OsMYB3 is a R2R3-MYB gene responsible for anthocyanin biosynthesis in black rice.

Black rice is a rare type of rice germplasm with various health benefits that are largely attributed to anthocyanin pigment accumulation in the pericarps. The anthocyanin biosynthesis in plant tissues is activated mainly by the MBW complexes, consisting of three types of transcription factors R2R3-MYB, bHLH, and WDR. In black rice, the bHLH and WDR components regulating anthocyanin biosynthesis in pericarps have been characterized, while the R2R3-MYB factor remains unknown. By examining the expression correlation between all putative rice MYB genes and anthocyanin biosynthesis-related genes based on transcriptome data of pericarps in combination with further molecular and genetic analysis, we proved that OsMYB3 (LOC_Os03g29614) was the determinant R2R3-MYB gene for anthocyanin biosynthesis in rice pericarps. The expression level of OsMYB3 in pericarps of black rice was significantly higher than that of white rice. The knockout of OsMYB3 in a black rice variety caused significant downregulation of 19 anthocyanin metabolites and many other flavonoids in grains. Our research deepens the understanding of regulatory system for anthocyanin biosynthesis in rice pericarps and provides implications for breeding black rice varieties with high anthocyanin level. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-021-01244-x.

OsDPE2 Regulates Rice Panicle Morphogenesis by Modulating the Content of Starch.

Starch is a carbon sink for most plants, and its biological role changes with response to the environment and during plant development. Disproportionating Enzyme 2 (DPE2) is a 4-α-glycosyltransferase involved in starch degradation in plants at night. LAX1 plays a vital role in axillary meristem initiation in rice. Herein, results showed that Oryza sativa Disproportionating Enzyme 2 (OsDPE2) could rescue the mutant phenotype of lax1-6, LAX1 mutant. OsDPE2 encodes rice DPE2 located in the cytoplasm. In this study, OsDPE2 affected the vegetative plant development of rice via DPE2 enzyme. Additionally, OsDPE2 regulated the reproductive plant development of rice by modulating starch content in young panicles. Furthermore, haplotype OsDPE2(AQ) with higher DPE2 enzyme activity increased the panicle yield of rice. In summary, OsDPE2 can regulate vegetative and reproductive plant development of rice by modulating starch content. Furthermore, DPE2 activities of OsDPE2 haplotypes are associated with the panicle yield of rice. This study provides guidance for rice breeding to improve panicle yield traits.

A Wireless Acoustic Emission Sensor System with ACMD-IGWO-XGBoost Algorithm for Living Tree Moisture Content Diagnosis.

Trunk water has an important influence on the metabolism and ecological balance of living trees, which affects the vegetation growth and moisture cycle of the whole forest ecosystem. The accurate and real-time measurement of moisture content (MC) is of vital guiding meaning to living tree cultivation and forest management. In this paper, a water content diagnosis system based on a wireless acoustic emission sensor network (WASN) was designed and implemented with the aim of the nondestructive detection of water content in living wood trunks. Firstly, the acoustic emission (AE) signal of the trunk epidermis was sampled at high speed; then, its characteristic parameters were calculated and transmitted wirelessly to the gateway. Furthermore, the optimal characteristic wavelet sequence was decomposed by the adaptive chirp mode decomposition (ACMD), and the improved grey wolf optimizer (IGWO) optimization XGBoost established the MC prediction model, which was improved by the multi-strategy joint optimization. Finally, field monitoring was carried out on Robinia Pseudoacacia, Photinia serrulata, Pinus massoniana and Toona sinensis. The average diagnostic accuracy reached 96.75%, which shows that the diagnosis system has excellent applicability in different working conditions.

Internet-of-Things-Based Multiple-Sensor Monitoring System for Soil Information Diagnosis Using a Smartphone.

