An Exact Algorithm for Minimum Vertex Cover Problem

In this paper, we propose a branch-and-bound algorithm to solve exactly the minimum vertex cover (MVC) problem. Since a tight lower bound for MVC has a significant influence on the efficiency of a branch-and-bound algorithm, we define two novel lower bounds to help prune the search space. One is based on the degree of vertices, and the other is based on MaxSAT reasoning. The experiment confirms that our algorithm is faster than previous exact algorithms and can find better results than heuristic algorithms

Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation

Recommender systems are essential to various fields, e.g., e-commerce, e-learning, and streaming media. At present, graph neural networks (GNNs) for session-based recommendations normally can only recommend items existing in users' historical sessions. As a result, these GNNs have difficulty recommending items that users have never interacted with (new items), which leads to a phenomenon of information cocoon. Therefore, it is necessary to recommend new items to users. As there is no interaction between new items and users, we cannot include new items when building session graphs for GNN session-based recommender systems. Thus, it is challenging to recommend new items for users when using GNN-based methods. We regard this challenge as '\textbf{G}NN \textbf{S}ession-based \textbf{N}ew \textbf{I}tem \textbf{R}ecommendation (GSNIR)'. To solve this problem, we propose a dual-intent enhanced graph neural network for it. Due to the fact that new items are not tied to historical sessions, the users' intent is difficult to predict. We design a dual-intent network to learn user intent from an attention mechanism and the distribution of historical data respectively, which can simulate users' decision-making process in interacting with a new item. To solve the challenge that new items cannot be learned by GNNs, inspired by zero-shot learning (ZSL), we infer the new item representation in GNN space by using their attributes. By outputting new item probabilities, which contain recommendation scores of the corresponding items, the new items with higher scores are recommended to users. Experiments on two representative real-world datasets show the superiority of our proposed method. The case study from the real-world verifies interpretability benefits brought by the dual-intent module and the new item reasoning module. The code is available at Github: https://github.com/Ee1s/NirGNNComment: 10 Pages, 6 figures, WWW'202

Evolved Bacterial Biosensor for Arsenite Detection in Environmental Water

Arsenic, a ubiquitous presence in the biosphere, often occurs from both natural and anthropogenic sources. Bacterial biosensors based on genetically engineered bacteria have promising applications in detecting the chemical compound and its toxicity. However, most of the bacteria biosensors take advantage of the existing wild-type substrate-induced promoters, which are often low in specificity, affinity and sensitivity, and thus limiting their applications in commercial or field use. In this study, we developed an in vivo evolution procedure with a bidirectional selection scheme for improving the sensitivity of an arsenite-responsive bacterial biosensor through optimization of the inducible operon. As a proof of concept, we evolved the arsenite-induced arsR operon for both low background and high expression through three successive rounds of fluorescence activated cell sorting (FACS) with bidirectional strategy. An arsR operon variant with 12-fold higher activity over the control was isolated, confirming multiple rounds of construction and screening of mutation library, as described here, can be efficiently applied to bacterial biosensor optimization. The evolved arsenite-responsive biosensor demonstrated an excellent performance in the detection of low trace arsenite in environmental water. These results indicate that the technologies of directed evolution could be used to improve the performance of bacterial biosensors, which will be helpful in promoting the practical application of bacterial biosensors

Synergistic Effects Induced by a Low Dose of Diesel Particulate Extract and Ultraviolet‑A in <i>Caenorhabditis elegans</i>: DNA Damage-Triggered Germ Cell Apoptosis

Diesel exhaust has been classified as a potential carcinogen and is associated with various health effects. A previous study showed that the doses for manifesting the mutagenetic effects of diesel exhaust could be reduced when coexposed with ultraviolet-A (UVA) in a cellular system. However, the mechanisms underlying synergistic effects remain to be clarified, especially in an <i>in vivo</i> system. In the present study, using <i>Caenorhabditis elegans</i> (<i>C. elegans</i>) as an <i>in vivo</i> system we studied the synergistic effects of diesel particulate extract (DPE) plus UVA, and the underlying mechanisms were dissected genetically using related mutants. Our results demonstrated that though coexposure of wild type worms at young adult stage to low doses of DPE (20 μg/mL) plus UVA (0.2, 0.5, and 1.0 J/cm²) did not affect worm development (mitotic germ cells and brood size), it resulted in a significant induction of germ cell death. Using the strain of <i>hus-1::gfp</i>, distinct foci of HUS-1::GFP was observed in proliferating germ cells, indicating the DNA damage after worms were treated with DPE plus UVA. Moreover, the induction of germ cell death by DPE plus UVA was alleviated in single-gene loss-of-function mutations of core apoptotic, checkpoint HUS-1, CEP-1/p53, and MAPK dependent signaling pathways. Using a reactive oxygen species (ROS) probe, it was found that the production of ROS in worms coexposed to DPE plus UVA increased in a time-dependent manner. In addition, employing a singlet oxygen (¹O₂) trapping probe, 2,2,6,6-tetramethyl-4-piperidone, coupled with electron spin resonance analysis, we demonstrated the increased ¹O₂ production in worms coexposed to DPE plus UVA. These results indicated that UVA could enhance the apoptotic induction of DPE at low doses through a DNA damage-triggered pathway and that the production of ROS, especially ¹O₂, played a pivotal role in initiating the synergistic process