Identification of Protein-Phenol Adducts in Meat Proteins: A Molecular Probe Technology Study.

Plant polyphenols with a catechol structure can form covalent adducts with meat proteins, which affects the quality and processing of meat products. However, there is a lack of fast and effective methods of characterizing these adducts and understanding their mechanisms. This study aimed to investigate the covalent interaction between myofibrillar protein (MP) and caffeic acid (CA), a plant polyphenol with a catechol structure, using molecular probe technology. The CA-MP adducts were separated via sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and detected via Western blot and LC-MS/MS analyses. The Western blot analysis revealed that various specific adducts were successfully enriched and identified as bands around 220 kDa, 45 kDa, and two distinct bands between 95 and 130 kDa. Combined with the LC-MS/MS analysis, a total of 51 peptides were identified to be CA-adducted, corresponding to 31 proteins. More than 80% of the adducted peptides carried one adducted site, and the rest carried two adducted sites. The adducted sites were located on cysteine (C/Cys), histidine (H/His), arginine (R/Arg), lysine (K/Lys), proline (P/Pro), and N-terminal (N-Term) residues. Results showed that the covalent interaction of CA and MP was highly selective for the R side chain of amino acids. Moreover, the adducts were more likely to form via C-N bonding than C-S bonding. This study provides new insights into the covalent interaction of plant polyphenols and meat proteins, which has important implications for the rational use of plant polyphenols in the meat processing industry.

A graph network model for neural connection prediction and connection strength estimation.

Objective. Reconstruction of connectomes at the cellular scale is a prerequisite for understanding the principles of neural circuits. However, due to methodological limits, scientists have reconstructed the connectomes of only a few organisms such asC. elegans, and estimated synaptic strength indirectly according to their size and number.Approach. Here, we propose a graph network model to predict synaptic connections and estimate synaptic strength by using the calcium activity data fromC. elegans. Main results. The results show that this model can reliably predict synaptic connections in the neural circuits ofC. elegans, and estimate their synaptic strength, which is an intricate and comprehensive reflection of multiple factors such as synaptic type and size, neurotransmitter and receptor type, and even activity dependence. In addition, the excitability or inhibition of synapses can be identified by this model. We also found that chemical synaptic strength is almost linearly positively correlated to electrical synaptic strength, and the influence of one neuron on another is non-linearly correlated with the number between them. This reflects the intrinsic interaction between electrical and chemical synapses.Significance. Our model is expected to provide a more accessible quantitative and data-driven approach for the reconstruction of connectomes in more complex nervous systems, as well as a promising method for accurately estimating synaptic strength.

Dissimilarity sparsity-preserving projections in feature extraction for visual recognition.

This paper investigates the use of feature dimensionality reduction approaches for high-dimensional data analysis. Most of the existing preserving projection methods are based on similarity, such as the well-known locality-preserving projections, neighborhood-preserving embedding, and sparsity-preserving projections. Here, we propose a simple yet very efficient preserving projection method based on sparsity and dissimilarity for feature extraction, named dissimilarity sparsity-preserving projections, which is an extended version of sparsity-preserving projections. Both projection coefficients and reconstructive residuals are considered in our proposed framework. We give an idea of a "dissimilarity metric" as the measurement of the relationship among the object data. If the value of the dissimilarity metric of two samples is large, the possibility of them belonging to the same class is small. The proposed methods do not have to preset the number of neighbors and heat kernel width, which is one of the important differences from other projection methods. In practical applications, an approximately direct and complete solution is obtained for the proposed algorithm. Experimental results on three widely used face datasets demonstrate that the proposed framework could achieve competitive performance in terms of accuracy and efficiency.