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The objective of this study was to compare the expression of Col1a1, Col1a2, and procollagen I in the seminiferous tubules of immature and adult mice and to characterize the cellular expression pattern of procollagen I in germ cells during spermatogenesis in order to provide necessary groundwork for further functional studies in the process of spermatogenesis. Microarray analysis demonstrated that Col1a1 and Col1a2 were abundantly expressed in the seminiferous tubules of 6-day-old mice compared with 60-day-old mice, and the expression levels of Col1a1 and Col1a2 mRNA were validated using a semi-quantitative RT-PCR assay. Western blot analysis further confirmed that procollagen I was expressed at a higher level in the seminiferous tubules of 6-day-old mice compared with 60-day-old mice. Immunohistochemical analysis revealed that type A spermatogonia were positive for procollagen I in the testis of 6-day-old mice, whereas Sertoli cells were negative for this protein. The in vivo procollagen I staining in type A spermatogonia was corroborated in spermatogonia exhibiting a high potential for proliferation and the ability to form germ cell colonies in in vitro culture. Moreover, procollagen I was also detected in type A spermatogonia, intermediate spermatogonia, type B spermatogonia, and preleptotene spermatocytes in the adult mouse testes, but positive staining disappeared in more differentiated germ cell lineages detaching from the basement membrane, including leptotene spermatocytes, pachytene spermatocytes, round spermatids and elongated spermatids. These data suggest that Col1a1, Col1a2 and procollagen I are associated with type A spermatogonia and play a potential role in mediating the detachment and migration of germ cells during spermatogenesis.

Fast and effective visualization research of leaf lettuce leaves was particularly important in modern fine agriculture irrigation. Therefore, hyperspectral imaging technology was used to test the moisture content in leaf lettuce sample. A method involving wavelet transform coupled with partial least squares regression (WT‐PLSR) was proposed to extract characteristic wavelengths, build models, and evaluate characteristic wavelengths. Hyperspectral imaging data of 200 leaf lettuce leaves of five moisture gradients were obtained using hyperspectral imaging instrument. The whole region of leaf lettuce sample was selected as region of interest (ROI) to extract the hyperspectral data using sheffield index, image segment, and masking. To reduce noise interference, Savitzky–Golay (SG) algorithm was used to manage spectral data. Besides, WT‐PLSR algorithm was used to select effective feature wavelengths, build detection model, and evaluate characteristic wavelengths. The best prediction model of moisture content in leaf lettuce sample was PLSR model (RMSEP = 0.1688, Rp2=0.8307) building by characteristic wavelength extracted from fourth layers of wavelet decomposition, and it was applied to achieve the distribution of moisture content in leaf lettuce leaves. Hyperspectral imaging technology coupled with WT‐PLSR algorithm can effectively realize the quantitative detection of moisture content in leaf lettuce sample, and visualizing distribution map of moisture content in leaf lettuce leaves offered a more intuitive and comprehensive assessment of moisture contents. Practical applications Well understanding moisture content in leaf lettuce leaves is very important for revelation of novel biological function and calcium (Ca) content. To facilitate more intuitively and comprehensively detect the moisture content in leaf lettuce leaves, a method involving wavelet transform coupled with partial least squares regression (WT‐PLSR) was proposed to extract characteristic wavelengths, build models, and evaluate characteristic wavelengths. It confirms that the WT‐PLSR algorithm is feasible and effective for building models of different moisture contents in leaf lettuce leaves, and visualizing distribution map of leaf lettuce leaves offered a more intuitive and comprehensive assessment of moisture contents.

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