Dietary Flaxseed Oil Prevents Western-Type Diet-Induced Nonalcoholic Fatty Liver Disease in Apolipoprotein-E Knockout Mice

The prevalence of nonalcoholic fatty liver disease (NAFLD) has dramatically increased globally during recent decades. Intake of n-3 polyunsaturated fatty acids (PUFAs), mainly eicosapentaenoic acid (EPA, C20:5n-3) and docosahexaenoic acid (DHA, C22:6n-3), is believed to be beneficial to the development of NAFLD. However, little information is available with regard to the effect of flaxseed oil rich in α-linolenic acid (ALA, C18:3n-3), a plant-derived n-3 PUFA, in improving NAFLD. This study was to gain the effect of flaxseed oil on NAFLD and further investigate the underlying mechanisms. Apolipoprotein-E knockout (apoE-KO) mice were given a normal chow diet, a western-type high-fat and high-cholesterol diet (WTD), or a WTD diet containing 10% flaxseed oil (WTD + FO) for 12 weeks. Our data showed that consumption of flaxseed oil significantly improved WTD-induced NAFLD, as well as ameliorated impaired lipid homeostasis, attenuated oxidative stress, and inhibited inflammation. These data were associated with the modification effects on expression levels of genes involved in de novo fat synthesis (SREBP-1c, ACC), triacylglycerol catabolism (PPARα, CPT1A, and ACOX1), inflammation (NF-κB, IL-6, TNF-α, and MCP-1), and oxidative stress (ROS, MDA, GSH, and SOD)

Optimizing Cold Chain Distribution Routes Considering Dynamic Demand: A Low-Emission Perspective

Cold chain logistics, with its high carbon emissions and energy consumption, contradicts the current advocacy for a “low-carbon economy”. Additionally, in the real delivery process, customers often generate dynamic demand, which has the characteristic of being sudden. Therefore, to help cold chain distribution companies achieve energy-saving and emission-reduction goals while also being able to respond quickly to customer needs, this article starts from a low-carbon perspective and constructs a two-stage vehicle distribution route optimization model that minimizes transportation costs and refrigeration costs, alongside carbon emissions costs. This research serves to minimize the above-mentioned costs while also ensuring a quick response to customer demands and achieving the goals of energy conservation and emission reduction. During the static stage, in order to determine the vehicle distribution scheme, an enhanced genetic algorithm is adopted. During the dynamic optimization stage, a strategy of updating key time points is employed to address the dynamic demand from customers. By comparing the dynamic optimization strategy with the strategy of dispatching additional vehicles, it is demonstrated that the presented model is capable of achieving an overall cost reduction of approximately 17.13%. Notably, carbon emission costs can be reduced by around 17.11%. This demonstrates that the dynamic optimization strategy effectively reduces the usage of distribution vehicles and lowers distribution costs