Automatic wood species identification systems have enabled fast and accurate identification of wood species outside of specialized laboratories with well-trained experts on wood species identification. Conventional automatic wood species identification systems consist of two major parts: a feature extractor and a classifier. Feature extractors require hand-engineering to obtain optimal features to quantify the content of an image. A Convolutional Neural Network (CNN), which is one of the Deep Learning methods, trained for wood species can extract intrinsic feature representations and classify them correctly. It usually outperforms classifiers built on top of extracted features with a hand-tuning process.We developed an automatic wood species identification system utilizing CNN models such as LeNet, MiniVGGNet, and their variants. A smartphone camera was used for obtaining macroscopic images of rough sawn surfaces from cross sections of woods. Five Korean softwood species (cedar, cypress, Korean pine, Korean red pine, and larch) were under classification by the CNN models. The highest and most stable CNN model was LeNet3 that is two additional layers added to the original LeNet architecture. The accuracy of species identification by LeNet3 architecture for the five Korean softwood species was 99.3%. The result showed the automatic wood species identification system is sufficiently fast and accurate as well as small to be deployed to a mobile device such as a smartphone.

This study demonstrates the utilization of light microscopy and Fast Fourier Transform-Peak Finding (FPF) method for microfibril angle (MFA) measurement from unstained sections of red pine (Pinus densiflora). To obtain an image with optimal contrast and resolution for MFA measurement, effects of numerical aperture (NA) of condenser lens and color filters were investigated. About 60% of NA of the maximum condenser NA produced an image with optimal contrast, but a color filter with short wavelength range (DAPI) created images with improved resolution. Manual angle measurement and the FPF method were applied to the image with optimal contrast for MFA measurement. The experimental results from the FPF method were considered to be more repeatable and less subjective than those from the manual angle measurement.

The pine wilt disease is one of the most serious forest diseases that kill the pine trees, and the study on the invasion and movement of the pine wood nematode within the tree is very important for understanding the inhabitation of pine wood nematode. In this relation, the microscopic observation was carried out to study the place of inhabitation and movement of pine wood nematode within the infested wood. In result, the rays were mainly infested by pine wood nematode and showed dark discoloration due to their necrosis in cross, radial and tangential surface. Also, the intensive damage was found in the resin canals. On the other hand, some traumatic resin canals in tangential band were identified in the sapwood near the cambium.In the ray, the pine wood nematode occurred more commonly in the ray parenchyma cell and fusiform ray with horizontal resin canal than in the ray tracheid and uniseriate ray without horizontal resin canal, respectively. The pine wood nematode was thought to move from ray to tracheid through the large natural opening, window-like pit, in the cross-field, neither through the small natural opening, bordered pit, in the tracheid nor through the tracheid wall by creating a bore hole.

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The present study was conducted to investigate the regulatory mechanism of plasminogen activators (PAs) activation by 17β-estradiol (E2) and progesterone (P4) in porcine uterine epithelial cells (pUECs). pUECs were collected from porcine uterine horn and cultured at 80% confluence. Then, 0.1% (v/v) DMSO, 20 ng/mL E2, and P4 with or without fetal bovine serum (FBS) treated to cultured cells for 24 hours. The supernatants were used for measurement of PAs activity and expression of urokinase-type PA (uPA), tissue-type PA (tPA), uPA specific receptor (uPAR), and type-1 PA inhibitor (PAI-1) mRNA were analyzed by real-time PCR. The expression of PAs-related genes was not affect by steroid hormones in both of serum treatment groups. However, PAs activity was increased by treatment of E2 compared to 0.1% DMSO treatment in serum-free group (p<0.05). Then, E2 and P4 were diluted with 0.002% (v/v) DMSO for reduction of its effect and treated to cultured cells without FBS. Only tPA mRNA was significantly increased by E2 treatment (p<0.05). PAs activity was enhanced in E2 treated group compared to control groups (p<0.05). These results indicate that serum-free condition is more proper to evaluate effect of steroid hormones and activation of PAs in pUECs was mainly regulated by estrogen. These regulation of PAs activation may be associated with uterine remodeling during pre-ovulatory phase in pigs, however, further studies are needed to investigate precise regulatory mechanism.