Investigating the genetic basis and regulatory mechanism of folate metabolism in maize (Zea mays)

Tong Lian. (2022). Investigating the genetic basis and regulatory mechanism of folate metabolism in maize (Zea mays) (PhD Dissertation in English). Gembloux, Belgium, Gembloux Agro-Bio Tech, University of Liège, 132 p., 12 tables, 29 figures. Abstract — Folates, one group of the essential B vitamins, play a crucial role in DNA biosynthesis, amino acid metabolism, and DNA methylation during the development and growth of organisms. Daily diet is the major source of folates for human beings. The low-level intake of folates may cause increase of the risk of number of serious diseases such as cancers and neural tube defects. The folate contents in crop plants, especially cereals, are quite low. Therefore, it’s necessary to improve the folate accumulation in crops, an approach called biofortification, to alleviate the folate deficiency problem worldwide. To this end, it’s imperative to understand the genetic basis and regulatory mechanism of folate metabolism in crops. Here we have designed two projects to identify the key metabolic pathways or genes that may contribute to folate accumulation in maize kernel. (1) Comparative transcriptome analysis reveals mechanisms of folate accumulation in maize grains Previously, the complexity of folate accumulation in the early stages of maize kernel development was reported, but the mechanisms remain unclear. In this study, two maize inbred lines, DAN3130 and JI63, with different folate accumulation patterns and levels in mature kernels were used to investigate the transcriptional regulation of folate metabolism by comparative transcriptome analysis. It was demonstrated that the folate accumulation during DAP 24 to kernel maturity was controlled by the circumjacent pathways of folate biosynthesis, such as pyruvate metabolism, glutamate metabolism, and serine/glycine metabolism. In addition, the differences in folate accumulation between these two inbred lines were found to be related to those genes involved in folate metabolism, including those in pteridine branch, para-aminobenzoate branch, serine/tetrahydrofolate (THF)/5-methyltetrahydrofolate cycle, and conversion of THF monoglutamate to THF polyglutamate. Those observations provided insight into the mechanisms underlying folate metabolism during maize kernel formation, thus being helpful for folate biofortification research in maize. (2) Genetic mapping of folate QTLs using a segregated population in maize (Zea mays L.) To increase folate accumulation in edible parts of crops is of great importance for human health. Molecular breeding is a feasible and efficient strategy for folate biofortification, but somewhat constrained by shortage of the knowledge on folate metabolism at molecular level. In this study, we reported the genetic mapping of the quantitative trait loci (QTLs) linking with folate accumulation levels using a segregated population crossed by two maize lines, one high in folates (GEMS31) and the other low in folates (DAN3130). As a result, two QTLs on chromosome 5 were obtained by association of whole-exome sequencing with kernel folate profiling. These QTLs were confirmed by bulk segregant analysis using pooled DNA in F6 and kernel folate profiling in F7, with an overlap with the QTLs identified in another segregated population. One candidate gene, named ZmCTM, was identified as a gene encoding a folate-binding protein that played an important role in folate metabolism in maize. Loss of ZmCTM function enhanced 5-methyl-tetrahydrofolate accumulation by three folds. We concluded that ZmCTM may participate in folate metabolism by converting 5-methyl-tetrahydrofolate to other folate derivatives in maize

MEDL-U: Uncertainty-aware 3D Automatic Annotation based on Evidential Deep Learning

Advancements in deep learning-based 3D object detection necessitate the availability of large-scale datasets. However, this requirement introduces the challenge of manual annotation, which is often both burdensome and time-consuming. To tackle this issue, the literature has seen the emergence of several weakly supervised frameworks for 3D object detection which can automatically generate pseudo labels for unlabeled data. Nevertheless, these generated pseudo labels contain noise and are not as accurate as those labeled by humans. In this paper, we present the first approach that addresses the inherent ambiguities present in pseudo labels by introducing an Evidential Deep Learning (EDL) based uncertainty estimation framework. Specifically, we propose MEDL-U, an EDL framework based on MTrans, which not only generates pseudo labels but also quantifies the associated uncertainties. However, applying EDL to 3D object detection presents three primary challenges: (1) relatively lower pseudolabel quality in comparison to other autolabelers; (2) excessively high evidential uncertainty estimates; and (3) lack of clear interpretability and effective utilization of uncertainties for downstream tasks. We tackle these issues through the introduction of an uncertainty-aware IoU-based loss, an evidence-aware multi-task loss function, and the implementation of a post-processing stage for uncertainty refinement. Our experimental results demonstrate that probabilistic detectors trained using the outputs of MEDL-U surpass deterministic detectors trained using outputs from previous 3D annotators on the KITTI val set for all difficulty levels. Moreover, MEDL-U achieves state-of-the-art results on the KITTI official test set compared to existing 3D automatic annotators.Comment: 6 pages Main, 1 page Reference, 5 pages Appendi

Few-shot Object Detection with Refined Contrastive Learning

Due to the scarcity of sampling data in reality, few-shot object detection (FSOD) has drawn more and more attention because of its ability to quickly train new detection concepts with less data. However, there are still failure identifications due to the difficulty in distinguishing confusable classes. We also notice that the high standard deviation of average precisions reveals the inconsistent detection performance. To this end, we propose a novel FSOD method with Refined Contrastive Learning (FSRC). A pre-determination component is introduced to find out the Resemblance Group (GR) from novel classes which contains confusable classes. Afterwards, refined contrastive learning (RCL) is pointedly performed on this group of classes in order to increase the inter-class distances among them. In the meantime, the detection results distribute more uniformly which further improve the performance. Experimental results based on PASCAL VOC and COCO datasets demonstrate our proposed method outperforms the current state-of-the-art research. FSRC can not only decouple the relevance of confusable classes to get a better performance, but also makes predictions more consistent by reducing the standard deviation of the AP of classes to be detected

Decoupled DETR For Few-shot Object Detection

Few-shot object detection (FSOD), an efficient method for addressing the severe data-hungry problem, has been extensively discussed. Current works have significantly advanced the problem in terms of model and data. However, the overall performance of most FSOD methods still does not fulfill the desired accuracy. In this paper we improve the FSOD model to address the severe issue of sample imbalance and weak feature propagation. To alleviate modeling bias from data-sufficient base classes, we examine the effect of decoupling the parameters for classes with sufficient data and classes with few samples in various ways. We design a base-novel categories decoupled DETR (DeDETR) for FSOD. We also explore various types of skip connection between the encoder and decoder for DETR. Besides, we notice that the best outputs could come from the intermediate layer of the decoder instead of the last layer; therefore, we build a unified decoder module that could dynamically fuse the decoder layers as the output feature. We evaluate our model on commonly used datasets such as PASCAL VOC and MSCOCO. Our results indicate that our proposed module could achieve stable improvements of 5% to 10% in both fine-tuning and meta-learning paradigms and has outperformed the highest score in recent works