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Article ; Online: Next-generation synthetic biology approaches for the accelerated discovery of microbial natural products

Lei Li

Engineering Microbiology, Vol 3, Iss 1, Pp 100060- (2023)

2023

Abstract: Microbial natural products (NPs) and their derivates have been widely used in health care and agriculture during the past few decades. Although large-scale bacterial or fungal (meta)genomic mining has revealed the tremendous biosynthetic potentials to ... ...

Abstract	Microbial natural products (NPs) and their derivates have been widely used in health care and agriculture during the past few decades. Although large-scale bacterial or fungal (meta)genomic mining has revealed the tremendous biosynthetic potentials to produce novel small molecules, there remains a lack of universal approaches to link NP biosynthetic gene clusters (BGCs) to their associated products at a large scale and speed. In the last ten years, a series of emerging technologies have been established alongside the developments in synthetic biology to engineer cryptic metabolite BGCs and edit host genomes. Diverse computational tools, such as antiSMASH and PRISM, have also been simultaneously developed to rapidly identify BGCs and predict the chemical structures of their products. This review discusses the recent developments and trends pertaining to the accelerated discovery of microbial NPs driven by a wide variety of next-generation synthetic biology approaches, with an emphasis on the in situ activation of silent BGCs at scale, the direct cloning or refactoring of BGCs of interest for heterologous expression, and the synthetic-bioinformatic natural products (syn-BNP) approach for the guided rapid access of bioactive non-ribosomal peptides.
Keywords	Natural products ; Synthetic biology ; Silent BGCs ; Large-scale discovery ; Peptide synthesis ; Biotechnology ; TP248.13-248.65 ; Microbiology ; QR1-502
Subject code	540
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Multi-Bit Biomemristic Behavior for Neutral Polysaccharide Dextran Blended with Chitosan

Lei Li

Nanomaterials, Vol 12, Iss 1072, p

2022 Volume 1072

Abstract: Natural biomaterials applicable for biomemristors have drawn prominent attention and are of benefit to sustainability, biodegradability, biocompatibility, and metabolism. In this work, multi-bit biomemristors based on the neutral polysaccharide dextran ... ...

Abstract	Natural biomaterials applicable for biomemristors have drawn prominent attention and are of benefit to sustainability, biodegradability, biocompatibility, and metabolism. In this work, multi-bit biomemristors based on the neutral polysaccharide dextran were built using the spin-casting method, which was also employed to explore the effect of dextran on the ternary biomemristic behaviors of dextran–chitosan nanocomposites. The doping of 50 wt% dextran onto the bio-nanocomposite optimized the ratio of biomemristance in high-, intermediate-, and low-resistance states (10 5 :10 4 :1). The interaction between dextran and chitosan (hydrogen-bond network) was verified by Fourier transform infrared (FTIR) and Raman spectroscopy analysis; through this interaction, protons derived from the self-dissociation of water may migrate under the electric field, and so proton conduction may be the reason for the ternary biomemristic behaviors. Observations from X-ray diffraction (XRD), thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC) analysis displayed that the 50 wt% dextran/50 wt% chitosan nanocomposite had the greatest amorphous ratio as well as the highest decomposition and peak transition temperatures in comparison with the other three dextran–chitosan nanocomposites. This work lays the foundation for neutral biomaterials applied to green ultra-high-density data-storage systems.
Keywords	neutral polysaccharide ; biomemristance ; dextran ; proton conduction ; Chemistry ; QD1-999
Subject code	540
Language	English
Publishing date	2022-03-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Improved Feature Pyramid Convolutional Neural Network for Effective Recognition of Music Scores

Lei Li

Computational Intelligence and Neuroscience, Vol

2022 Volume 2022

Abstract: Music written by composers and performed by multidimensional instruments is an art form that reflects real-life emotions. Historically, people disseminated music primarily through sheet music recording and oral transmission. Among them, recording music ... ...

