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Comprehensive Summary: Recently, photodynamic therapy (PDT) has attracted wide attention due to its less susceptibility to drug resistance, broad‐spectrum biocidal activity and biosafety in normal tissues. However, the traditional photosensitizers (PSs) face the disadvantage of poor therapeutic efficacy due to the requirement of an aerobic environment to generate 1O2 through Type ІI pathway. Herein, we designed and synthesized a novel cationic conjugated oligomer oligo(phenylene vinylene) (OPV) and studied its antibacterial photodynamic activity against both Gram‐negative Escherichia coli (E. coli) and Gram‐positive bacteria methicillin‐resistant Staphylococcus aureus (MRSA). Importantly, the OPV can rapidly produce reactive oxygen species (ROS) through double pathways, Type I and II mechanism under white light irradiation, and efficiently kill E. coli and MRSA at a nanomolar level. The dual type photosensitizing capability makes OPV promising for enhanced PDT to treat pathogens and tumors in complex environments. [ABSTRACT FROM AUTHOR]

Lithology identification is the research basis in oil and gas reservoir exploration and is critical for the formation characterization and reservoir development. Traditional lithofacies identification methods rely on the knowledge and experience of geologists and are usually done manually. With the development of deep learning technology and its application in the field of geophysics, lithofacies identification based on deep-learning approach has attracted great attention in recent years. Well logging data have obvious sequence characteristics. Therefore, we propose to use a bidirectional long and short-term memory (BiLSTM) neural network to learn long-term information for more effective lithology facies classification. In addition, we also perform correlation analysis on the input well logging curves and conduct median filter at different scales according to the correlation degree to extract the geological features within data itself and discard the interference of noise. The raw data-based lithofacies identification can reflect the noise resistance of the neural network model to some extent, while the filtered data are more beneficial for the model to extract the geological features correlated with lithofacies and provide accurate classification results. We validate our proposed framework by applying it to a case study from the Council Grove gas reservoir located in Kansas. Furthermore, we compare the effect of input data and network model on the identification results. The experimental results show that the proposed lithofacies identification method has higher classification accuracy. [ABSTRACT FROM AUTHOR]

Seismic inversion is one of the important methods to realize reservoir prediction in oil and gas field exploration. The seismic stochastic inversion based on Metropolis-Hastings algorithm makes full use of well logging data and improves the vertical resolution of inversion results. However, conventional Metropolis-Hastings algorithm has slow convergence speed and poor stability. In this paper, an elastic impedance inversion method based on optimized Metropolis-Hastings algorithm is established by combining quantum annealing algorithm with Metropolis-Hastings algorithm. The feasibility of the method is verified by model calculation and actual data test.

Integrated Project Delivery (IPD) is regarded as an effective project delivery method that can deal with the challenge of the rapid development of the architecture, engineering, and construction (AEC) industry. In the IPD team, the alliance profit is not distributed fairly and effectively due to uncertainty, preventing the achievement of the IPD project goals. This study focuses on optimizing the profit distribution among stakeholders in IPD projects and uses quadratic programming models to solve fuzzy cooperative games in the IPD. A payoff function is used in the fuzzy alliance to determine the characteristics of the interval-valued fuzzy numbers, and different weights of the alliance and the efficiency of the player's participation in the IPD are considered in the profit distribution. A case study is conducted, and the results of the proposed method are compared with those of crisp cooperative games. The results show that the fuzzy cooperative game increases the profit of participants in IPD projects. It is more practical to use weight fuzzy cooperative games than crisp games to express imputation. Moreover, the quadratic programming models and methods result in a fair and efficient profit distribution scheme in IPD projects. [ABSTRACT FROM AUTHOR]

d-Amino acids such as d-alanine are substrates for bacterial peptidoglycan biosynthesis and are selectively taken up by bacteria and not mammalian cells. Consequently, d-amino acid metabolism is an attractive target for antibiotic discovery and the development of bacteria-specific imaging agents. d-Fluoroalanine and the deuterium-labeled analogue fludalanine (MK641) were originally explored as antibiotics by Merck but failed in clinical trials due to unaccepted toxicity. Herein, we synthesized a fluorine-18 labeled d-fluoroalanine, d-3-[ 18 F]fluoroalanine (d-[ 18 F]FAla), and its deuterated analogue, d-3-[ 18 F]fluoroalanine- d 3 (d-[ 18 F]FAla- d 3 ), and evaluated their capability to image bacterial infection. Both d-[ 18 F]FAla and d-[ 18 F]FAla- d 3 can accumulate up to 0.64-0.78% ID/cc in the infectious area at 15 min postinjection. Despite the reduction of in vivo defluorination not being observed for deuterated 18 F-labeled d-fluoroalanine, these radiolabeled d-alanine analogues were able to differentiate bacterial infection from sterile inflammation in a soft-tissue model of S. aureus infection.