This study examines a central tenet of the "membranemicelle" model of humic substancessthat humic molecules form "micelle-like" aggregates with hydrophobic interiors into which nonpolar organic compounds partition. We solubilized atrazine, labeled by a trifluoromethyl group on the ethylamino side chain, in aqueous solutions containing 10% humic acid by weight and observed the F-19 nuclear magnetic resonance (NMR) relaxation of atrazine induced by paramagnetic probes added to the humic solution. One paramagnetic probe, Gd‚EDTA anion, remains in aqueous solution while the other, TEMPO (2,2,6,6tetramethyl-1-piperidinyloxy), is sorbed by humic micelles. The hydrophilic anionic paramagnetic probe induces virtually no paramagnetic relaxation in atrazine solubilized by 10% aqueous humic acid solutions while the hydrophobic neutral paramagnetic probe causes rapid paramagnetic relaxation. These results show that atrazine solubilized by concentrated humic acid solutions occupies a domain accessible only to neutral hydrophobic molecules and, hence, confirms the existence of hydrophobic domains. We suggest that atrazine resides in the hydrophobic interior of humic acid micelles.

Soil salinity is a major problem in agriculture because high accumulation of Na+ ions in plants causes toxicity that can result in yield reduction. Na+/K+ homeostasis is known to be important for salt tolerance in plants. Na+/K+ homeostasis in rice (Oryza sativa L.) involves nine high-affinity K+ transporter (HKT) encoding Na+-K+ symporter, five OsNHX Na+/H+ antiporters, and OsSOS1 Na+/K+ antiporter genes. In the present study, we investigated various molecular and physiological processes to evaluate germination rate, growth pattern, ion content, and expression of OsHKT, OsNHX, and OsSOS1genes related to Na+/K+ homeostasis in different rice genotypes under salt stress. We found a significant increase in the germination percentage, plant vigor, Na+/K+ ratio, and gene expression of the OsHKT family in both the roots and shoots of the Nagdong cultivar and salt-tolerant cultivar Pokkali. In the roots of Cheongcheong and IR28 cultivars, Na+ ion concentrations were found to be higher than K+ ion concentrations. Similarly, high expression levels of OsHKT1, OsHKT3, and OsHKT6 were observed in Cheongcheong, whereas expression levels of OsHKT9 was high in IR28. The expression patterns of OsNHX and OsSOS1 and regulation of other micronutrients differed in the roots and shoots regions of rice and were generally increased by salt stress. The OsNHX family was also expressed at high levels in the roots of Nagdong and in the roots and shoots of Pokkali; in contrast, comparatively low expression levels were observed in the roots and shoots of Cheongcheong and IR28 (with the exception of high OsNHX1 expression in the roots of IR28). Furthermore, the OsSOS1 gene was highly expressed in the roots of Nagdong and shoots of Cheongcheong. We also observed that salt stress decreases chlorophyll content in IR28 and Pokkali but not in Cheongcheong and Nagdong. This study suggests that under salt stress, cultivar Nagdong has more salt-tolerance than cultivar Cheongcheong.

Recently, the estimation of bone maturation using deep learning has been actively conducted. However, many studies have considered hand–wrist radiographs, while a few studies have focused on estimating cervical vertebral maturation (CVM) using lateral cephalograms. This study proposes the use of deep learning models for estimating CVM from lateral cephalograms. As the second, third, and fourth cervical vertebral regions (denoted as C2, C3, and C4, respectively) are considerably smaller than the whole image, we propose a stepwise segmentation-based model that focuses on the C2–C4 regions. We propose three convolutional neural network-based classification models: a one-step model with only CVM classification, a two-step model with region of interest (ROI) detection and CVM classification, and a three-step model with ROI detection, cervical segmentation, and CVM classification. Our dataset contains 600 lateral cephalogram images, comprising six classes with 100 images each. The three-step segmentation-based model produced the best accuracy (62.5%) compared to the models that were not segmentation-based.

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