Erratum: Yang, X. et al. Optimization of Culture Conditions for Amoxicillin Degrading Bacteria Screened from Pig Manure. Int. J. Environ. Res. Public Health 2020, 17, 1973.

The second affiliation of the paper should have been included in our original article [...].

Erratum: Yang, X., et al. Isolation, Screening, and Characterization of Antibiotic-Degrading Bacteria for Penicillin V Potassium (PVK) from Soil on a Pig Farm. Int. J. Environ. Res. Public Health 2019, 16, 2166.

The second affiliation of the paper should have been included in our original article [...].

Optimization of Culture Conditions for Amoxicillin Degrading Bacteria Screened from Pig Manure.

(1) Objective: The objective of this study was to screen amoxicillin (AMX)-degrading bacterial strains in pig manure and optimize the fermentation conditions for these strains to achieve high fermentation rate, which can provide an effective way for the practical application of bacterial strains as antibiotic-degrading bacterial in treating livestock waste for antibiotic residues. (2) Methods: Antibiotic susceptibility tests and high-performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS) were employed to screen AMX-degrading bacterial strains in pig manure. The culture conditions were optimized for AMX-degrading bacterial strains using Plackeet-Burman design (PBD), the steepest ascent design, and the response surface methods, coupled with the Box-Behnken design (BBD). The effects of culture time, temperature, rotator (mixing) speed, inoculum level, and initial pH value on the growth of AMX-degrading strains were investigated. Experimental data obtained from BBD were utilized to generate a second-order polynomial regression model for evaluating the effects of the tested variables on the optical density at 600 nm (OD600) of culture solutions as the growth indicator for the screened AMX-degrading strains. (3) Results: The initial pH, culture time, and the inoculum level had significant effects on the OD600 value (growth) of the screened AMX-degrading strains. The initial pH value was found to be the most critical factor influencing the growth of bacteria. The optimized culture condition for the bacterial growth determined by the response surface methodology was: the initial pH of 6.9, culture time of 52 h, and inoculum level of 2%. The average OD value of 12 different fermentation conditions in the initial fermentation tests in this study was 1.72 and the optimization resulted in an OD value of 3.00. The verification experiment resulted in an OD value of 2.94, which confirmed the adequacy of the optimization model for the determining the optimal culture condition. (4) Conclusions: The growth of the screened strain of AMX-degrading bacteria could be optimized by changing the fermentation conditions. The optimization could be achieved by using the Box-Behnken response surface method and Plackett-Burman experimental design.

An Improved Single Shot Multibox Detector Method Applied in Body Condition Score for Dairy Cows.

Body condition scores (BCS) is an important parameter, which is in high correlation with the health status of a dairy cow, metabolic disorder and milk composition during the production period. To evaluate BCS, the traditional methods rely on veterinary experts or skilled staff to look at a cow and touch it. These methods have low efficiency especially on large-scale farms. Computer vision methods are widely used but there are some improvements to increase BCS accuracy. In this study, a low cost BCS evaluation method based on deep learning and machine vision is proposed. Firstly, the back-view images of the cows are captured by network cameras, resulting in 8972 images that constituted the sample data set. The camera is a common 2D camera, which is cheaper and easier to install compared with 3D cameras. Secondly, the key body parts such as tails, pins and rump in the images were labeled manually, the Sing Shot multi-box Detector (SSD) method was used to detect the tail and evaluate the BCS. Inspired by DenseNet and Inception-v4, a new SSD was introduced by changing the network connection method of the original SSD. Finally, the experiments show that the improved SSD method can achieve 98.46% classification accuracy and 89.63% location accuracy, and it has: (1) faster detection speed with 115 fps; (2) smaller model size with 23.1 MB compared to original SSD and YOLO-v3, these are significant advantages for reducing hardware costs.

Isolation, Screening, and Characterization of Antibiotic-Degrading Bacteria for Penicillin V Potassium (PVK) from Soil on a Pig Farm.

(1) Background: Antibiotics are frequently used on farm animals, making animal husbandry a relatively large source of antibiotic pollution of the environment. The present study aims to isolate and acclimatize antibiotic-degrading bacterial strains for penicillin V potassium (PVK) from the contaminated soil of a pig farm. (2) Methods: Bacterial strains were isolated and acclimatized by continuous enrichment of cultures with PVK as the sole carbon source. The antibiotic susceptibility test, thiol mercury salt ultraviolet spectrophotometry (TMSUS), morphological observations, and 16S rDNA sequence analysis were used to identify and characterize the isolated strains. (3) Results: Four bacterial isolates (denoted as LM-1, LM-2, LM-3, LM-4) were obtained, and two of them (LM-1, LM-2) with the highest degradation rates were identified to belong to the same genera as Bacillus. These two isolates were found to be resistant to PVK antibiotic in an antibiotic sensitivity test. The TMSUS indicated that the strains LM-1 and LM-2 had good performance in PVK degradation (68% for LM-1, 66% for LM-2 in 48 h) when the initial PVK concentration was about 100 µg/mL. (4) Conclusions: Two bacterial strains isolated from the soil on a pig farm are effective in degrading PVK and can be potentially used for bioremediation of PVK antibiotic-contaminated soils.