Milk proteins are well known to produce aerated food due to the amphiphilicity. However, milk proteins are commonly added in blends for the desirable properties in food industry. In this study, the foaming properties of milk protein mixtures (MPM), a mixtures of whey protein isolated (WPI) and milk protein concentrate (MPC), was studied through foaming capacity (FC), foam stability (FS), and foam morphology at pH 3.0-9.0. Physi-ochemical, structural, surface properties, and Pearson correlation analysis were measured to gain insight into foaming behavior. Results indicated that MPM showed excellent FC (113.0-114.3 %) and FS (90.7-93.0 %) at pH 6.0-9.0, and foam displayed a smaller size and uniform distribution. MPM solutions showed smaller particles, higher solubility, and lower apparent viscosity at pH 6.0-9.0, which resulted in an increase in surface pressure and adsorption rate (Kdiff), facilitating more protein absorbed to interface. To further investigate structural changes, various spectral methods were used, in which the structure of MPM was changed with pH. Correlation analysis further suggests that Kdiff and solubility positively affect the formation of foam, while free sulfhydryl and fl-sheet contributed to stabilizing foams. These findings provide valuable information on MPM as ingredients for aerated foods under acidic, neutral, and alkaline conditions.

The purpose of this study was to evaluate the effects of pasteurization and spray drying on goat milk lipids by liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) and multiple vari-able statistics. A total of 1061 lipids assigned to 29 subclasses in raw and thermal-treated groups were identified. One hundred and 85 different lipids (DLs) (VIP >= 1 and |Log2FC| >= 1.0) were selected from pairwise comparisons of goat milk by different treatments. Glycerophospholipids were the most affected subclasses by thermal pro-cesses, especially by spray drying. Five potential lipid markers [(DG (16:1_18:0), TG (18:1_22:1_18:2), Cer (t17:2/31:0), LPC (0:0/20:0), and LPS (20:0/0:0] were used to distinguish different treated goat milk. Moreover, glycerophospholipid metabolism was the primary pathway of DLs. These results would provide more details of lipid profiles in thermally treated (pasteurization and spray drying) goat milk.

This study investigated the differences in structural and physicochemical properties of the model infant formula treated by different thermal processes including Pasteurization (P), Ultra-pasteurization (UP), Ultra-high temperature instantaneous sterilization (UHT), and In-container Sterilization (ICS), respectively. Results of electrophoresis and fluorescence spectroscopy indicated that interactions between proteins occurred by hydrophobic interaction and disulfide bonds during thermal treatments. Particle size and micro-structure showed the formation of larger particles after heat treatment (size: ICSM>UHTM>UPM>PM). The shift of saccharide band (1042 cm-1 to 1073 cm-1) and the change in amide Ⅰ and Ⅱ by Fourier Transform Infrared, the changes in color, and thermal properties all suggested the strongest Maillard reaction during ICS processing. Samples treated by pasteurization exhibited the lowest foaming capacity (90.30 %) and highest foaming stability (48.72 %) (P < 0.05). The differences in emulsion properties were insignificant (P > 0.05). Pasteurization may be better for infant formula processing.

Antibiotic combination is a promising strategy to extend the lifetime of antibiotics and thereby combat antimicrobial resistance. However, screening for new antibiotic combinations is both time-consuming and labor-intensive. In recent years, an increasing number of researchers have used computational models to predict effective antibiotic combinations. In this review, we summarized existing computational models for antibiotic combinations and discussed the limitations and challenges of these models in detail. In addition, we also collected and summarized available data resources and tools for antibiotic combinations. This study aims to help computational biologists design more accurate and interpretable computational models.