Immunity of replicating Mu to self-integration: a novel mechanism employing MuB protein

We describe a new immunity mechanism that protects actively replicating/transposing Mu from self-integration. We show that this mechanism is distinct from the established cis-immunity mechanism, which operates by removal of MuB protein from DNA adjacent to Mu ends. MuB normally promotes integration into DNA to which it is bound, hence its removal prevents use of this DNA as target. Contrary to what might be expected from a cis-immunity mechanism, strong binding of MuB was observed throughout the Mu genome. We also show that the cis-immunity mechanism is apparently functional outside Mu ends, but that the level of protection offered by this mechanism is insufficient to explain the protection seen inside Mu. Thus, both strong binding of MuB inside and poor immunity outside Mu testify to a mechanism of immunity distinct from cis-immunity, which we call 'Mu genome immunity'. MuB has the potential to coat the Mu genome and prevent auto-integration as previously observed in vitro on synthetic A/T-only DNA, where strong MuB binding occluded the entire bound region from Mu insertions. The existence of two rival immunity mechanisms within and outside the Mu genome, both employing MuB, suggests that the replicating Mu genome must be segregated into an independent chromosomal domain. We propose a model for how formation of a 'Mu domain' may be aided by specific Mu sequences and nucleoid-associated proteins, promoting polymerization of MuB on the genome to form a barrier against self-integration

Enzyme or whole cell immobilization for efficient biocatalysis: focusing on novel supporting platforms and immobilization techniques

Biocatalysts represented by enzymes and enzyme-containing whole cells are generally fragile and easily inactivated in practical application conditions. The immobilization concept and techniques have been recognized as classic and powerful strategy for tackling such challenges (Hanefeld et al., 2009). Based on this background, a special Research Topic entitled Enzyme or Whole Cell Immobilization for Efficient Biocatalysis: Focusing on Novel Supporting Platforms and Immobilization Techniques had been organized and presented in the platform of Frontiers in Bioengineering and Biotechnology, which aimed to collect different insights and latest findings regarding but not limited to new theories, techniques and methodologies in this area. Over the past year since Sept. 2019, this Research Topic has attracted 242 authors from more than 10 countries to participate and contribute their manuscripts. Consequently, this special issue has selected and presented 40 peer-reviewed articles to meet the readers, including 31 Original Researches, four Brief Research Reports, four Reviews, and one General Commentary, which involved various aspects and every corner of this area

Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach

The electric vehicle (EV) and electric vehicle charging station (EVCS) have been widely deployed with the development of large-scale transportation electrifications. However, since charging behaviors of EVs show large uncertainties, the forecasting of EVCS charging power is non-trivial. This paper tackles this issue by proposing a reinforcement learning assisted deep learning framework for the probabilistic EVCS charging power forecasting to capture its uncertainties. Since the EVCS charging power data are not standard time-series data like electricity load, they are first converted to the time-series format. On this basis, one of the most popular deep learning models, the long short-term memory (LSTM) is used and trained to obtain the point forecast of EVCS charging power. To further capture the forecast uncertainty, a Markov decision process (MDP) is employed to model the change of LSTM cell states, which is solved by our proposed adaptive exploration proximal policy optimization (AePPO) algorithm based on reinforcement learning. Finally, experiments are carried out on the real EVCSs charging data from Caltech, and Jet Propulsion Laboratory, USA, respectively. The results and comparative analysis verify the effectiveness and outperformance of our proposed framework.Comment: Accepted by IEEE Transactions on Intelligent Vehicle

Bismuth coordination networks containing deferiprone: synthesis, characterisation, stability and antibacterial activity

A series of bismuth–dicarboxylate–deferiprone coordination networks have been prepared and structurally characterised. The new compounds have been demonstrated to release the iron overload drug deferiprone on treatment with PBS and have also been shown to have antibacterial activity against H. pylori

C3: Zero-shot Text-to-SQL with ChatGPT

This paper proposes a ChatGPT-based zero-shot Text-to-SQL method, dubbed C3, which achieves 82.3\% in terms of execution accuracy on the holdout test set of Spider and becomes the state-of-the-art zero-shot Text-to-SQL method on the Spider Challenge. C3 consists of three key components: Clear Prompting (CP), Calibration with Hints (CH), and Consistent Output (CO), which are corresponding to the model input, model bias and model output respectively. It provides a systematic treatment for zero-shot Text-to-SQL. Extensive experiments have been conducted to verify the effectiveness and efficiency of our proposed method