Abstract	Music written by composers and performed by multidimensional instruments is an art form that reflects real-life emotions. Historically, people disseminated music primarily through sheet music recording and oral transmission. Among them, recording music in sheet music form was a great musical invention. It became the carrier of music communication and inheritance, as well as a record of humanity's magnificent music culture. The advent of digital technology solves the problem of difficult musical score storage and distribution. However, there are many drawbacks to using data in image format, and extracting music score information in editable form from image data is currently a challenge. An improved convolutional neural network for musical score recognition is proposed in this paper. Because the traditional convolutional neural network SEGNET misclassifies some pixels, this paper employs the feature pyramid structure. Use additional branch paths to fuse shallow image details, shallow texture features that are beneficial to small objects, and high-level features of global information, enrich the multi-scale semantic information of the model, and alleviate the problem of the lack of multiscale semantic information in the model. Poor recognition performance is caused by semantic information. By comparing the recognition effects of other models, the experimental results show that the proposed musical score recognition model has a higher recognition accuracy and a stronger generalization performance. The improved generalization performance allows the musical score recognition method to be applied to more types of musical score recognition scenarios, and such a recognition model has more practical value.
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7 ; Neurosciences. Biological psychiatry. Neuropsychiatry ; RC321-571
Subject code	780
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Hindawi Limited
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Learning Recommendation Algorithm Based on Improved BP Neural Network in Music Marketing Strategy

Lei Li

Computational Intelligence and Neuroscience, Vol

2021 Volume 2021

Abstract: The growth and popularity of streaming music have changed the way people consume music, and users can listen to online music anytime and anywhere. By integrating various recommendation algorithms/strategies (user profiling, collaborative filtering, ... ...

Abstract	The growth and popularity of streaming music have changed the way people consume music, and users can listen to online music anytime and anywhere. By integrating various recommendation algorithms/strategies (user profiling, collaborative filtering, content filtering, etc.), we capture users’ interests and preferences and recommend the content of interest to them. To address the sparsity of behavioral data in digital music marketing, which leads to inadequate mining of user music preference features, a metric ranking learning recommendation algorithm with fused content representation is proposed. Relative partial order relations are constructed using observed and unobserved behavioral data to enable the model to be fully trained, while audio feature extraction submodels related to the recommendation task are constructed to further alleviate the data sparsity problem, and finally, the preference relationships between users and songs are mined through metric learning. Convolutional neural networks are used to extract the high-level semantic features of songs, and then the high-level semantic features of songs extracted from the previous layer are reformed into a session time sequence list according to the time sequence of user listening in order to build a bidirectional recurrent neural network model based on the attention mechanism so that it can reduce the influence of noisy data and learn the strong dependencies between songs.
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7 ; Neurosciences. Biological psychiatry. Neuropsychiatry ; RC321-571
Subject code	780
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Hindawi Limited
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Graphene Oxide

Lei Li

Nanomaterials, Vol 10, Iss 1448, p

Graphene Quantum Dot Nanocomposite for Better Memristic Switching Behaviors

2020 Volume 1448

Abstract: Tristable memristic switching provides the capability for multi-bit data storage. In this study, all-inorganic multi-bit memory devices were successfully manufactured by the attachment of graphene quantum dots (GQDs) onto graphene oxide (GO) through a ... ...

Abstract	Tristable memristic switching provides the capability for multi-bit data storage. In this study, all-inorganic multi-bit memory devices were successfully manufactured by the attachment of graphene quantum dots (GQDs) onto graphene oxide (GO) through a solution-processable method. By means of doping GQDs as charge-trapping centers, the device indium-tin oxide (ITO)/GO:0.5 wt%GQDs/Ni revealed controllable memristic switching behaviors that were tunable from binary to ternary, and remarkably enhanced in contrast with ITO/GO/Ni. It was found that the device has an excellent performance in memristic switching parameters, with a SET1, SET2 and RESET voltage of −0.9 V, −1.7 V and 5.15 V, as well as a high ON2/ON1/OFF current ratio (10 3 :10 2 :1), and a long retention time (10 4 s) together with 100 successive cycles. The conduction mechanism of the binary and ternary GO-based memory cells was discussed in terms of experimental data employing a charge trapping-detrapping mechanism. The reinforcement effect of GQDs on the memristic switching of GO through cycle-to-cycle operation has been extensively investigated, offering great potential application for multi-bit data storage in ultrahigh-density, nonvolatile memory.
Keywords	tristable memristic switching ; all-inorganic multi-bit memory ; charge-trap memristor ; GO:GQDs nanocomposite ; Chemistry ; QD1-999
Subject code	600
Language	English
Publishing date	2020-07-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Multi-Layered Projected Entangled Pair States for Image Classification

