Protective effect of Solanum Nigrum Linn green fruit ethanolic extract on alcoholic liver injury in mice

Alcoholic liver injury is a liver disease caused by excessive alcohol consumption,which can lead to chronic liver disease death.Solanum Nigrum Linn taste bitter,cold,has the effect of clearing heat and detoxification,promoting blood and detumescence.Solanum Nigrum Linn fruit contains a variety of antioxidant enzymes,can remove the body produced by aerobic metabolism harmful substances.In this paper,a model of alcohol-induced liver injury in C57BL/6 mice was established to evaluate the protective effect of Solanum Nigrum Linn green fruit(SNGF)ethanolic extract on alcohol-induced liver injury.H&E staining and oil red O(ORO)staining showed that hepatic lobules were clearly demarcated,vacuoles were significantly reduced and lipid droplets were reduced in SNGF ethanolic extract treatment group.Serum levels of TC,TG,LDH,TBA,AKP,ALT and AST were decreased in the SNGF ethanolic extract treatment group,and SNGF ethanolic extract could clear reactive oxygen species(ROS)in time.MDA content was signifi cantly decreased after SNGF ethanolic extract treatment,while superoxide dismutase(SOD)and GSH-Px contents were increased after SNGF ethanolic extract treatment.These results suggest that SNGF ethanolic extract has a protective effect on alcohol-induced liver injury.

Characters of Bio-organic Fertilizer and Trails on Its Effect When Applied to Peach

[Objective] This study aimed to investigate the characteristics of bio-organic fertilizer and its effect when applied to peach.[Method] Through launching demonstration trial on the application of bio-organic fertilizer in the major fruit producing areas in Liaoning Province,the effects of bio-organic fertilizer on peach growth and soil were investigated.[Results] After application of bio-organic fertilizer,both the peach yield and fruit quality were improved to some extent,of which yields was increased by 16.4％ compared with the control,and vitamin C and total sugar contents were also significantly increased; application of bio-organic fertilizer also improved the contents of total nitrogen,rapidly available phosphorus,available potassium and organic matter in soil,and reduced the soil volume weight.[Conclusion] Bioorganic fertilizer can significantly improve fruit yield and quality,as well as improving orchard soil and protecting the environment,thus possessing a bright application prospect in the production of green fruits.

On Construction of Ecological Economic Forests

To consolidate the results of returning cukivated land into forests and realize sustainable development, it is an important way to develop ecological economic forests at a reasonable scale. At the same time, with the improving quality of people＇s lives, we must pay attention to production of pollution-free green fruits. This paper, from the point of ecological economics, takes the example of date trees for example, presenting the connotation of ecological economic forest and main construction technologies, in order to provide basis for the industrial development after returning cultivated land into forests.

An adaptive segmentation method combining MSRCR and mean shift algorithm with K-means correction of green apples in natural environment

During the recognition and localization process of green apple targets,problems such as uneven illumination,occlusion of branches and leaves need to be solved.In this study,the multi-scale Retinex with color restoration(MSRCR)algorithm was applied to enhance the original green apple images captured in an orchard environment,aiming to minimize the impacts of varying light conditions.The enhanced images were then explicitly segmented using the mean shift algorithm,leading to a consistent gray value of the internal pixels in an independent fruit.After that,the fuzzy attention based on information maximization algorithm(FAIM)was developed to detect the incomplete growth position and realize threshold segmentation.Finally,the poorly segmented images were corrected using the K-means algorithm according to the shape,color and texture features.The users intuitively acquire the minimum enclosing rectangle localization results on a PC.A total of 500 green apple images were tested in this study.Compared with the manifold ranking algorithm,the K-means clustering algorithm and the traditional mean shift algorithm,the segmentation accuracy of the proposed method was 86.67%,which was 13.32%,19.82%and 9.23%higher than that of the other three algorithms,respectively.Additionally,the false positive and false negative errors were 0.58%and 11.64%,respectively,which were all lower than the other three compared algorithms.The proposed method accurately recognized the green apples under complex illumination conditions and growth environments.Additionally,it provided effective references for intelligent growth monitoring and yield estimation of fruits.

Automatic tracking of multi-fruit targets with oscillation under natural growth conditions

It is important for intelligent orchards to be able to achieve automatic monitoring of fruit growth information within a natural growing environment.The issue of how to track green and oscillating fruits under the influence of wind and farming operations is a key aspect of monitoring of the growth state of the fruit.In order to realize the accurate tracking of green fruit targets,a new method based on target tracking is proposed.First,an optical flow method is applied to realize the automatic detection of green fruit targets,and this lays the foundation for the accurate and automatic tracking of these targets.Then,Kalman and kernelized correlation filter(KCF)algorithms are applied to achieve multi-target tracking and prediction.In order to verify the performance of these different algorithms on various types of green fruit targets,experiments were carried out based on nine video sequences.The experimental results for the tracking of single,double and triple green fruit targets show that the average tracking success rates of the Kalman algorithm are 88.15%,82.30%and 53.10%,respectively,and those of the KCF algorithm are 94.07%,87.35%and 61.46%,respectively,meaning that the average tracking results from KCF are 5.92%,5.05%and 8.36%higher than those from the Kalman algorithm.The time consumed is also reduced by 35.40%,36.27%and 40.86%,respectively.The results show that it is feasible to apply the KCF algorithm to the tracking of green fruit targets.