Epiphytic orchids account for 68% of vascular epiphytes and 69% of orchid species, demonstrating the importance of epiphytism to the diversification of the Orchidaceae. All orchids require mycorrhizal associations with specific fungi at the earliest stages of germination and development, and these symbioses may have played crucial roles in their adaptation to, and diversification within, canopy habitats. We present the first comprehensive comparison of fungal communities, encompassing both epiphytic and terrestrial orchids as well as their habitat substrates (bark and soil) on Yakushima Island, Japan. We analyzed mycorrhizal communities associated with 29 orchid species, representing eight tribes in four subfamilies, using both Sanger and high-throughput sequencing (HTS) methods, and also characterized fungal communities in the substrates using HTS. Our results reveal that epiphytic and terrestrial orchids have differing mycorrhizal communities and the fungal composition of their habitat substrates also differs. Although overall fungal richness was lower in epiphytic than in terrestrial substrates, the diversity of fungi in the rhizoctonia, including members of Tulasnellaceae, Ceratobasidiaceae, and Serendipitaceae, was similarly high for both habitat types. Furthermore, some rhizoctonia fungi were shared between orchid life forms and exhibited symbiotic compatibility across habitats in in vitro culture. Although rhizoctonia communities differed between habitat substrates, comparable diversity and the presence of shared taxa across habitats imply that these fungi may have reduced barriers to canopy colonization and contribute to the successful establishment and diversification of epiphytic orchids.
Understanding how tree growth responds to climate is important for predicting how plantation forests may react to ongoing climate change. In this study, we examined the climate–growth relationships of six Cryptomeria japonica cultivars across five common-garden sites in southwestern Japan using dendrochronological methods. We analyzed 48 years of ring width data to evaluate how monthly and annual temperature and precipitation affect radial growth at site and cultivar levels. Static and moving correlation analyses revealed that late winter to early spring temperatures (February–March) positively influenced radial growth at most sites, likely by facilitating cambial reactivation, while high summer precipitation tended to suppress growth at some sites, possibly through reduced solar irradiance. Precipitation effects were variable across sites, with autumn rainfall showing positive effects at lower-elevation sites. At the cultivar level, early-growth type cultivars tended to show positive temperature responses, while late-growth types showed more variable precipitation responses, though the majority of cultivar-level correlations did not reach statistical significance. Multivariate ordination (PCA) and permutational multivariate analysis of variance (PERMANOVA) on correlation-based response profiles indicated that geographic location was a significant driver of climate-growth response patterns (R2 = 0.28, p = 0.014), while cultivar type explained a substantial but non-significant proportion of variance (R2 = 0.23, p = 0.085). These results suggest that local environmental conditions primarily shape climate sensitivity in C. japonica, while genetic provenance provides an additional, non-negligible layer of variation. Our findings underscore the importance of considering both site characteristics and cultivar characteristics in forest management and climate adaptation strategies for C. japonica plantations.
Periodontal disease has been reported to increase the risk of threatened preterm labor (TPL). However, studies analyzing the oral, vaginal, and rectal lumen commensal flora in women with TPL are limited. In this study, a total of 60 women were enrolled, including 30 with TPL and 30 without TPL. We assessed periodontal clinical parameters, salivary hormone levels, and the microbiome using next-generation sequencing. Probing pocket depth (PPD) and bleeding on probing were greater in the TPL than in the non-TPL group. The TPL group was associated with lower progesterone levels and an increase in PPD ≥ 4 mm. Significant differences in alpha diversity in only vaginal Faith’s phylogenetic diversity, and significant differences in beta diversity at all sites were observed. ANCOM showed decreased Lactobacillales in the saliva and Bifidobacterium in the rectal lumen, and increased Staphylococcus in the oral cavity and vagina in the TPL group. The peptidoglycan synthesis pathway was significantly upregulated in the oral and vaginal tissues in the TPL group. Overall, the TPL group had lower progesterone levels and more severe periodontal disease; furthermore, the low progesterone levels in the TPL group were associated with oral and vaginal dysbiosis of the microbiota.
Introduction: Frailty in older adults is a multidimensional condition that affects healthy life expectancy. Engagement in meaningful activities (MA) is thought to support healthy aging, but the specific relationship between satisfaction with MA and frailty remains underexplored. This study investigated the longitudinal association between satisfaction with MA and frailty (physical, social, and cognitive domains) in community-dwelling older adults.Method: A total of 231 community-dwelling older adults who completed a 2-year follow-up were analyzed. Frailty in each domain was assessed at baseline and at 1- and 2-years. Participants' MA and satisfaction were obtained through face-to-face interviews. Mixed-effects logistic regression models were used to examine the longitudinal associations between satisfaction with MA and frailty status trajectories.Results: At follow-up, changes in physical frailty status were observed in most participants. Robust status was commonly maintained in the social and cognitive frailty domain. Participants' MA primarily involved leisure, sports, and domestic activities. Mixed-effects logistic regression analysis showed that satisfaction with MA was significantly associated with physical frailty, even after adjusting for potential covariates (odds ratio 0.68; 95% confidence interval 0.45, 0.99, p = 0.045). However, this association was not significant for social or cognitive frailty.Conclusion: Satisfaction with MA was longitudinally associated with physical frailty, but not with social or cognitive frailty.
The prediction of mechanical strength using morphological scanning electron microscopy (SEM) images would accelerate the development of bone-substitute materials. However, only a few studies have confirmed the relationship between the sintering temperature and surface properties of sintered bodies by analyzing their SEM images. Therefore, in this study, we attempted to classify and predict properties based on SEM images of β-tricalcium phosphate (β-TCP), which was liquid crystal display (LCD) 3D printed and sintered, by deep learning, for the purpose of applying it to the optimization of sintering temperature and estimation of mechanical strength. Synthesized β-TCP powder was mixed with light-curing resin and polyethylene glycol and poured into a vat of the LCD 3D printer. The CAD data of the gyroid structure with interconnecting holes was prepared for 3D printing. The printed green bodies were sintered at various temperatures for 3 h to obtain sintered bodies. As the sintering temperature increased, the degree of sintering of β-TCP progressed, the size of the sintered body decreased, and the maximum compressive strength increased. It was found that the clustering of the SEM images of 3D printed β-TCP sintered at different temperatures can be made by transfer learning and hierarchical cluster analysis. Images of the sintered β-TCP surface can be classified by deep learning (transfer learning) and cluster analysis. The constructed model in this study successfully predicted the compressive strength of the 3D printed sintered bodies from their SEM images. The proposed deep learning method can be used for future surface property analyses of ceramic materials and sintering condition optimization.
