tools that can efficiently introduce predetermined modifications, including knockouts and knockins, into the pig genome. These modifications can confer desired phenotypes to pigs to improve production traits, such as optimal meat production, enhanced feed digestibility, and disease resistance. Besides, given their genetic, anatomic, and physiologic similarities to humans, pigs can also be modified to model human diseases or to serve as an organ source for xenotransplantation to save human lives. To date, many genetically modified pig models with agricultural or biomedical values have been established by using gene editing tools. These pig models are expected to accelerate research progress in related fields and benefit humans

Modulation of cell cycle increases CRISPR-mediated homology-directed DNA repair

Abstract Background Gene knock‐in (KI) in animal cells via homology‐directed repair (HDR) is an inefficient process, requiring a laborious work for screening from few modified cells. HDR tends to occur in the S and G2/M phases of cell cycle; therefore, strategies that enhance the proportion of cells in these specific phases could improve HDR efficiency. Results We used various types of cell cycle inhibitors to synchronize the cell cycle in S and G2/M phases in order to investigate their effect on regulating CRISPR/Cas9-mediated HDR. Our results indicated that the four small molecules—docetaxel, irinotecan, nocodazole and mitomycin C—promoted CRISPR/Cas9-mediated KI with different homologous donor types in various animal cells. Moreover, the small molecule inhibitors enhanced KI in animal embryos. Molecular analysis identified common signal pathways activated during crosstalk between cell cycle and DNA repair. Synchronization of the cell cycle in the S and G2/M phases results in CDK1/CCNB1 protein accumulation, which can initiate the HDR process by activating HDR factors to facilitate effective end resection of CRISPR-cleaved double-strand breaks. We have demonstrated that augmenting protein levels of factors associated with the cell cycle via overexpression can facilitate KI in animal cells, consistent with the effect of small molecules. Conclusion Small molecules that induce cell cycle synchronization in S and G2/M phases promote CRISPR/Cas9-mediated HDR efficiency in animal cells and embryos. Our research reveals the common molecular mechanisms that bridge cell cycle progression and HDR activity, which will inform further work to use HDR as an effective tool for preparing genetically modified animals or for gene therapy

A Static Control Algorithm for Adaptive Beam String Structures Based on Minimal Displacement

The beam string structure (BSS) is a type of prestressed structure and has been widely used in large span structures nowadays. The adaptive BSS is a typical smart structure that can optimize the working status itself by controlling the length of active struts via certain control device. The control device commonly consists of actuators in all struts and sensors on the beam. The key point of the control process is to determine the length adjustment values of actuators according to the data obtained by preinstalled sensors. In this paper, a static control algorithm for adaptive BSS has been presented for the adjustment solution. To begin with, an optimization model of adaptive BSS with multiple active struts is established, which uses a sensitivity analysis method. Next, a linear displacement control process is presented, and the adjustment values of struts are calculated by a simulated annealing algorithm. A nonlinear iteration procedure is used afterwards to calibrate the results of linear calculation. Finally, an example of adaptive BSS under different external loads is carried out to verify the feasibility and accuracy of the algorithm. And the results also show that the adaptive BSS has much better adaptivity and capability than the noncontrolled BSS

A Static Control Algorithm for Adaptive Beam String Structures Based on Minimal Displacement

The beam string structure (BSS) is a type of prestressed structure and has been widely used in large span structures nowadays. The adaptive BSS is a typical smart structure that can optimize the working status itself by controlling the length of active struts via certain control device. The control device commonly consists of actuators in all struts and sensors on the beam. The key point of the control process is to determine the length adjustment values of actuators according to the data obtained by preinstalled sensors. In this paper, a static control algorithm for adaptive BSS has been presented for the adjustment solution. To begin with, an optimization model of adaptive BSS with multiple active struts is established, which uses a sensitivity analysis method. Next, a linear displacement control process is presented, and the adjustment values of struts are calculated by a simulated annealing algorithm. A nonlinear iteration procedure is used afterwards to calibrate the results of linear calculation. Finally, an example of adaptive BSS under different external loads is carried out to verify the feasibility and accuracy of the algorithm. And the results also show that the adaptive BSS has much better adaptivity and capability than the noncontrolled BSS

Robust Day-Ahead Dispatch for Integrated Power-Heat-Gas Microgrid considering Wind Power Uncertainty

Wind power generation has been widely deployed in the modern power system due to the issues of energy crisis and environment pollution. Meanwhile, the microgrid is gradually regarded as a feasible way to connect and accommodate the distributed wind power generations. Recently, more research studies also focus on incorporating various energy systems, for example, heat and gas into the microgrid in terms of satisfying different types of load demands. However, the uncertainty of wind power significantly impacts the economy of the integrated power-heat-gas microgrid. To deal with this issue, this paper presents a two-stage robust model to achieve the optimal day-ahead economic dispatch strategy considering the worst-case wind power scenarios. The first stage makes the initial day-ahead dispatch decision before the observation of uncertain wind power. The additional adjustment action is made in the second stage once the wind power uncertainty is observed. Based on the duality theory and Big-M approach, the original second-stage problem can be dualized and linearized. Therefore, the column-and-constraint generation algorithm can be further implemented to achieve the optimal day-ahead economic dispatch strategy for the integrated power-heat-gas microgrid. The experimental results indicate the effectiveness of the presented approach for achieving operation cost reduction and promoting wind power utilization. The robustness and the economy of the two-stage robust model can be balanced, of which the performances significantly outperform those of the single-stage robust model and the deterministic model