LncRNA LINK-A Remodels Tissue Inflammatory Microenvironments to Promote Obesity.

High-fat diet (HFD)-induced obesity is a crucial risk factor for metabolic syndrome, mainly due to adipose tissue dysfunctions associated with it. However, the underlying mechanism remains unclear. This study has used genetic screening to identify an obesity-associated human lncRNA LINK-A as a critical molecule bridging the metabolic microenvironment and energy expenditure in vivo by establishing the HFD-induced obesity knock-in (KI) mouse model. Mechanistically, HFD LINK-A KI mice induce the infiltration of inflammatory factors, including IL-1ß and CXCL16, through the LINK-A/HB-EGF/HIF1α feedback loop axis in a self-amplified manner, thereby promoting the adipose tissue microenvironment remodeling and adaptive thermogenesis disorder, ultimately leading to obesity and insulin resistance. Notably, LINK-A expression is positively correlated with inflammatory factor expression in individuals who are overweight. Of note, targeting LINK-A via nucleic acid drug antisense oligonucleotides (ASO) attenuate HFD-induced obesity and metabolic syndrome, pointing out LINK-A as a valuable and effective therapeutic target for treating HFD-induced obesity. Briefly, the results reveale the roles of lncRNAs (such as LINK-A) in remodeling tissue inflammatory microenvironments to promote HFD-induced obesity.

Assessment of kiwifruit firmness by using airflow and laser technique.

Firmness is a valid and widely acknowledged indication of fruit quality that is directly connected to physical structure and mechanical qualities. The deformation signals of kiwifruit for firmness assessment were acquired using an assessment system based on airflow and laser technology in this investigation. Using partial least squares regression (PLSR), genetic algorithm optimization of bp neural network (GA-BP), and an extreme learning machine (ELM), deformation data from kiwifruit was used to create models of Magness-Taylor penetration firmness prediction. The ELM model outperformed the PLSR model, and GA-BP model in the prediction set, with a correlation coefficient of 0.876 and a root mean squared error of 3.576 N in the prediction set. These findings showed that an assessment system based on airflow and laser techniques can be utilized to assess the firmness of kiwifruit quickly and nondestructively.

The optimal spacing interval between principal shelterbelts of the farm-shelter forest network.

The farm-shelter forest network is a complex grid protection system, with a windbreak that is distinctly different from that of the single shelterbelt. We selected the farm-shelter forest network of a jujube field in the Tarim Basin of northwest China and used a combination of field measurements and wind tunnel tests to determine the optimal spacing interval between principal shelterbelts. The wind speed reductive curve of the farm-shelter forest network showed a gradual wind speed tendency to stability. Therefore, a model was established based on the energy transfer balance between the upper and the lower airflows for a steady wind speed. The prediction error of the model was found to be < 1%. The model results indicated that increasing the spacing interval between principal shelterbelts from 10 to 20 H, where H is the shelterbelt height, maintained more than 70% of the windbreak effect of the farm-shelter forest network. If the spacing interval between principal shelterbelts were to be increased from 10 to 20 H, the jujube planting area would be increased by 0.54%. Therefore, a thorough consideration of the windbreak effect of each shelterbelt, the synergistic effects of shelterbelts, the windbreak effects of tall crops, and the effects of temperature and humidity in farm-shelter forest networks indicates that increasing the spacing interval will not only maintain the windbreak effect, but it will also reduce the side effects of shelterbelts, increase the planting area, favor mechanized operation, and improve planting efficiency.

Prediction of tenderness of chicken by using viscoelasticity based on airflow and optical technique.

Tenderness is an index for evaluating meat quality. A prediction model of tenderness was established based on the chicken deformation, which was determined by a viscoelasticity system combined with airflow and optical technique. Different preprocessing methods were used to preprocess the deformation. The interval variables that represent the viscoelasticity of the chicken in deformation, were screen by synergy interval partial least squares algorithm (Si-PLS) and moving window partial least squares algorithm (Mw-PLS). The prediction model was established by principal component regression (PCR) and partial least squares regression (PLSR). The optimum PLSR prediction model was established when Mw-PLS was used to screen the interval variables of Savitzy-Golay (S-G) smoothing data. The correlation coefficient and the root mean square error of the calibration set were 0.965 and 0.874 kg, respectively. The corresponding value of the prediction set was 0.943 and 1.005 kg. This research provides a new method to assess the quality of poultry meat that conducts on airflow and optical techniques.