Comparative analysis of CO2 reduction by soluble Escherichia coli formate dehydrogenase H and its selenocysteine-to-cysteine substitution variant

Metal-dependent formate dehydrogenases (Me-FDHs) are highly active CO2-reducing enzymes operating at low redox potentials and employ either molybdenum or tungsten to reduce the bound substrate. This makes them suitable for electrochemical applications such as fossil-free production of commodity chemicals utilizing renewable energy. Electrocatalytic CO2 reduction by cathode-immobilized Me-FDHs has been recently demonstrated and rational protein engineering can be used to optimize Me-FDHs for various carbon reduction reactions. In the present study, CO2 reduction by soluble monomeric Escherichia coli formate dehydrogenase H (EcFDH-H) was demonstrated and the function of its nucleophilic selenocysteine residue as a transient ligand of a centrally bound molybdenum atom was investigated. Kinetic analysis of the wildtype enzyme revealed maximum CO2 reduction rates of 44 ± 6 s−1 at pH 5.8 that was decreased to 19% and 0% in the case of selenocysteine substitution with the structural homologues cysteine and serine, respectively. Further selenocysteine-to-cysteine substitution effects included an increased acid tolerance as well as stronger inhibition by nitrate and azide indicating a shift of the Mo oxidation state from IV to VI. Conversely, a destabilizing effect on the oxidized Mo(VI) center could be assigned to the native selenocysteine residue that may facilitate the observed efficient CO2 reduction by rapid transition between Mo oxidation states. Taken together, the performed characterization of EcFDH-H as a catalyst for CO2 reduction and the selenocysteine substitution analysis furthers the understanding of the active-site structure of Me-FDHs and thereby supports the development of more efficient biocatalysts for CO2 reduction

Comparative analysis of CO2 reduction by soluble Escherichia coli formate dehydrogenase H and its selenocysteine-to-cysteine substitution variant

Metal-dependent formate dehydrogenases (Me-FDHs) are highly active CO2-reducing enzymes operating at low redox potentials and employ either molybdenum or tungsten to reduce the bound substrate. This makes them suitable for electrochemical applications such as fossil-free production of commodity chemicals utilizing renewable energy. Electrocatalytic CO2 reduction by cathode-immobilized Me-FDHs has been recently demonstrated and rational protein engineering can be used to optimize Me-FDHs for various carbon reduction reactions. In the present study, CO2 reduction by soluble monomeric Escherichia coli formate dehydrogenase H (EcFDH-H) was demonstrated and the function of its nucleophilic selenocysteine residue as a transient ligand of a centrally bound molybdenum atom was investigated. Kinetic analysis of the wildtype enzyme revealed maximum CO2 reduction rates of 44 ± 6 s−1 at pH 5.8 that was decreased to 19% and 0% in the case of selenocysteine substitution with the structural homologues cysteine and serine, respectively. Further selenocysteine-to-cysteine substitution effects included an increased acid tolerance as well as stronger inhibition by nitrate and azide indicating a shift of the Mo oxidation state from IV to VI. Conversely, a destabilizing effect on the oxidized Mo(VI) center could be assigned to the native selenocysteine residue that may facilitate the observed efficient CO2 reduction by rapid transition between Mo oxidation states. Taken together, the performed characterization of EcFDH-H as a catalyst for CO2 reduction and the selenocysteine substitution analysis furthers the understanding of the active-site structure of Me-FDHs and thereby supports the development of more efficient biocatalysts for CO2 reduction

Experimental Studies of The Multi-cylinders Compound Profile Meshing Pair

In order to improve the wear resistance of the meshing pair in Single Screw Compressor, our team developed the multi-cylinders compound profile which could be used in the meshing pair instead of the traditional profile. In this paper, the reliability and utility of the new profile was verified by an experiment. In this experiment, a designed multi-cylinders compound profile meshing pair was applied in an oil-flooded single screw air compressor as the experimental prototype, then the prototype ran 2000 hours continuously in the experimental platform. and the displacement was detected with changing discharge pressure and rotating speed. It is shown that the prototype has a steady displacement, and the energy efficiency grade is very close to the best grade. The displacement is slightly reduced with the discharge pressure arising, but the reduction is far less than the original single line profile meshing pair machine. These prove that the new pair has good sealing performance. A comparative observation on the star wheel profile is conducted at the end of test, and the results demonstrate that the nodular cast iron star wheel new designed has high wear resistant property and good hydrodynamic lubrication characteristic. Thus, the nodular cast iron can be used to make star wheel to reduce the cost of the single screw instead of the expensive PEEK material