The rise of Internet of Things (IoT) technology has moved the digital world in a new direction and is considered the third wave of the information industry. To meet the current growing demand for food, the agricultural industry should adopt updated technologies and smart agriculture based on the IoT which will strongly enable farmers to reduce waste and increase productivity. This research presents a novel system for the application of IoT technology in agricultural soil measurements, which consists of multiple sensors (temperature and moisture), a micro-processor, a microcomputer, a cloud platform, and a mobile phone application. The wireless sensors can collect and transmit soil information in real time with a high speed, while the mobile phone app uses the cloud platform as a monitoring center. A low power consumption is specified in the hardware and software, and a modular power supply and time-saving algorithm are adopted to improve the energy effectiveness of the nodes. Meanwhile, a novel soil information prediction strategy was explored based on the deep Q network (DQN) reinforcement learning algorithm. Following the weighted combination of a bidirectional long short-term memory, online sequential extreme learning machine, and parallel extreme machine learning, the DQN Bi-OS-P prediction model was obtained. The proposed data acquisition system achieved a long-term stable and reliable collection of time-series soil data with equal intervals and provided an accurate dataset for the precise diagnosis of soil information. The RMSE, MAE, and MAPE of the DQN Bi-OS-P were all reduced, and the R2 was improved by 0.1% when compared to other methods. This research successfully implemented the smart soil system and experimentally showed that the time error between the value displayed on the mobile phone app and its exact acquisition moment was no more than 3 s, proving that mobile applications can be effectively used for the real-time monitoring of soil quality and conditions in wireless multi-sensing based on the Internet of Things.

Multimodal Deep-Learning Framework for Accurate Prediction of Wettability Evolution of Laser-Textured Surfaces.

The lengthy process through which laser-textured surfaces transform from hydrophilic to hydrophobic severely restricts their practical applications. Accurately predicting the wettability evolution curve is crucial; however, developing a reliable prediction model remains challenging. Herein, a data-driven multimodal deep-learning framework was developed, in which multimodal data of micro/nanostructure morphology images, composition distribution images, and time information are effectively coupled and fed into a convolutional neural network (CNN). Rich data input and in-depth data mining make the framework more robust, achieving accurate prediction of the wettability evolution curves of various typical micro/nanostructures. Additionally, accurate prediction of input images with varying magnifications and untrained laser-textured surfaces demonstrates the generalizability of the multimodal CNN framework. The visualization results of the convolution layer confirmed the rationality of the information learned by the model. Additionally, the proposed multimodal CNN framework was successfully utilized to investigate the optimization process. Further, a laser-textured surface with a shorter evolution period and a larger final contact angle was realized. The proposed multimodal CNN framework offers an efficient and cost-effective method for predicting the wettability evolution curves and exploring the optimization processes, enhancing the application potential of laser micro/nanofabrication of superhydrophobic surfaces.

Martensite Start Temperature Prediction through a Deep Learning Strategy Using Both Microstructure Images and Composition Data.

In recent decades, various previous research has established empirical formulae or thermodynamic models for martensite start temperature (Ms) prediction. However, most of this research has mainly considered the effect of composition and ignored complex microstructural factors, such as morphology, that significantly affect Ms. The main limitation is that most microstructures cannot be digitized into numerical data. In order to solve this problem, a convolutional neural network model that can use both composition information and microstructure images as input was established for Ms prediction in a medium-Mn steel system in this research. Firstly, the database was established through experimenting. Then, the model was built and trained with the database. Finally, the performance of the model was systematically evaluated based on comparison with other, traditional AI models. It was proven that the new model provided in this research is more rational and accurate because it considers both composition and microstructural factors. In addition, because of the use of microstructure images for data augmentation, the deep learning had a low risk of overfitting. When the deep-learning strategy is used to deal with data that contains both numerical and image data types, obtaining the value matrix that contains interaction information of both numerical and image data through data preprocessing is probably a better approach than direct linking of the numerical data vector to the fully connected layer.

Investigation on Thermal Barrier Coating Damages for Ultrafast Laser Drilling of Cooling Holes.

To mitigate the challenges pertaining to coating damage and processing defects arising from the utilization of ultrafast laser drilling for microhole creation in thermal barrier coatings (TBCs), thereby exerting substantial influence on the long-term durability of these microholes, the investigation proposes a comprehensive methodology. It encompasses the design of a two-factor four-level full factorial experiment and the execution of experimental research on picosecond laser drilling of TBC microholes. By meticulously analyzing the morphology of the microholes and the coating interface, the damage mechanisms associated with picosecond laser drilling of TBC microholes, as well as the influence of laser process parameters on coating damage, are revealed. The findings reveal that the optimal microhole entrance quality and the lowest roughness along the hole perimeter are attained at a laser power of 12 W and a scanning speed of 320 mm/s. Moreover, at a laser power of 30 W and a scanning speed of 320 mm/s, the minimal crack length on the blunt angle side of the hole and the highest machining quality are observed.

Chemical constitutes and anti-obesity potential of seedlings of purple-leafed rice in high-fat diet induced obese mice.

