The milk fat globule membrane (MFGM) is a complex structure with numerous functions, and its composition is affected by many factors.  There have been few systematic investigations on goat MFGM proteome profiling during lactation.  Individual milk samples from 15 healthy dairy goats were obtained at six lactation time points for investigation of the MFGM proteome using both data-independent acquisition (DIA) and data-dependent acquisition (DDA) proteomics techniques combined with multivariate statistical analysis.  Using the DIA method, 890 variably abundant MFGM proteins were discovered throughout the lactation cycle.  From 1 to 240 d, butyrophilin subfamily 1 member A1, lipoprotein lipase, perilipin-2, and adipose triglyceride lipase were upregulated, while APOE, complement C3, clusterin, and IgG were downregulated.  Furthermore, from 1 to 90 d, annexin A1, annexin A2, and antithrombin-III were downregulated, then upregulated by d 240.  Albumin had a high degree of connectedness, indicating that it was a key protein, according to protein–protein interaction research.  Overall, our findings gave new insights into the biological features of MFGM protein in goat milk throughout lactation, which may aid in the creation of specialized MFGM products and infant formula.

Chemical defoliation and ripening are a prerequisite for mechanical harvesting of cotton, and the boll opening rate is a critical determinant of timing and rate of defoliates and ripening agents as well as harvest.  Given the low efficiency and poor timeliness of manual determination of boll opening rates, we have developed a rapid method based on digital images.  Field images were collected 7 days before and 7, 14, and 21 days after the application of harvest aids, with the boll opening rates (BOR) varying from 25 to 95%.  We set four shooting heights, five shooting angles and two shooting directions, and a total of 912 original images (each 5,184×3,456 pixels) were obtained.  Actual ground boll opening rates were monitored simultaneously. Each single image was segmented into 500×500 pixels sub-images.  The four deep learning networks were used to identify opened and unopened cotton bolls, and YOLOv5 performed best in balancing recognition time and accuracy.  To address the issue of boundary boll recognition caused by image segmentation, the original images were segmented into 10 different sizes (100, 200, 300, 400, 500, 600, 700, 800, 900, and 1,000 pixels), and YOLOv5 model was then used to identify bolls in each size of the sub-images.  The bounding boxes marking cotton bolls at the same position of two different sizes of sub-images, were combined to obtain new corrected bounding boxes in merged image.   Based on the true values of BOR, the best combination of sub-images is 400×400 pixels with 700×700 pixels.  This combination was used to examine the recognition results of various shooting parameters, and we found that the optimal shooting height for the digital camera was 20-30 cm above the canopy, with a downward angle of 0-30° (BOR higher than 40%) and 15-30° (BOR lower than 40%) from the horizontal and shooting direction parallel to the planting rows.  The method established in this study can enable a less-destructive and rapid detection of BOR in the range of 25 to 95% boll opening rate, with a model R² value >92% and a relative root mean square error <10%, suggesting its high precision and stability for field application.