An Adaptive Context-Aware Transaction Model for Mobile and Ubiquitous Computing

Transaction management for mobile and ubiquitous computing (MUC)aims at providing mobile users with reliable and transparent services anytime anywhere. Traditional mobile transaction models built on client-proxy-server architecture cannot make this vision a reality because (1) in these models, base stations (proxy) are the prerequisite for mobile hosts (client) to connect with databases (server), and 2)few models consider context-based transaction management. In this paper, we propose a new network architecture for MUC transactions, with the goal that people can get online network access and transaction even while moving around; and design a context-aware transaction model and a context-driven coordination algorithm adaptive to dynamically changing MUC transaction context. The simulation results have demonstrated that our model and algorithm can significantly improve the successful ratio of MUC transactions

Leveraging ChatGPT for Power System Programming Tasks

The rapid digitalization of power systems has led to a significant increase in coding tasks for power engineers. This research article explores how ChatGPT, an advanced AI language model, can assist power engineers and researchers in a range of coding tasks. From simple to complex, we present three case studies to illustrate the benefits of ChatGPT in various coding scenarios. For routine tasks such as daily unit commitment, ChatGPT can increase efficiency by directly generating batch number of codes and reducing repetitive programming and debugging time for power engineers. For complex problems such as decentralized optimization of mul-ti-vector energy systems, ChatGPT can reduce the learning cost of power engineers on problem formulation and the choice of numerical solvers. For new problems without readily avaliable solutions such as ultra-fast unit commitment, ChatGPT can organize technology roadmap, gen-erate data and develop model and code. Furthermore, this paper discuss generic prompt ap-proaches for different tasks in power systems, providing insights for power engineers and re-searchers seeking to harness ChatGPT in terms of auto coding, new knowledge learning and new problem solving. The findings demonstrate the potential of ChatGPT as a powerful tool in the domain of power system engineering

DMRM: A Dual-channel Multi-hop Reasoning Model for Visual Dialog

Visual Dialog is a vision-language task that requires an AI agent to engage in a conversation with humans grounded in an image. It remains a challenging task since it requires the agent to fully understand a given question before making an appropriate response not only from the textual dialog history, but also from the visually-grounded information. While previous models typically leverage single-hop reasoning or single-channel reasoning to deal with this complex multimodal reasoning task, which is intuitively insufficient. In this paper, we thus propose a novel and more powerful Dual-channel Multi-hop Reasoning Model for Visual Dialog, named DMRM. DMRM synchronously captures information from the dialog history and the image to enrich the semantic representation of the question by exploiting dual-channel reasoning. Specifically, DMRM maintains a dual channel to obtain the question- and history-aware image features and the question- and image-aware dialog history features by a mulit-hop reasoning process in each channel. Additionally, we also design an effective multimodal attention to further enhance the decoder to generate more accurate responses. Experimental results on the VisDial v0.9 and v1.0 datasets demonstrate that the proposed model is effective and outperforms compared models by a significant margin.Comment: Accepted at AAAI 202

Gap analysis for DNA-based biomonitoring of aquatic ecosystems in China

DNA-based taxon identification is improving the assessment and management of biodiversity in rivers. However, the lack of comprehensive DNA barcode reference libraries and globally highly unequal coverage are still hindering the application prospects of this method worldwide. Here, we analyzed the COI barcode gap in two reference libraries, Barcode of Life Data Systems (BOLD) and NCBI GenBank, with a focus on three aquatic animal groups (freshwater fish, aquatic insects and molluscs) in Chinese rivers. Our data show gaps in barcode coverage (e.g., organisms without barcodes) of ca. 40–70% of taxa in these groups in the BOLD or NCBI GenBank database, respectively. These gaps can rise even further if the barcode thresholds are set to contain at least five reference sequences per taxon. Furthermore, most barcodes are from non-local samples, and only 14.4% (BOLD) and 28.8% (NCBI GenBank) of reference sequences were from organisms sampled in China, respectively. The pairwise genetic distance of local barcodes is 3 to 5 times lower than non-local barcodes, indicating that the latter may not be a good substitute. When looking at individual catchments, ca. 60% of the potentially occurring aquatic species have one or more barcodes, yet the barcode coverage varies slightly across ten major river catchments, ranging from 54.3% (Liao River basin) to 68.2% (Huai River basin). The taxa Salmoniformes and Perciformes in freshwater fish, Odonata and Diptera in aquatic insects, and Bivalvia in molluscs have the best barcode coverage in most catchments (mean coverage >70%). This study gives the first overview and current status of barcode reference libraries of three major aquatic animal groups in Chinese rivers. Our results will help to better interpret current metabarcoding studies from China, and also provide a basis to develop a strategy of filling the gaps in the reference libraries of aquatic species in China