LAZY4 acts additively with the starch-statolith-dependent gravity-sensing pathway to regulate shoot gravitropism and tiller angle in rice.

Rice tiller angle is a key agronomic trait that has significant effects on the establishment of a high-yield rice population. However, the molecular mechanism underlying the control of rice tiller angle remains to be clarified. Here, we characterized the novel tiller-angle gene LAZY4 (LA4) in rice through map-based cloning. LA4 encodes a C3H2C3-type RING zinc-finger E3 ligase localized in the nucleus, and an in vitro ubiquitination assay revealed that the conserved RING finger domain is essential for its E3 ligase activity. We found that expression of LA4 can be induced by gravistimulation and that loss of LA4 function leads to defective shoot gravitropism caused by impaired asymmetric auxin redistribution upon gravistimulation. Genetic analysis demonstrated that LA4 acts in a distinct pathway from the starch biosynthesis regulators LA2 and LA3, which function in the starch-statolith-dependent pathway. Further genetic analysis showed that LA4 regulates shoot gravitropism and tiller angle by acting upstream of LA1 to mediate lateral auxin transport upon gravistimulation. Our studies reveal that LA4 regulates shoot gravitropism and tiller angle upstream of LA1 through a novel pathway independent of the LA2-LA3-mediated gravity-sensing mechanism, providing new insights into the rice tiller-angle regulatory network.

Application for Identifying the Origin and Predicting the Physiologically Active Ingredient Contents of Gastrodia elata Blume Using Visible-Near-Infrared Spectroscopy Combined with Machine Learning.

Gastrodia elata (G. elata) Blume is widely used as a health product with significant economic, medicinal, and ecological values. Due to variations in the geographical origin, soil pH, and content of organic matter, the levels of physiologically active ingredient contents in G. elata from different origins may vary. Therefore, rapid methods for predicting the geographical origin and the contents of these ingredients are important for the market. This paper proposes a visible-near-infrared (Vis-NIR) spectroscopy technology combined with machine learning. A variety of machine learning models were benchmarked against a one-dimensional convolutional neural network (1D-CNN) in terms of accuracy. In the origin identification models, the 1D-CNN demonstrated excellent performance, with the F1 score being 1.0000, correctly identifying the 11 origins. In the quantitative models, the 1D-CNN outperformed the other three algorithms. For the prediction set of eight physiologically active ingredients, namely, GA, HA, PE, PB, PC, PA, GA + HA, and total, the RMSEP values were 0.2881, 0.0871, 0.3387, 0.2485, 0.0761, 0.7027, 0.3664, and 1.2965, respectively. The Rp2 values were 0.9278, 0.9321, 0.9433, 0.9094, 0.9454, 0.9282, 0.9173, and 0.9323, respectively. This study demonstrated that the 1D-CNN showed highly accurate non-linear descriptive capability. The proposed combinations of Vis-NIR spectroscopy with 1D-CNN models have significant potential in the quality evaluation of G. elata.

Two Noncentrosymmetric Bismuth Phosphates: Rational Design Synthesis and Optical Properties.

Developing strategies to rational design noncentrosymmetric structure still attract much interest for their applications in nonlinear optical and piezoelectric materials. Two noncentrosymmetric (NCS) alkaline earth metal bismuth phosphates have been successfully achieved via partial replacement of Bi3+ with Ca2+ or Sr2+ ions. BiCa(H0.5PO4)2 (designated as CaBiPO) and BiSr(H0.5PO4)2 (designated as SrBiPO), together with their solid solution Bi(Sr1-xCax)(H0.5PO4)2 (0 < x ≤ 0.5), crystallize in the NCS space group C2. Both CaBiPO and SrBiPO exhibit ultraviolet nonlinear optical (NLO) properties, and their second-harmonic generation effects belong to type-II phase matching. Meanwhile, they could also act as photoluminescence hosts in which the Eu3+-doping samples SrBiPO:xEu3+ (x = 0.02-0.2) emit orange light. The effect of different radius ions on the derivative structures and the structure-NLO property relationship has also been discussed in detail.

Rapid detection of hepatitis B virus DNA level based on interval-point data fusion of infrared spectra.

Hepatitis B is an infectious disease cause by the hepatitis B virus (HBV). In recent years, HBV-DNA level clinically gets more attention for its detailed information than other serological markers. Unfortunately, common clinical method for HBV-DNA level detection is limited for its hours consuming. This study combined infrared spectroscopy with machine learning to investigate the feasibility of near-infrared (NIR) and mid-infrared (MIR) spectra for rapid detection of HBV-DNA level. Based on partial least squares-discriminant analysis (PLS-DA) modeling method, the optimal NIR and MIR models and traditional data fusion models were constructed, respectively. Considering inequal weight between interval and point data in machine learning, interval-point data fusion method was used to compare with other traditional date fusion methods. The results of the study illustrate that interval-point data fusion of NIR and MIR spectra combined with PLS-DA modeling can provide a rapid method for HBV-DNA level detection.

Early warning and diagnostic visualization of Sclerotinia infected tomato based on hyperspectral imaging.

This research explored the feasibility of early warning and diagnostic visualization of Sclerotinia infected tomato by using hyperspectral imaging technology. Healthy tomato plants and tomato plants with Sclerotinia sclerotiorum were cultivated, and hyperspectral images at 400-1000 nm were collected from healthy and infected tomato leaves at 1, 3, 5, and 7 days of incubation. After preprocessing the spectra with first derivative (FD), second derivative (SD), standard normal variant (SNV), and multiplicative scatter correction (MSC) partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used to construct tomato sclerotinia identification model and select the best preprocessing method. On this basis, two band screening methods, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), were introduced to reduce data redundancy and improve the model's prediction accuracy. The results showed that the accuracy of the validation sets and operation speed of the CARS-PLS and CARS-SVM models were 87.88% and 1.8 s, and 87.95% and 1.78 s, respectively. The experiment was based on the SNV-CARS-SVM prediction model combined with image processing, spectral extraction, and visualization analysis methods to create diagnostic visualization software, which opens a new avenue to the implementation of online monitoring and early warning system for sclerotinia infected tomato.

Estimation of chemical oxygen demand in different water systems by near-infrared spectroscopy.

To monitor environmental water pollution effectively and meet human water needs, it is crucial to develop a fast, simple, and accurate method for monitoring chemical oxygen demand (COD) in various water systems. In this study, COD prediction models for different water systems were developed by combining near-infrared (NIR) spectroscopy with partial least squares regression (PLSR). Samples of wastewater, surface water, and seawater were collected from Guangzhou, Guangdong Province, China. Three pretreatment methods were used to preprocess the spectra in order to improve the accuracy and minimalism of the model. We investigate the performance of two variable selection algorithms, namely, binary gray wolf optimization (BGWO) and competitive adaptive reweighting sampling (CARS). The results show that both BGWO and CARS improved the performance of the model in terms of higher accuracy and less wavelength input; both of the combined model performances were better than that of PLSR alone, and CARS-PLSR achieved the best results. Using CARS-PLSR, surface water, wastewater, and seawater model inputs were reduced by 96 %, 96 %, and 82 % as compared to the PLSR results, respectively, and the testing sets R2 reached 0.860, 0.815, and 0.692, respectively. The spectral variable selection algorithm could identify the important spectral variables between COD content and NIR spectra in three water systems, thereby improving the accuracy and simplicity of the PLSR model for COD prediction. Our results have important practical value for predicting COD content in different water systems by NIR spectroscopy.