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Background: Compared to breast-fed (BF), formula-fed (FF) infants exhibit more rapid weight gain, a different fecal microbial profile, as well as elevated serum insulin, insulin growth factor 1 (IGF-1), and branched chain amino acids (BCAAs). Since infant formula contains more protein and lower free amino acids than breast milk, it is thought that protein and/or free amino acids may be key factors that explain phenotypic differences between BF and FF infants. Methods: Newborn rhesus monkeys (Macaca mulatta) were either exclusively BF or fed regular formula or reduced protein formula either supplemented or not with a mixture of amino acids. Longitudinal sampling and clinical evaluation were performed from birth to 16 weeks including anthropometric measurements, intake records, collection of blood for hematology, serum biochemistry, hormones, and metabolic profiling, collection of urine for metabolic profiling, and collection of feces for 16s rRNA fecal microbial community profiling. Results: Reducing protein in infant formula profoundly suppressed intake, lowered weight gain and improved the FF-specific metabolic phenotype in the first month of age. This time-dependent change paralleled an improvement in serum insulin. All lower protein FF groups showed reduced protein catabolism with lower levels of blood urea nitrogen (BUN), urea, ammonia, albumin, creatinine, as well as lower excretion of creatinine in urine compared to infants fed regular formula. Levels of fecal microbes (Bifidobacterium and Ruminococcus from the Ruminococcaceae family), that are known to have varying ability to utilize complex carbohydrates, also increased with protein reduction. Adding free amino acids to infant formula did not alter milk intake or fecal microbial composition, but did significantly increase urinary excretion of amino acids and nitrogen-containing metabolites. However, despite the lower protein intake, these infants still exhibited a distinct FF-specific metabolic phenotype characterized by accelerated weight gain, higher levels of insulin and C-peptide as well as elevated amino acids including BCAA, lysine, methionine, threonine and asparagine. Conclusions: Reducing protein and adding free amino acids to infant formula resulted in growth and metabolic performance of infants that were more similar to BF infants, but was insufficient to reverse the FF-specific accelerated growth and insulin-inducing high BCAA phenotype.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Human milk delivers an array of bioactive components that safeguard infant growth and development and maintain healthy gut microbiota. Milk fat globule membrane (MFGM) is a biologically functional fraction of milk increasingly linked to beneficial outcomes in infants through protection from pathogens, modulation of the immune system and improved neurodevelopment. In the present study, we characterized the fecal microbiome and metabolome of infants fed a bovine MFGM supplemented experimental formula (EF) and compared to infants fed standard formula (SF) and a breast-fed reference group. The impact of MFGM on the fecal microbiome was moderate; however, the fecal metabolome of EF-fed infants showed a significant reduction of several metabolites including lactate, succinate, amino acids and their derivatives from that of infants fed SF. Introduction of weaning food with either human milk or infant formula reduces the distinct characteristics of breast-fed- or formula-fed- like infant fecal microbiome and metabolome profiles. Our findings support the hypothesis that higher levels of protein in infant formula and the lack of human milk oligosaccharides promote a shift toward amino acid fermentation in the gut. MFGM may play a role in shaping gut microbial activity and function.

Formula-fed (FF) infants exhibit a different metabolic profile than breast-fed (BF) infants. Two potential mechanisms are the higher protein level in formula compared with breast milk and the removal of the milk fat and associated milk fat globule membranes (MFGM) during production of infant formula. To determine whether MFGM may impact metabolism, formula-fed infants were randomly assigned to receive either an MFGM isolate-supplemented experimental formula (EF) or a standard formula (SF) from 2 until 6 months and compared with a BF reference group. Infants consuming EF had higher levels of fatty acid oxidation products compared to infants consuming SF. Although the protein level in the study formula was approximately 12 g/L (lower than most commercial formulas), a metabolic difference between FF and BF remained such that FF infants had higher levels of amino acid catabolism by-products and a low efficiency of amino acid clearance (preference for protein metabolism). BF infants had higher levels of fatty acid oxidation products (preference for fat metabolism). These unique, energy substrate-driven metabolic outcomes did not persist after diet was shifted to weaning foods and appeared to be disrupted by complementary feeding. Our results suggest that MFGM may have a role in directing infant metabolism.

