Lightweight Detection Network Based on Sub-Pixel Convolution and Objectness-Aware Structure for UAV Images.

Unmanned Aerial Vehicles (UAVs) can serve as an ideal mobile platform in various situations. Real-time object detection with on-board apparatus provides drones with increased flexibility as well as a higher intelligence level. In order to achieve good detection results in UAV images with complex ground scenes, small object size and high object density, most of the previous work introduced models with higher computational burdens, making deployment on mobile platforms more difficult.This paper puts forward a lightweight object detection framework. Besides being anchor-free, the framework is based on a lightweight backbone and a simultaneous up-sampling and detection module to form a more efficient detection architecture. Meanwhile, we add an objectness branch to assist the multi-class center point prediction, which notably improves the detection accuracy and only takes up very little computing resources. The results of the experiment indicate that the computational cost of this paper is 92.78% lower than the CenterNet with ResNet18 backbone, and the mAP is 2.8 points higher on the Visdrone-2018-VID dataset. A frame rate of about 220 FPS is achieved. Additionally, we perform ablation experiments to check on the validity of each part, and the method we propose is compared with other representative lightweight object detection methods on UAV image datasets.

Directed evolution and metabolic engineering generate an Escherichia coli cell factory for de novo production of 4-hydroxymandelate.

4-hydroxymandelate is a high-value aromatic compound used in the medicine, cosmetics, food, and chemical industry. However, existing natural extraction and chemical synthesis methods are costly and lead to environmental pollution. This study employed metabolic engineering and directed evolution strategies for de novo 4-hydroxymandelate biosynthesis. Two key challenges were addressed: insufficient precursor supply and limited activity of crucial enzymes. Through gene overexpression and multi-level gene interference using CRISPRi, An Escherichia coli chassis capable of producing the key precursor 4-hydroxyphenylpyruvate and the titer reached 5.05 mM (0.91 g/L). A mutant clone was obtained, HmaSV152G, which showed a 5.13-fold improvement in the catalytic rate. During fermentation, a high production of 194.87 mM (32.768 g/L) 4-hydroxymandelate was achieved in 76 h with a batch supply of glucose in a 5-L bioreactor. This study demonstrated the great potential of biosensors in protein engineering and provides a reference for large-scale production of other high-value aromatic compounds.

Identification of QTL controlling volatile terpene contents in tea plant (Camellia sinensis) using a high-aroma 'Huangdan' x 'Jinxuan' F1 population.

Aroma is an important factor affecting the character and quality of tea. The improvement of aroma trait is a crucial research direction of tea plant breeding. Volatile terpenes, as the major contributors to the floral odors of tea products, also play critical roles in the defense responses of plants to multiple stresses. However, previous studies have largely focused on the aroma formation during the manufacture of tea or the comparison of raw tea samples. The mechanisms causing different aroma profiles between tea cultivars have remained underexplored. In the current study, a high-density genetic linkage map of tea plant was constructed based on an F1 population of 'Huangdan' × 'Jinxuan' using genotyping by sequencing. This linkage map covered 1754.57 cM and contained 15 linkage groups with a low inter-marker distance of 0.47 cM. A total of 42 QTLs associated with eight monoterpene contents and 12 QTLs associated with four sesquiterpenes contents were identified with the average PVE of 12.6% and 11.7% respectively. Furthermore, six candidate genes related to volatile terpene contents were found in QTL cluster on chromosome 5 by RNA-seq analysis. This work will enrich our understanding of the molecular mechanism of volatile terpene biosynthesis and provide a theoretical basis for tea plant breeding programs for aroma quality improvement.

Non-Volatile Metabolic Profiling and Regulatory Network Analysis in Fresh Shoots of Tea Plant and Its Wild Relatives.

There are numerous non-volatile metabolites in the fresh shoots of tea plants. However, we know little about the complex relationship between the content of these metabolites and their gene expression levels. In investigating this, this study involved non-volatile metabolites from 68 accessions of tea plants that were detected and identified using untargeted metabolomics. The tea accessions were divided into three groups from the results of a principal component analysis based on the relative content of the metabolites. There were differences in variability between the primary and secondary metabolites. Furthermore, correlations among genes, gene metabolites, and metabolites were conducted based on Pearson's correlation coefficient (PCC) values. This study offered several significant insights into the co-current network of genes and metabolites in the global genetic background. Thus, the study is useful for providing insights into the regulatory relationship of the genetic basis for predominant metabolites in fresh tea shoots.