Mechanism of Escherichia coli Lethality Caused by Overexpression of flhDC, the Flagellar Master Regulator Genes, as Revealed by Transcriptome Analysis.

The flhDC operon of Escherichia coli encodes a transcription factor that initiates flagella synthesis, elevates flagella construction and enhances cell motility, which all are energetically costly and highly regulated processes. In this study, we found that overexpression of flhDC genes from a strong regulatable pN15E6 plasmid could inhibit the growth of E. coli host cells and even eventually cause death. We used transcriptome analysis to investigate the mechanism of flhDC overexpression lethal to host bacteria. The results showed that a total of 568 differentially expressed genes (DEGs), including 378 up-regulated genes and 190 down-regulated genes were detected when the flhDC genes were over-expressed. Functional enrichment analysis results showed that the DEGs are related to a series of crucial biomolecular processes, including flagella synthesis, oxidative phosphorylation and pentose phosphate pathways, etc. We then examined, using RT-qPCR, the expression of key genes of the oxidative phosphorylation pathway at different time points after induction. Results showed that their expression increased in the early stage and decreased afterward, which was suggested to be the result of feedback on the overproduction of ROS, a strong side effect product of the elevated oxidative phosphorylation process. To further verify the level of ROS output, flhDC over-expressed bacteria cells were stained with DCHF-DA and a fluorescence signal was detected using flow cytometry. Results showed that the level of ROS output was higher in cells with over-expressed flhDC than in normal controls. Besides, we found upregulation of other genes (recN and zwf) that respond to ROS damage. This leads to the conclusion that the bacterial death led by the overexpression of flhDC genes is caused by damage from ROS overproduction, which leaked from the oxidative phosphorylation pathway.

CeCR: Cross-entropy contrastive replay for online class-incremental continual learning.

Aiming at the realization of learning continually from an online data stream, replay-based methods have shown superior potential. The main challenge of replay-based methods is the selection of representative samples which are stored in the buffer and replayed. In this paper, we propose the Cross-entropy Contrastive Replay (CeCR) method in the online class-incremental setting. First, we present the Class-focused Memory Retrieval method that proceeds the class-level sampling without replacement. Second, we put forward the class-mean approximation memory update method that selectively replaces the mistakenly classified training samples with samples of current input batch. In addition, the Cross-entropy Contrastive Loss is proposed to implement the model training with obtaining more solid knowledge to achieve effective learning. Experiments show that the CeCR method has comparable or improved performance in two benchmark datasets in comparison with the state-of-the-art methods.

HRBUST-LLPED: A Benchmark Dataset for Wearable Low-Light Pedestrian Detection.

Detecting pedestrians in low-light conditions is challenging, especially in the context of wearable platforms. Infrared cameras have been employed to enhance detection capabilities, whereas low-light cameras capture the more intricate features of pedestrians. With this in mind, we introduce a low-light pedestrian detection (called HRBUST-LLPED) dataset by capturing pedestrian data on campus using wearable low-light cameras. Most of the data were gathered under starlight-level illumination. Our dataset annotates 32,148 pedestrian instances in 4269 keyframes. The pedestrian density reaches high values with more than seven people per image. We provide four lightweight, low-light pedestrian detection models based on advanced YOLOv5 and YOLOv8. By training the models on public datasets and fine-tuning them on the HRBUST-LLPED dataset, our model obtained 69.90% in terms of AP@0.5:0.95 and 1.6 ms for the inference time. The experiments demonstrate that our research can assist in advancing pedestrian detection research by using low-light cameras in wearable devices.

Subspace structural constraint-based discriminative feature learning via nonnegative low rank representation.

Feature subspace learning plays a significant role in pattern recognition, and many efforts have been made to generate increasingly discriminative learning models. Recently, several discriminative feature learning methods based on a representation model have been proposed, which have not only attracted considerable attention but also achieved success in practical applications. Nevertheless, these methods for constructing the learning model simply depend on the class labels of the training instances and fail to consider the essential subspace structural information hidden in them. In this paper, we propose a robust feature subspace learning approach based on a low-rank representation. In our approach, the low-rank representation coefficients are considered as weights to construct the constraint item for feature learning, which can introduce a subspace structural similarity constraint in the proposed learning model for facilitating data adaptation and robustness. Moreover, by placing the subspace learning and low-rank representation into a unified framework, they can benefit each other during the iteration process to realize an overall optimum. To achieve extra discrimination, linear regression is also incorporated into our model to enforce the projection features around and close to their label-based centers. Furthermore, an iterative numerical scheme is designed to solve our proposed objective function and ensure convergence. Extensive experimental results obtained using several public image datasets demonstrate the advantages and effectiveness of our novel approach compared with those of the existing methods.

Ruthenium catalysts supported on high-surface-area zirconia for the catalytic wet oxidation of N,N-dimethyl formamide.

Three weight percent ruthenium catalysts were prepared by incipient-wet impregnation of two different zirconium oxides, and characterized by BET, XRD and TPR. Their activity was evaluated in the catalytic wet oxidation (CWO) of N,N-dimethyl formamide (DMF) in an autoclave reactor. Due to a better dispersion, Ru catalyst supported on a high-surface-area zirconia (Ru/ZrO(2)-A) possessed higher catalytic properties. Due to over-oxidation of Ru particles, the catalytic activity of the both catalysts decreased during successive tests. The effect of oxygen partial pressure and reaction temperature on the DMF reactivity in the CWO on Ru/ZrO(2)-A was also investigated. 98.6% of DMF conversion was obtained through hydrothermal decomposition within 300 min at conditions of 200 degrees C and 2.0 MPa of nitrogen pressure. At 240 degrees C and 2.0 MPa of oxygen pressure 98.3% of DMF conversion was obtained within 150 min.

Self-supervised sparse coding scheme for image classification based on low rank representation.

Recently, sparse representation, which relies on the underlying assumption that samples can be sparsely represented by their labeled neighbors, has been applied with great success to image classification problems. Through sparse representation-based classification (SRC), the label can be assigned with minimum residual between the sample and its synthetic version with class-specific coding, which means that the coding scheme is the most significant factor for classification accuracy. However, conventional SRC-based coding schemes ignore dependency among the samples, which leads to an undesired result that similar samples may be coded into different categories due to quantization sensitivity. To address this problem, in this paper, a novel approach based on self-supervised sparse representation is proposed for image classification. In the proposed approach, the manifold structure of samples is firstly exploited with low rank representation. Next, the low-rank representation matrix is used to characterize the similarity of samples in order to establish a self-supervised sparse coding model, which aims to preserve the local structure of codings for similar samples. Finally, a numerical algorithm utilizing the alternating direction method of multipliers (ADMM) is developed to obtain the approximate solution. Experiments on several publicly available datasets validate the effectiveness and efficiency of our proposed approach compared with existing state-of-the-art methods.