The Rice Pentatricopeptide Repeat Protein PPR756 Is Involved in Pollen Development by Affecting Multiple RNA Editing in Mitochondria.

In land plants, the pentatricopeptide repeat (PPR) proteins form a large family involved in post-transcriptional processing of RNA in mitochondria and chloroplasts, which is critical for plant development and evolutionary adaption. Although studies showed a number of PPR proteins generally influence the editing of organellar genes, few of them were characterized in detail in rice. Here, we report a PLS-E subclass PPR protein in rice, PPR756, loss of function of which led to the abolishment of RNA editing events among three mitochondrial genes includin

Chemical Constituents from the Wild Atractylodes macrocephala Koidz and Acetylcholinesterase Inhibitory Activity Evaluation as Well as Molecular Docking Study

Screening the lead compounds which could interact both with PAS and CAS of acetylcholinesterase (AChE) is an important trend in finding innovative drugs for Alzheimer’s disease (AD). In this paper, four sesquiterpenes, i.e., atractylenolide III (1), atractylenolide IV (2), 3-acetyl-atractylon (3) and β-eudesmol (4), were obtained from the wild Atractylode macrocephala grown in Qimen for the first time. Their structures were elucidated mainly by NMR spectroscopy. To screen the potential dual site inhibitors of AChE, the compounds 1, 2, 3, as well as a novel and rare bisesquiterpenoid lactone, biatractylenolide II (5), which was also obtained from the tilted plant in our previous investigation, were evaluated their AChE inhibitory activities by using Ellman’s colorimetric method. The results showed that biatractylenolide II displayed moderate inhibitory activity (IC50 = 19.61 ± 1.11 μg/mL) on AChE. A further molecular docking study revealed that biatractylenolide II can interact with both the peripheral anionic site (PAS) and the catalytic active site (CAS) of AChE. These data suggest that biatractylenolide II can be considered a new lead compound to research and develop more potential dual site inhibitors of AChE

A Knowledge-Aware Attentional Reasoning Network for Recommendation

Knowledge-graph-aware recommendation systems have increasingly attracted attention in both industry and academic recently. Many existing knowledge-aware recommendation methods have achieved better performance, which usually perform recommendation by reasoning on the paths between users and items in knowledge graphs. However, they ignore the users' personal clicked history sequences that can better reflect users' preferences within a period of time for recommendation. In this paper, we propose a knowledge-aware attentional reasoning network KARN that incorporates the users' clicked history sequences and path connectivity between users and items for recommendation. The proposed KARN not only develops an attention-based RNN to capture the user's history interests from the user's clicked history sequences, but also a hierarchical attentional neural network to reason on paths between users and items for inferring the potential user intents on items. Based on both user's history interest and potential intent, KARN can predict the clicking probability of the user with respective to a candidate item. We conduct experiment on Amazon review dataset, and the experimental results demonstrate the superiority and effectiveness of our proposed KARN model

Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index

Phenology is an indicator of crop growth conditions, and is correlated with crop yields. In this study, a phenological approach based on a remote sensing vegetation index was explored to predict the yield in 314 counties within the US Corn Belt, divided into semi-arid and non-semi-arid regions. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product MOD09Q1 was used to calculate the normalized difference vegetation index (NDVI) time series. According to the NDVI time series, we divided the corn growing season into four growth phases, calculated phenological information metrics (duration and rate) for each growth phase, and obtained the maximum correlation NDVI (Max-R2). Duration and rate represent crop growth days and rate, respectively. Max-R2 is the NDVI value with the most significant correlation with corn yield in the NDVI time series. We built three groups of yield regression models, including univariate models using phenological metrics and Max-R2, and multivariate models using phenological metrics, and multivariate models using phenological metrics combined with Max-R2 in the whole, semi-arid, and non-semi-arid regions, respectively, and compared the performance of these models. The results show that most phenological metrics had a statistically significant (p 2 = 0.44). Models established with phenological metrics realized yield prediction before harvest in the three regions with R2 = 0.64, 0.67, and 0.72. Compared with the univariate Max-R2 models, the accuracy of models built with Max-R2 and phenology metrics improved. Thus, the phenology metrics obtained from MODIS-NDVI accurately reflect the corn characteristics and can be used for large-scale yield prediction. Overall, this study showed that phenology metrics derived from remote sensing vegetation indexes could be used as crop yield prediction variables and provide a reference for data organization and yield prediction with physical crop significance

Properties and Stability of Perilla Seed Protein-Stabilized Oil-in-Water Emulsions: Influence of Protein Concentration, pH, NaCl Concentration and Thermal Treatment

Perilla seed protein (PSP) was extracted from defatted perilla seed meal and applied in oil-in-water (O/W) emulsions as an emulsifier. We investigated the influences of protein concentration (0.25–1.5 wt %), pH (3.0–9.0), NaCl concentration (0–350 mmol/L) and thermal treatment (70–90 °C, 30 min) on the physical characteristics of O/W emulsions, including volume-average diameter, ζ-potential, interfacial protein concentration, microstructure and so on. Results showed that increasing PSP concentration would decrease the d4,3 and a 1.0 wt % PSP concentration was sufficient to ensure the stability of emulsion. Under pH 3.0–9.0, emulsions were stable except at pH 3.0–5.0 which was proximal to the isoelectric point (pH 4.5) of PSP. At high NaCl concentrations (250–350 mmol/L), the emulsions exhibited relatively lower absolute ζ-potential values and a large number of aggregated droplets. A moderate thermal treatment temperature (e.g., 70 °C) was favorable for the emulsion against aggregation and creaming. However, when 90 °C thermal treatment was performed, a clear layer separation was observed after 2 weeks storage and the emulsion showed a poor stability. The findings of this work are of great importance for the utilization and development of PSP as an emulsifier for food emulsions