The seedlings of purple leafed rice (SPLR) are characterized by high contents of anthocyanins and various polyphenols, whose total anthocyanin content can reach 15.16 mg g-1, indicating that SPLR may have many bioactivities. Therefore, in order to evaluate the anti-obesity potential value of SPLR, the nutrient components were first identified, and the anti-obesity effect of SPLR was evaluated with a high-fat diet (HFD) induced obese mouse model for 12 weeks. The results demonstrated that SPLR supplementation could alleviate HFD-induced obesity and suppress pro-inflammatory cytokine production and macrophage infiltration in epididymal white adipose tissue of HFD-induced obese mice. The findings indicated that supplementation with SPLR may contribute to preventing obesity and related complications.

RNA-seq reveals transcriptional differences in anthocyanin and vitamin biosynthetic pathways between black and white rice.

Anthocyanins and vitamins in black rice are the micronutrients vital to human health, both of which predominantly accumulate in the bran fraction. Some studies have demonstrated that black rice contains more vitamins compared with common white rice, indicating potential association between anthocyanin and vitamin accumulation. In this study, transcriptomes of pericarps collected from 27 black rice accessions and 49 white rice accessions at 10 days after flowering (DAF) were sequenced and analyzed. We identified 830 differentially expressed genes (DEGs) including 58 transcription factors (TFs) between black and white rice. Among 58 differentially expressed transcription factors, OsTTG1 was confirmed to be the one and only WD40 repeat protein regulating anthocyanin biosynthesis in the pericarp. Moreover, we identified 53 differentially expressed synthetic-related genes among 42 main synthesis enzymes in the biosynthesis pathway of seven vitamins including ß-carotene, vitamin B1, vitamin B2, vitamin B5, vitamin B7, vitamin B9 and vitamin E. Collectively, our results provide valuable insights into the molecular mechanism of biosynthesis of anthocyanins and vitamins and the potential effect of anthocyanin biosynthesis on vitamin biosynthesis in black rice.

Identification of Antioxidant and Anti-α-amylase Components in Lotus (Nelumbo nucifera, Gaertn.) Seed Epicarp.

Lotus seed epicarp, a byproduct of lotus seed production process, is usually discarded as a waste. In this study, antioxidant and anti-α-amylase activities of freeze-dried water and various methanol extracts of lotus seed epicarp were evaluated. The extract obtained by 80% methanol exhibited the strongest DPPH and ABTS radical scavenging activity and ferric reducing power, as well as the greatest inhibitory potential on α-amylase. The excellent antioxidant and α-amylase inhibitory activities of 80% methanol extract might be attributed to its highest concentrations of total phenolics, flavonoids, and condensed tannins. The inhibition kinetic analysis revealed that the 80% methanol extract was a reversible and uncompetitive-type inhibitor of α-amylase. Furthermore, based on MALDI-TOF-MS and thiolysis-HPLC-ESI-MS, the main active components present in 80% methanol extract were identified to be B-type heteropolymeric condensed tannins built up of mixtures of propelargonidins, procyanidins, and prodelphinidins, with the predominance of procyanidins and epicatechin as the main constitutive units. The results obtained suggested that lotus seed epicarp could be exploited as a potential source of natural antioxidants and α-amylase inhibitors.

The rice white green leaf 2 gene causes defects in chloroplast development and affects the plastid ribosomal protein S9.

BACKGROUND: Plastid ribosomal proteins (PRPs) play important roles in the translation of key proteins involved in chloroplast development and photosynthesis. PRPs have been widely studied in many plant species; however, few studies have investigated their roles in rice. RESULT: In the present study, we used ethyl methane sulfonate mutagenesis and obtained a novel rice mutant called white green leaf 2 (wgl2). The wgl2 mutants exhibited an albino phenotype from germination through the three-leaf stage, and then gradually transitioned to green through the later developmental stages. Consistent with this albino phenotype, wgl2 mutants had abnormal chloroplasts and lower levels of photosynthetic pigments. Map-based cloning and DNA sequencing analyses of wgl2 revealed a single-nucleotide substitution (G to T) in the first exon of LOC_Os03g55930, which resulted in a substitution of glycine 92 to valine (G92 V). WGL2 encodes a conserved ribosomal protein, which localizes to the chloroplast. Complementation and targeted deletion experiments confirmed that the point mutation in WGL2 is responsible for the wgl2 mutant phenotype. WGL2 is preferentially expressed in the leaf, and mutating WGL2 led to obvious changes in the expression of genes related to chlorophyll biosynthesis, photosynthesis, chloroplast development, and ribosome development compared with wild-type. CONCLUSIONS: WGL2 encodes a conserved ribosomal protein, which localizes to the chloroplast. WGL2 is essential for early chloroplast development in rice. These results facilitate research that will further uncover the molecular mechanism of chloroplast development.