Perilla frutescen is used as traditional food and medicine in East Asia. Its seeds contain high levels of α-linolenic acid (ALA), which is important for health, but is scarce in our daily meals. Previous reports on RNA-seq of perilla seed had identified fatty acid (FA) and triacylglycerol (TAG) synthesis genes, but the underlying mechanism of ALA biosynthesis and its regulation still need to be further explored. So we conducted Illumina RNA-sequencing in seven temporal developmental stages of perilla seeds. Sequencing generated a total of 127 million clean reads, containing 15.88 Gb of valid data. The de novo assembly of sequence reads yielded 64,156 unigenes with an average length of 777 bp. A total of 39,760 unigenes were annotated and 11,693 unigenes were found to be differentially expressed in all samples. According to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, 486 unigenes were annotated in the “lipid metabolism” pathway. Of these, 150 unigenes were found to be involved in fatty acid (FA) biosynthesis and triacylglycerol (TAG) assembly in perilla seeds. A coexpression analysis showed that a total of 104 genes were highly coexpressed (r > 0.95). The coexpression network could be divided into two main subnetworks showing over expression in the medium or earlier and late phases, respectively. In order to identify the putative regulatory genes, a transcription factor (TF) analysis was performed. This led to the identification of 45 gene families, mainly including the AP2-EREBP, bHLH, MYB, and NAC families, etc. After coexpression analysis of TFs with highly expression of FAD2 and FAD3 genes, 162 TFs were found to be significantly associated with two FAD genes (r > 0.95). Those TFs were predicted to be the key regulatory factors in ALA biosynthesis in perilla seed. The qRT-PCR analysis also verified the relevance of expression pattern between two FAD genes and partial candidate TFs. Although it has been reported that some TFs are involved in seed development, more direct evidence is still needed to verify their function. However, these findings can provide clues to reveal the possible molecular mechanisms of ALA biosynthesis and its regulation in perilla seed.

Perilla (Perilla frutescens), a traditional medicinal and oilseed crop in Asia, contains extremely high levels of polyunsaturated α-linolenic acid (ALA) (up to 60.9%) in its seeds. ALA biosynthesis is a multistep process catalyzed by fatty acid desaturases (FADs), but the FAD gene family in perilla has not been systematically characterized. Here, we identified 42 PfFADs in the perilla genome and classified them into five subfamilies. Subfamily members of PfFADs had similar exon/intron structures, conserved domain sequences, subcellular localizations, and cis-regulatory elements in their promoter regions. PfFADs also possessed various expression patterns. PfFAD3.1 was highly expressed in the middle stage of seed development, whereas PfFAD7/8.3 and PfFAD7/8.5 were highly expressed in leaf and later stages of seed development, respectively. Phylogenetic analysis revealed that the evolutionary features coincided with the functionalization of different subfamilies of PUFA desaturase. Heterologous overexpression of PfFAD3.1 in Arabidopsis thaliana seeds increased ALA content by 17.68%–37.03%. These findings provided insights into the characteristics and functions of PfFAD genes in perilla.

Electrochemical biosensors are one of the most emerging sensor technologies for many applications including disease diagnosis, environmental monitoring, and food safety, etc. They often rely on amplification strategies to achieve ultrasensitivity for the specific analytes of interest, as they feature extremely low abundance, such as ctDNA and other protein‐type cancer biomarkers. Among all the amplification strategies, hybridization chain reaction (HCR) is extremely cost‐effective, simple, enzyme‐free, and reacts under isothermal conditions, thus often employed in the electrochemical biosensors towards the goal of sensitivity enhancement. By coupling HCR and electrochemistry, it has proved to benefit a great variety of analytes, including ions, small molecules, nucleic acids, proteins, and cells, etc. Given the recent advance of HCR‐amplified electrochemical biosensors, here one aims to review the significant achievements of this platform over the past 10 years, covering their design in HCR constructs in response to the respective targets, and their challenges and opportunities.

<p>In this paper, a novel reinforcement learning (RL) approach, continuous dynamic policy programming (CDPP) is proposed to tackle the issues of both learning stability and sample efficiency in the current RL methods with continuous actions.<br> The proposed method naturally extends the relative entropy regularization from the value function-based framework to the actor-critic (AC) framework of deep deterministic policy gradient (DDPG) to stabilize the learning process in continuous action space. It tackles the intractable softmax operation over continuous actions in the critic by Monte Carlo estimation and explores the practical advantages of the Mellowmax operator. A Boltzmann sampling policy is proposed to guide the exploration of actor following the relative entropy regularized critic.<br> Evaluated by several benchmark tasks, the proposed method clearly illustrates the positive impact of the relative entropy regularization including efficient exploration behavior and stable policy update in RL with continuous action space and successfully outperforms the related baseline approach in both sample efficiency and learning stability.</p>