Dependence of Microcrack Behavior in Wood on Moisture Content during Drying

A modified confocal laser scanning microscopy (CLSM) system was developed not only to observe the microcracks on the surface of Cryptomeria japonica D. Don in situ at the cellular level but also to obtain information about the moisture content (MC) of the wood surface by measuring the change in its electrical resistivity. The sequential images and changes in the electrical resistivity of the wood surface indicated that microcracks formed between the tracheid and ray parenchyma in the latewood region at >1.0E + 07 Ω/sq (square). Microcracks formed when the MC of the wood surface was below the fiber saturation point determined through regression analysis of the surface electrical resistivity and MC. Most of the microcracks develop when the surface electrical resistivity ranged from 3.95E + 10 to 3.60E + 12 Ω/sq. When the surface MC was ~2.5%, microcracks closed and the surface electrical resistivity was either ~1.00E + 15 Ω/sq or outside the measurement range. The modified CLSM and the method to measure the MC of the wood surface can be used to acquire information about the surface MC in specific areas shown in CLSM images. The findings indicated that the MC of the surface of the wood plays an important role in suppressing the emergence of microcracks in drying wood. The modified CLSM system and the method of measuring the MC of the surface of wood can be used to efficiently evaluate methods of drying wood and the quality of dried wood

Characterization of microbial communities during Grifola frondosa (maitake) wood log cultivation

Abstract The method used to cultivate the popular Japanese mushroom Grifola frondosa (maitake), called 'wood log cultivation', comprised two steps: (1) the mycelium is grown around a wood log in a plastic bag, and (2) the mycelium that has spread on the wood log is transferred into casing substrates in a forest. This method is still popular in Japan due to its low cost and high-quality crop production. The importance of the microbiome that inhabits mushroom-cultivation surroundings has recently attracted attention, but no study of the microbial communities in maitake cultivation has been published. We investigated how the bacterial communities changed in wood logs in comparison with a control group (without inoculation) and their interaction with maitake during the first to fourth years of maitake wood log cultivation. A maitake biomass was detected by quantitative PCR in wood logs but not in the casings, and we thus decided to investigate the bacterial communities in wood log samples for control and first- to fourth-year cultivation. The results indicate that the phyla Proteobacteria, Firmicutes, and Gemmatimonadota play key roles in changes of the microbiome composition for maitake wood log cultivation. In a functional profile, bacteria communities in the wood logs during maitake cultivation showed higher relative abundance in cellulolysis, glycolysis, TCA cycle, and many biosynthesis pathways, whereas the control group showed higher relative abundance in fermentation. These results suggested that (i) the bacterial communities which inhabited maitake cultivated wood logs may help the maitake degrade wood cellulose, and (ii) part of the glucose from the cellulose degraded by both maitake and bacteria was used for the bacterial TCA cycle instead of fermentation. Bacteria also produce some chemicals that maitake mycelium may need. It is also likely that some potential intracellular parasites dwell with maitake. The different cultivation stages showed different network structures. A network analysis indicated that Class Gammaproteobacteria is a potential keystone taxon for the microbiome network stability of maitake cultivated wood logs. These results contribute to the understanding of the microbiome in maitake-cultivation surroundings and will improve maitake wood log cultivation

A movie of figures created by the VAE model.

Vascular bundles of bamboo are determinants for mechanical properties of bamboo material and for physiological properties of living bamboo. The morphology of vascular bundles reflecting mechanical and physiological functions differs not only within internode tissue but also among different internodes in the culm. Although the distribution of vascular bundle fibers has received much attention, quantitative evaluation of the morphological transformation of vascular bundles associated with spatial distribution patterns has been limited. In this study deep learning models were used to determine quantitative changes in the distribution and morphology of vascular bundles in the culms of moso bamboo (Phyllostachys pubescens). A precise model for extracting vascular bundles from cross-sectional images was constructed using the U-Net model. Analyses of extracted vascular bundles from different internodes showed significant changes in vascular bundle distribution and morphology among internodes. Vascular bundles in lower internodes showed outer relative position and larger area than those in upper internodes. Aspect ratio and eccentricity indicate that vascular bundles in internodes near the base have more elliptical morphology, with a long axis in the radial direction. The variational autoencoder model using extracted vascular bundles enabled simulation of the morphological transformation of vascular bundles along with radial direction. These deep learning models enabled highly accurate quantification of vascular bundle morphologies, and will contribute to a further understanding of bamboo development as well as evaluation of the mechanical and physiological properties of bamboo.</div

Test images used in the present study to analyze by Model 2.

Test images used in the present study to analyze by Model 2.</p

All raw data of properties of vascular bundles predicted by Model 2.

All raw data of properties of vascular bundles predicted by Model 2.</p

Evaluation of U-Net models.

Vascular bundles of bamboo are determinants for mechanical properties of bamboo material and for physiological properties of living bamboo. The morphology of vascular bundles reflecting mechanical and physiological functions differs not only within internode tissue but also among different internodes in the culm. Although the distribution of vascular bundle fibers has received much attention, quantitative evaluation of the morphological transformation of vascular bundles associated with spatial distribution patterns has been limited. In this study deep learning models were used to determine quantitative changes in the distribution and morphology of vascular bundles in the culms of moso bamboo (Phyllostachys pubescens). A precise model for extracting vascular bundles from cross-sectional images was constructed using the U-Net model. Analyses of extracted vascular bundles from different internodes showed significant changes in vascular bundle distribution and morphology among internodes. Vascular bundles in lower internodes showed outer relative position and larger area than those in upper internodes. Aspect ratio and eccentricity indicate that vascular bundles in internodes near the base have more elliptical morphology, with a long axis in the radial direction. The variational autoencoder model using extracted vascular bundles enabled simulation of the morphological transformation of vascular bundles along with radial direction. These deep learning models enabled highly accurate quantification of vascular bundle morphologies, and will contribute to a further understanding of bamboo development as well as evaluation of the mechanical and physiological properties of bamboo.</div

Preparation of training data for extracting vascular bundles from cross section images.

Different internodes (the 2nd, 12th, 22nd, and 32nd internodes) in culms (A) were used to prepare blocks and cross sectional images were obtained (B, left side), by which mask images were drawn by hand (B, right side). Scale = 1 mm. Original images and mask images pairs were cropped with gray scale (C) to train U-Net model.</p