Mulberry leaf extract reduces abdominal fat deposition via adenosine-activated protein kinase/sterol regulatory element binding protein-1c/acetyl-CoA carboxylase signaling pathway in female Arbor Acre broilers.

This experiment was carried out to investigate the mechanism of action of mulberry leaf extract (MLE) in reducing abdominal fat accumulation in female broilers. A total of 192 one-day-old female Arbor Acres (AA) broilers were divided into 4 diet groups, with each group consisting of 8 replicates with 6 birds per replicate. The diets contained a basal diet and 3 test diets with supplementation of 400, 800, or 1,200 MLE mg/kg, respectively. The trial had 2 phases that lasted from 1 to 21 d and from 22 to 56 d, respectively. The growth performance, abdominal fat deposition, fatty acid composition, serum biochemistry and mRNA expression of genes related to fat metabolism in liver were determined. The results showed that, 1) dietary supplementation with MLE had no significant impact on broilers final body weight, average daily gain (ADG), or feed to gain ration (F/G) (P > 0.05), but linearly reduced abdominal fat accumulation in both experimental phases (P < 0.05); 2) the total contents of monounsaturated fatty acids (MUFA) and polyunsaturated fatty acids (PUFA), such as palmitoleic acid, oleic acid, and eicosadienoic acid, were increased quadratically as a result of dietary supplements of 400, 800, and 1,200 mg/kg MLE (P < 0.01), while the total contents of saturated fatty acids (SFA), such as teracosanoic acid were decreased (P < 0.01); 3) the addition of 800 or 1,200 MLE mg/kg to the diet linearly reduced total cholesterol (TC) in the serum and liver (P < 0.05). Adenosine-activated protein kinase (AMPK) mRNA expression in the liver was quadratically increased by the addition of 800 or 1,200 MLE mg/kg to the diet (P < 0.05), and the mRNA expression of sterol regulatory element binding protein-1c (SREBP-1c), acetyl-CoA carboxylase (ACC), and acetyl-CoA carboxylate), fatty acid synthase (FAS) were linearly decreased (P < 0.05). In conclusion, MLE can be employed as a viable fat loss feed supplement in fast-growing broiler diets since it reduces abdominal fat deposition in female AA broilers via the AMPK/SREBP-1c/ACC signaling pathway. MLE can also be utilized to modify the fatty acid profile in female broilers (AA) at varied inclusion levels.

A Novel Semi-Supervised Method of Electronic Nose for Indoor Pollution Detection Trained by M-S4VMs.

Electronic nose (E-nose), as a device intended to detect odors or flavors, has been widely used in many fields. Many labeled samples are needed to gain an ideal E-nose classification model. However, the labeled samples are not easy to obtain and there are some cases where the gas samples in the real world are complex and unlabeled. As a result, it is necessary to make an E-nose that cannot only classify unlabeled samples, but also use these samples to modify its classification model. In this paper, we first introduce a semi-supervised learning algorithm called S4VMs and improve its use within a multi-classification algorithm to classify the samples for an E-nose. Then, we enhance its performance by adding the unlabeled samples that it has classified to modify its model and by using an optimization algorithm called quantum-behaved particle swarm optimization (QPSO) to find the optimal parameters for classification. The results of comparing this with other semi-supervised learning algorithms show that our multi-classification algorithm performs well in the classification system of an E-nose after learning from unlabeled samples.

A Novel Optimization Technique to Improve Gas Recognition by Electronic Noses Based on the Enhanced Krill Herd Algorithm.

An electronic nose (E-nose) is an intelligent system that we will use in this paper to distinguish three indoor pollutant gases (benzene (C6H6), toluene (C7H8), formaldehyde (CH2O)) and carbon monoxide (CO). The algorithm is a key part of an E-nose system mainly composed of data processing and pattern recognition. In this paper, we employ support vector machine (SVM) to distinguish indoor pollutant gases and two of its parameters need to be optimized, so in order to improve the performance of SVM, in other words, to get a higher gas recognition rate, an effective enhanced krill herd algorithm (EKH) based on a novel decision weighting factor computing method is proposed to optimize the two SVM parameters. Krill herd (KH) is an effective method in practice, however, on occasion, it cannot avoid the influence of some local best solutions so it cannot always find the global optimization value. In addition its search ability relies fully on randomness, so it cannot always converge rapidly. To address these issues we propose an enhanced KH (EKH) to improve the global searching and convergence speed performance of KH. To obtain a more accurate model of the krill behavior, an updated crossover operator is added to the approach. We can guarantee the krill group are diversiform at the early stage of iterations, and have a good performance in local searching ability at the later stage of iterations. The recognition results of EKH are compared with those of other optimization algorithms (including KH, chaotic KH (CKH), quantum-behaved particle swarm optimization (QPSO), particle swarm optimization (PSO) and genetic algorithm (GA)), and we can find that EKH is better than the other considered methods. The research results verify that EKH not only significantly improves the performance of our E-nose system, but also provides a good beginning and theoretical basis for further study about other improved krill algorithms' applications in all E-nose application areas.

A Novel Pre-Processing Technique for Original Feature Matrix of Electronic Nose Based on Supervised Locality Preserving Projections.

An electronic nose (E-nose) consisting of 14 metal oxide gas sensors and one electronic chemical gas sensor has been constructed to identify four different classes of wound infection. However, the classification results of the E-nose are not ideal if the original feature matrix containing the maximum steady-state response value of sensors is processed by the classifier directly, so a novel pre-processing technique based on supervised locality preserving projections (SLPP) is proposed in this paper to process the original feature matrix before it is put into the classifier to improve the performance of the E-nose. SLPP is good at finding and keeping the nonlinear structure of data; furthermore, it can provide an explicit mapping expression which is unreachable by the traditional manifold learning methods. Additionally, some effective optimization methods are found by us to optimize the parameters of SLPP and the classifier. Experimental results prove that the classification accuracy of support vector machine (SVM combined with the data pre-processed by SLPP outperforms other considered methods. All results make it clear that SLPP has a better performance in processing the original feature matrix of the E-nose.

A Novel Semi-Supervised Electronic Nose Learning Technique: M-Training.

When an electronic nose (E-nose) is used to distinguish different kinds of gases, the label information of the target gas could be lost due to some fault of the operators or some other reason, although this is not expected. Another fact is that the cost of getting the labeled samples is usually higher than for unlabeled ones. In most cases, the classification accuracy of an E-nose trained using labeled samples is higher than that of the E-nose trained by unlabeled ones, so gases without label information should not be used to train an E-nose, however, this wastes resources and can even delay the progress of research. In this work a novel multi-class semi-supervised learning technique called M-training is proposed to train E-noses with both labeled and unlabeled samples. We employ M-training to train the E-nose which is used to distinguish three indoor pollutant gases (benzene, toluene and formaldehyde). Data processing results prove that the classification accuracy of E-nose trained by semi-supervised techniques (tri-training and M-training) is higher than that of an E-nose trained only with labeled samples, and the performance of M-training is better than that of tri-training because more base classifiers can be employed by M-training.