No abstract

Nitrification, the microbial oxidation of ammonia to nitrate via nitrite, occurs in a wide range of acidic soils. However, the ammonia-oxidizing bacteria (AOB) that have been isolated from soil to date are acid-sensitive. Here we report the isolation and characterization of an acid-adapted AOB from an acidic agricultural soil. The isolated AOB, strain TAO100, is classified within the Gammaproteobacteria based on phylogenetic characteristics. TAO100 can grow in the pH range of 5-7.5 and survive in highly acidic conditions until pH 2 by forming cell aggregates. Whereas all known gammaproteobacterial AOB (γ-AOB) species, which have been isolated from marine and saline aquatic environments, are halophiles, TAO100 is not phenotypically halophilic. Thus, TAO100 represents the first soil-originated and non-halophilic γ-AOB. The TAO100 genome is considerably smaller than those of other γ-AOB and lacks several genes associated with salt tolerance which are unnecessary for survival in soil. The ammonia monooxygenase subunit A gene of TAO100 and its transcript are higher in abundance than those of ammonia-oxidizing archaea and betaproteobacterial AOB in the strongly acidic soil. These results indicate that TAO100 plays an important role in the nitrification of acidic soils. Based on these results, we propose TAO100 as a novel species of a new genus, Candidatus Nitrosoglobus terrae.

The morphological traits, color traits, and chemical components of new tea shoots and leaves varied depending on radiation intensity, shoot growth, and cropping season. These findings are useful for covering cultivation with high quality and high productivity in tea gardens. © 2018 Society of Chemical Industry.

Tea trees are kept in shaded locations to increase their chlorophyll content, which influences green tea quality. Therefore, monitoring change in chlorophyll content under low light conditions is important for managing tea trees and producing high-quality green tea. Hyperspectral remote sensing is one of the most frequently used methods for estimating chlorophyll content. Numerous studies based on data collected under relatively low-stress conditions and many hyperspectral indices and radiative transfer models show that shade-grown tea performs poorly. The performance of four machine learning algorithms-random forest, support vector machine, deep belief nets, and kernel-based extreme learning machine (KELM)-in evaluating data collected from tea leaves cultivated under different shade treatments was tested. KELM performed best with a root-mean-square error of 8.94 ± 3.05 µg cm −2 and performance to deviation values from 1.70 to 8.04 for the test data. These results suggest that a combination of hyperspectral reflectance and KELM has the potential to trace changes in the chlorophyll content of shaded tea leaves.Ultraviolet and visible light (UV-vis) spectroscopy and high-performance liquid chromatography (HPLC) techniques have previously been used to quantify chlorophyll content. These are destructive methods and may enforce a limited sample size because they are expensive and labour-intensive. It is also not always possible to apply these methods in situ. Alternately, some portable chlorophyll content meters, such as CL-01, SPAD-502, Dualex, and CCM-200, have been used to estimate chlorophyll content [9]. However, light intensity also influences leaf thickness [10], and then it often makes the output of the meter obscure [11]. In contrast, hyperspectral reflectance measurements may be used for detecting various responses from crops [12] or evaluating vegetation properties [13,14], and the use of reflectance for estimating chlorophyll content is being seriously considered. Some pigments, such as chlorophylls and carotenoids, absorb energy strongly in the ultraviolet, blue, and red regions, and then the reflectance and transmittance are weak [15]. The reflectance from vegetation is characterised by the low reflectance over the blue (400-500 nm) and red (650-690 nm) spectral regions due to strong absorption by chlorophylls plus carotenoids in the blue wavelengths and chlorophyll a in the red wavelengths, and the steep rise in reflectance between 680 and 750 nm is caused by the chlorophylls increasing to absorb at wavelengths beyond 700 nm [16]. Based on these features, some empirical approaches, such as vegetation indices, which are generally based on a few narrow or broad spectral bands, are convenient for use to estimate chlorophyll content. Most vegetation indices for chlorophyll content use wavelengths ranging from 400 to 860 nanometres [17][18][19] or the red edge (680 to 750 nm) [20,21]. However, some datasets, such as LOPEX, CALMIT, ANGERS, and HAWAII, that have been used for the development of vegetation indi...