Genome-Wide Analysis and Expression Profiling of the Phenylalanine Ammonia-Lyase Gene Family in Solanum tuberosum.

Phenylalanine ammonia-lyase is one of the most widely studied enzymes in the plant kingdom. It is a crucial pathway from primary metabolism to significant secondary phenylpropanoid metabolism in plants, and plays an essential role in plant growth, development, and stress defense. Although PAL has been studied in many actual plants, only one report has been reported on potato, one of the five primary staple foods in the world. In this study, 14 StPAL genes were identified in potato for the first time using a genome-wide bioinformatics analysis, and the expression patterns of these genes were further investigated using qRT-PCR. The results showed that the expressions of StPAL1, StPAL6, StPAL8, StPAL12, and StPAL13 were significantly up-regulated under drought and high temperature stress, indicating that they may be involved in the stress defense of potato against high temperature and drought. The expressions of StPAL1, StPAL2, and StPAL6 were significantly up-regulated after MeJa hormone treatment, indicating that these genes are involved in potato chemical defense mechanisms. These three stresses significantly inhibited the expression of StPAL7, StPAL10, and StPAL11, again proving that PAL is a multifunctional gene family, which may give plants resistance to multiple and different stresses. In the future, people may improve critical agronomic traits of crops by introducing other PAL genes. This study aims to deepen the understanding of the versatility of the PAL gene family and provide a valuable reference for further genetic improvement of the potato.

BPJDet: Extended Object Representation for Generic Body-Part Joint Detection.

Detection of human body and its parts has been intensively studied. However, most of CNNs-based detectors are trained independently, making it difficult to associate detected parts with body. In this paper, we focus on the joint detection of human body and its parts. Specifically, we propose a novel extended object representation integrating center-offsets of body parts, and construct an end-to-end generic Body-Part Joint Detector (BPJDet). In this way, body-part associations are neatly embedded in a unified representation containing both semantic and geometric contents. Therefore, we can optimize multi-loss to tackle multi-tasks synergistically. Moreover, this representation is suitable for anchor-based and anchor-free detectors. BPJDet does not suffer from error-prone post matching, and keeps a better trade-off between speed and accuracy. Furthermore, BPJDet can be generalized to detect body-part or body-parts of either human or quadruped animals. To verify the superiority of BPJDet, we conduct experiments on datasets of body-part (CityPersons, CrowdHuman and BodyHands) and body-parts (COCOHumanParts and Animals5C). While keeping high detection accuracy, BPJDet achieves state-of-the-art association performance on all datasets. Besides, we show benefits of advanced body-part association capability by improving performance of two representative downstream applications: accurate crowd head detection and hand contact estimation.