Background: Compared to breast-fed (BF), formula-fed (FF) infants exhibit more rapid weight gain, a different fecal microbial profile, as well as elevated serum insulin, insulin growth factor 1 (IGF-1), and branched chain amino acids (BCAAs). Since infant formula contains more protein and lower free amino acids than breast milk, it is thought that protein and/or free amino acids may be key factors that explain phenotypic differences between BF and FF infants. Methods: Newborn rhesus monkeys (Macaca mulatta) were either exclusively BF or fed regular formula or reduced protein formula either supplemented or not with a mixture of amino acids. Longitudinal sampling and clinical evaluation were performed from birth to 16 weeks including anthropometric measurements, intake records, collection of blood for hematology, serum biochemistry, hormones, and metabolic profiling, collection of urine for metabolic profiling, and collection of feces for 16s rRNA fecal microbial community profiling. Results: Reducing protein in infant formula profoundly suppressed intake, lowered weight gain and improved the FF-specific metabolic phenotype in the first month of age. This time-dependent change paralleled an improvement in serum insulin. All lower protein FF groups showed reduced protein catabolism with lower levels of blood urea nitrogen (BUN), urea, ammonia, albumin, creatinine, as well as lower excretion of creatinine in urine compared to infants fed regular formula. Levels of fecal microbes (Bifidobacterium and Ruminococcus from the Ruminococcaceae family), that are known to have varying ability to utilize complex carbohydrates, also increased with protein reduction. Adding free amino acids to infant formula did not alter milk intake or fecal microbial composition, but did significantly increase urinary excretion of amino acids and nitrogen-containing metabolites. However, despite the lower protein intake, these infants still exhibited a distinct FF-specific metabolic phenotype characterized by accelerated weight gain, higher levels of insulin and C-peptide as well as elevated amino acids including BCAA, lysine, methionine, threonine and asparagine. Conclusions: Reducing protein and adding free amino acids to infant formula resulted in growth and metabolic performance of infants that were more similar to BF infants, but was insufficient to reverse the FF-specific accelerated growth and insulin-inducing high BCAA phenotype.

In order to study the influence of different boundary constraints on the bearing characteristics of the bolt anchorage section, the stress conditions of the bolt in the state of tension bearing and bolt-surrounding rock coordinated deformation bearing state were analyzed, and the numerical calculation with different boundary constraints was carried out. The research results show that the axial force of the bolt decreases along the anchorage length under different boundary constraints, and the attenuation speed is faster under lateral constraints. Under the condition of top fixed and lateral restraint, the axial force of the bolt decreases at 1/2 of the anchorage length, obviously slows down and approaches to zero. The axial force of the bottom fixed lateral restraint bolt decreases slowly within 5 cm of the anchorage beginning, and then decreases in a power function. Under the condition of top fixed and lateral restraint, the shear stress peak of the bolt-anchorage agent interface appears at 2 cm of the outer end of the anchorage, while under the condition of bottom fixed and lateral restraint, the shear stress peak is at 9-10 cm of the anchorage depth. Under the condition of top fixed and lateral restraint, the peak shear stress of anchorage agent-surrounding rock interface is located at the outer end of anchorage, and the peak shear stress under the condition of bottom fixed lateral restraint is located at 8 cm outside the anchorage. The magnitude and location of the shear stress peak value of the two interfaces are different, which is the important influencing factor of various anchorage failure modes.

Reservoir operation is an important part of basin water resources management. The rational use of reservoir operation scheme can not only enhance the capacity of flood control and disaster reduction in the basin, but also improve the efficiency of water use and give full play to the comprehensive role the reservoir. The conventional decision-making method of reservoir operation scheme is computationally large, subjectivity and difficult to capture the nonlinear relationship. To solve these problems, this paper proposes a reservoir operation scheme decision-making model IWGAN-IWOA-CNN based on artificial intelligence and deep learning technology. In view of the lack of data in the original reservoir operation scheme and the limited improvement of data characteristics by the traditional data augmentation algorithm, an improved generative adversarial network algorithm (IWGAN) is proposed. IWGAN uses the loss function which integrates Wasserstein distance, gradient penalty and difference item, and dynamically adds random noise in the process of model training. The whale optimization algorithm is improved by introducing Logistic chaotic mapping to initialize population, non-linear convergence factor and adaptive weights, and Levy flight perturbation strategy. The improved whale optimization algorithm (IWOA) is used to optimize hyperparameters of convolutional neural networks (CNN), so as to obtain the best parameters for model prediction. The experimental results show that the data generated by IWGAN has certain representation ability and high quality; IWOA has faster convergence speed, higher convergence accuracy and better stability; IWGAN-IWOA-CNN model has higher prediction accuracy and reliability of scheme selection.

Effective condition monitoring and fault diagnosis of bearings can not only maximize the life of rolling bearings and prevent unexpected shutdowns caused by equipment failures but also eliminate unnecessary costs and waste caused by excessive maintenance. However, the existing deep-learning-based bearing fault diagnosis models have the following defects. First of all, these models have a large demand for fault data. Second, the previous models only consider that single-scale features are generally less effective in diagnosing bearing faults. Therefore, we designed a bearing fault data collection platform based on the Industrial Internet of Things, which is used to collect bearing status data from sensors in real time and feed it back into the diagnostic model. On the basis of this platform, we propose a bearing fault diagnosis model based on deep generative models with multiscale features (DGMMFs) to solve the above problems. The DGMMF model is a multiclassification model, which can directly output the abnormal type of the bearing. Specifically, the DGMMF model uses 4 different variational autoencoder models to augment the bearing data and integrates features of different scales. Compared with single-scale features, these multiscale features contain more information and can perform better. Finally, we conducted a large number of related experiments on the real bearing fault datasets and verified the effectiveness of the DGMMF model using multiple evaluation metrics. The DGMMF model has achieved the highest value under all metrics, among which the value of precision is 0.926, the value of recall is 0.924, the value of accuracy is 0.926, and the value of F1 score is 0.925.