To investigate the inner mechanisms of meat quality differences between high-quality (HQ) and low-quality (LQ), a comparative quantitative study between longissimus thoracis and external intercostals of goat muscle was performed from proteome to phosphorylated proteome using RP-HPLC in combination with the ‘isobaric tag’ for relative and absolute quantitation (iTRAQ) labelling strategy. Altogether, 1441 proteins were identified in our study, of which 673 were phosphoproteins, and a total of twenty were common differentially expressed proteins. Myosin, carbonic anhydrase, and phosphoglucomutase could be used as proteins marker for HQ and LQ meat. Bioinformatics analysis showed that these proteins exhibited different rates for glycolysis and oxidative phosphorylation reaction, thus causing the different pH and NADH change rates, and resulting in better colour, tenderness, and water retention in HQ meat. The release of Ca2+ and adenosine triphosphate changed the meat quality through calcium signalling. Our finding provides a comprehensive view of proteome changes and their phosphorylation levels in goat muscle, involved in producing meats of different muscle parts. It also gives a better understanding of the regulation of protein on various biological processes that determine the final meat quality attributes. 相似文献
Mangiferin (MGF) is a phenolic compound isolated from mango, but its poor solubility significantly limits its use. In this study, MGF was embedded into the inner aqueous phase of W1/O/W2 emulsions. Firstly, the dissolution method of MGF was determined. MGF remained stable in solution with pH 13 at 30 min, and its solubility reached 10 mg mL−1. When the pH of MGF solutions was adjusted from pH 13 to pH 6, MGF did not immediately crystallise, providing sufficient time to construct the MGF-loaded W1/O/W2 emulsions. Subsequently, the MGF-loaded W1/O/W2 emulsions were constructed using polyglycerol polyricinoleate (PGPR) and calcium caseinate (CAS). The formation and stability of the W1/O/W2 emulsions were investigated. The MGF-loaded W1/O/W2 emulsions stabilised with 1% PGPR and 1% – 3% CAS exhibited a low viscosity, limited loading capacity, and poor stability. Conversely, the MGF-loaded W1/O/W2 emulsions stabilised by 3%PGPR–3%CAS exhibited optimal loading capacity (encapsulation efficiency = 95.31% and loading efficiency = 0.91%) and stability, which was attributed to the fact that high viscosity and gel state retarded the migration of inner aqueous phase. These results indicated that the W1/O/W2 emulsions stabilised by PGPR and CAS may be a potential alternative for encapsulating mangiferin. 相似文献
Journal of Applied Electrochemistry - Ni, Ce, and Ta modified Ti/SnO2–Sb2O5–RuO2 anodes were first prepared by thermal decomposition strategy and applied for Orange G degradation to... 相似文献
Multimedia Tools and Applications - With the rapid development of the Internet, the color digital image copyright protection is facing severe challenges. Simultaneously, the color digital images... 相似文献
With the development of Computer-aided Diagnosis (CAD) and image scanning techniques, Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis. Therefore, WSI analysis has become the key to modern digital histopathology. Since 2004, WSI has been used widely in CAD. Since machine vision methods are usually based on semi-automatic or fully automatic computer algorithms, they are highly efficient and labor-saving. The combination of WSI and CAD technologies for segmentation, classification, and detection helps histopathologists to obtain more stable and quantitative results with minimum labor costs and improved diagnosis objectivity. This paper reviews the methods of WSI analysis based on machine learning. Firstly, the development status of WSI and CAD methods are introduced. Secondly, we discuss publicly available WSI datasets and evaluation metrics for segmentation, classification, and detection tasks. Then, the latest development of machine learning techniques in WSI segmentation, classification, and detection are reviewed. Finally, the existing methods are studied, and the application prospects of the methods in this field are forecasted.
Multimedia Tools and Applications - The crowding in bus is an important factor affecting passenger satisfaction and bus dispatching level. However, how to use video images to detect crowding... 相似文献
Pipe failure prediction has become a crucial demand of operators in daily operation and asset management due to the increase in operation risks of water distribution networks. In this paper, two machine learning algorithms, namely, random forest (RF) and logistic regression (LR) algorithms are employed for pipe failure prediction. RF algorithm consists of a group of decision trees that predicts pipe failure independently and makes the final decision by voting together. For the LR algorithm, the mapping relationship between existing data and decision variables is expressed by the logistic function. Then, the prediction is made by comparing the conditional probability with the fixed threshold value. The proposed algorithms are illustrated using an actual water distribution network in China. Results indicate that the RF algorithm performs better than the LR algorithm in terms of accuracy, recall, and area under the receiver operating characteristic curve. The effects of seven characteristics on pipe failures are analyzed, and diameter and length are identified as the top two influential factors.