CCD-Based Skinning Injury Recognition on Potato Tubers (Solanum tuberosum L.): A Comparison between Visible and Biospeckle Imaging.

Skinning injury on potato tubers is a kind of superficial wound that is generally inflicted by mechanical forces during harvest and postharvest handling operations. Though skinning injury is pervasive and obstructive, its detection is very limited. This study attempted to identify injured skin using two CCD (Charge Coupled Device) sensor-based machine vision technologies, i.e., visible imaging and biospeckle imaging. The identification of skinning injury was realized via exploiting features extracted from varied ROIs (Region of Interests). The features extracted from visible images were pixel-wise color and texture features, while region-wise BA (Biospeckle Activity) was calculated from biospeckle imaging. In addition, the calculation of BA using varied numbers of speckle patterns were compared. Finally, extracted features were implemented into classifiers of LS-SVM (Least Square Support Vector Machine) and BLR (Binary Logistic Regression), respectively. Results showed that color features performed better than texture features in classifying sound skin and injured skin, especially for injured skin stored no less than 1 day, with the average classification accuracy of 90%. Image capturing and processing efficiency can be speeded up in biospeckle imaging, with captured 512 frames reduced to 125 frames. Classification results obtained based on the feature of BA were acceptable for early skinning injury stored within 1 day, with the accuracy of 88.10%. It is concluded that skinning injury can be recognized by visible and biospeckle imaging during different stages. Visible imaging has the aptitude in recognizing stale skinning injury, while fresh injury can be discriminated by biospeckle imaging.

Biological Activity of Optimized Codon Bovine Type III Interferon Expressed in Pichia pastoris.

Type III interferons (IFN-λs) exhibit potent antiviral activity and immunomodulatory effects in specific cells. Nucleotide fragments of the bovine ifn-λ (boifn-λ) gene were synthetized after codon optimization. The boifn-λ gene was then amplified by splicing using overlap extension PCR (SOE PCR), resulting in the serendipitous acquisition of the mutated boIFN-λ3V18M. The recombinant plasmid pPICZαA-boIFN-λ3/λ3V18M was constructed, and the corresponding proteins were expressed in Pichia pastoris with a high-level extracellular soluble form. Dominant expression strains of boIFN-λ3/λ3V18M were selected by Western blot and ELISA and cultured on a large scale, and the recombinant proteins purified by ammonium sulfate precipitation and ion exchange chromatography yielded 1.5g/L and 0.3 g/L, with 85% and 92% purity, respectively. The antiviral activity of boIFN-λ3/λ3V18M exceeded 106 U/mg, and they were neutralized with IFN-λ3 polyclonal antibodies, were susceptible to trypsin, and retained stability within defined pH and temperature ranges. Furthermore, boIFN-λ3/λ3V18M exerted antiproliferative effects on MDBK cells without cytotoxicity at 104 U/mL. Overall, boIFN-λ3 and boIFN-λ3V18M did not differ substantially in biological activity, except for reduced glycosylation of the latter. The development of boIFN-λ3 and comparative evaluation with the mutant provide theoretical insights into the antiviral mechanisms of boIFN-λs and provide material for therapeutic development.

Feather Damage Monitoring System Using RGB-Depth-Thermal Model for Chickens.

Feather damage is a continuous health and welfare challenge among laying hens. Infrared thermography is a tool that can evaluate the changes in the surface temperature, derived from an inflammatory process that would make it possible to objectively determine the depth of the damage to the dermis. Therefore, the objective of this article was to develop an approach to feather damage assessment based on visible light and infrared thermography. Fusing information obtained from these two bands can highlight their strengths, which is more evident in the assessment of feather damage. A novel pipeline was proposed to reconstruct the RGB-Depth-Thermal maps of the chicken using binocular color cameras and a thermal infrared camera. The process of stereo matching based on binocular color images allowed for a depth image to be obtained. Then, a heterogeneous image registration method was presented to achieve image alignment between thermal infrared and color images so that the thermal infrared image was also aligned with the depth image. The chicken image was segmented from the background using a deep learning-based network based on the color and depth images. Four kinds of images, namely, color, depth, thermal and mask, were utilized as inputs to reconstruct the 3D model of a chicken with RGB-Depth-Thermal maps. The depth of feather damage can be better assessed with the proposed model compared to the 2D thermal infrared image or color image during both day and night, which provided a reference for further research in poultry farming.