Search

Background Necrotic enteritis (NE) is an infectious intestinal disease caused by Clostridium perfringens (C. perfringens) that is now re-emerging and causing concern within the poultry industry. Previously, the supplementation of antibiotics in feed was the most popular control strategy against C. perfringens. However, with the ban on supplementing growth-promoting antibiotics in livestock feed, alternatives to antibiotics will be essential in order to control necrotic enteritis. A possible alternative to antibiotics could be the medium or long chain fatty acids (MCFA or LCFA) as these are able to destroy cell membranes which in turn results in the death of bacteria. In this study, the in vitro antimicrobial activity of different combinations with microencapsulated caprylic acid (C8: 0), capric acid (C10: 0), lauric acid (C12: 0) and myristic acid (C14: 0) against C. perfringens and in vivo control the NE-inducing C. perfringens in broiler chicken were analyzed. Results The minimum inhibitory concentration (MIC) and the minimum bactericidal concentration (MBC) assay results revealed that three different combinations of medium/long chain fatty acids varied in antimicrobial activities against C. perfringens type A strain (CVCC52, quality control), C. perfringens type A strain (C8-1), C. perfringens type G strain (D25) and C. perfringens type G strain (MZ1). Specifically, combination of C12: 0 and C14: 0 (C12–14) showed the highest antimicrobial activity against the four strains of C. perfringens (MIC ≤ 12.5 μg/mL, MBC = 50 μg/mL), followed by the combination of C10: 0 and C12: 0 (C10–12) (MIC, MBC ≤ 50 μg/mL). The in vivo study, 189 of 818-crossbred chickens that were fed a wheat-based diet and randomly divided into nine groups, with six treatment groups supplemented with either a high dose (1 g/kg) or low dose (0.5 g/kg) of three combinations respectively. The remaining three groups comsisted of a positive group supplement with avilamycin (0.01 g/kg), an infected control and an uninfected control. All chickens were challenged with C. perfringens from day 14 to day 17, except those in the uninfected control group. On day 20, the duodenum and jejunum necrotic lesions scores were calculated and the results showed that there was significant decrease in the C12–C14 high dose group (1.43 ± 0.23, 0.48 ± 0.13) and the C10–12 high dose group (1.52 ± 0.19, 0.48 ± 0.11) compared to the infected group (2.86 ± 0.21, 1.20 ± 0.28). Conclusions This finding indicated that dietary microencapsulated C12–C14 and C10–C12 could inhibit the growth of C. perfringens in chickens, which proves is viability to serve as an alternative to antibiotics used for necrotic enteritis caused by C. perfringens.

Most quadruped reptiles, such as lizards, salamanders and crocodiles, swing their waists while climbing on horizontal or vertical surfaces. Accompanied by body movement, the centroid trajectory also becomes more of a zigzag path rather than a straight line. Inspired by gecko’s gait and posture on a vertical surface, a gecko inspired model with one pendular waist and four active axil legs, which is called GPL model, is proposed. Relationship between the waist position, dynamic gait, and driving forces on supporting feet is analyzed. As for waist trajectory planning, a singular line between the supporting feet is found and its effects on driving forces are discussed. Based on the GPL model, it is found that a sinusoidal waist trajectory, rather than a straight line, makes the driving forces on the supporting legs smaller. Also, a waist close to the pygal can reduce the driving forces compared to the one near middle vertebration, which is in accord with gecko’s body bending in the process of climbing. The principles of configuration design and gait planning are proposed based on theoretical analyses. Finally, a bio-inspired robot DracoBot is developed and both of the driving force measurements and climbing experiments reinforce theoretical analysis and the rationality of gecko’s dynamic gait.

This paper proposes NNF — a fuzzy Petri Net system based on neural network for proposition logic representation, and gives the formal definition of NNF. For the NNF model, forward reasoning algorithm, backward reasoning algorithm and knowledge learning algorithm are discussed based on weight training algorithm of neural network — Back Propagation algorithm.Thus NNF is endowed with the ability of learning a rule. The paper concludes with a discussion on extending NNF to predicate logic, forming NNPrF, and proposing the formal definition and a reasoning algorithm of NNPrF.