Lei Li / Hong Lai

Sustainability, Vol 15, Iss 5120, p

2023 Volume 5120

Abstract: Tensor networks have been recognized as a powerful numerical tool; they are applied in various fields, including physics, computer science, and more. The idea of a tensor network originates from quantum physics as an efficient representation of quantum ... ...

Abstract	Tensor networks have been recognized as a powerful numerical tool; they are applied in various fields, including physics, computer science, and more. The idea of a tensor network originates from quantum physics as an efficient representation of quantum many-body states and their operations. Matrix product states (MPS) form one of the simplest tensor networks and have been applied to machine learning for image classification. However, MPS has certain limitations when processing two-dimensional images, meaning that it is preferable for an projected entangled pair states (PEPS) tensor network with a similar structure to the image to be introduced into machine learning. PEPS tensor networks are significantly superior to other tensor networks on the image classification task. Based on a PEPS tensor network, this paper constructs a multi-layered PEPS (MLPEPS) tensor network model for image classification. PEPS is used to extract features layer by layer from the image mapped to the Hilbert space, which fully utilizes the correlation between pixels while retaining the global structural information of the image. When performing classification tasks on the Fashion-MNIST dataset, MLPEPS achieves a classification accuracy of 90.44%, exceeding tensor network models such as the original PEPS. On the COVID-19 radiography dataset, MLPEPS has a test set accuracy of 91.63%, which is very close to the results of GoogLeNet. Under the same experimental conditions, the learning ability of MLPEPS is already close to that of existing neural networks while having fewer parameters. MLPEPS can be used to build different network models by modifying the structure, and as such it has great potential in machine learning.
Keywords	tensor networks ; image classification ; multi-layered projected entangled pair states ; Environmental effects of industries and plants ; TD194-195 ; Renewable energy sources ; TJ807-830 ; Environmental sciences ; GE1-350
Subject code	006
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Engineering Polymer-Based Porous Membrane for Sustainable Lithium-Ion Battery Separators

Lei Li / Yutian Duan

Polymers, Vol 15, Iss 3690, p

2023 Volume 3690

Abstract: Due to the growing demand for eco-friendly products, lithium-ion batteries (LIBs) have gained widespread attention as an energy storage solution. With the global demand for clean and sustainable energy, the social, economic, and environmental ... ...

Abstract	Due to the growing demand for eco-friendly products, lithium-ion batteries (LIBs) have gained widespread attention as an energy storage solution. With the global demand for clean and sustainable energy, the social, economic, and environmental significance of LIBs is becoming more widely recognized. LIBs are composed of cathode and anode electrodes, electrolytes, and separators. Notably, the separator, a pivotal and indispensable component in LIBs that primarily consists of a porous membrane material, warrants significant research attention. Researchers have thus endeavored to develop innovative systems that enhance separator performance, fortify security measures, and address prevailing limitations. Herein, this review aims to furnish researchers with comprehensive content on battery separator membranes, encompassing performance requirements, functional parameters, manufacturing protocols, scientific progress, and overall performance evaluations. Specifically, it investigates the latest breakthroughs in porous membrane design, fabrication, modification, and optimization that employ various commonly used or emerging polymeric materials. Furthermore, the article offers insights into the future trajectory of polymer-based composite membranes for LIB applications and prospective challenges awaiting scientific exploration. The robust and durable membranes developed have shown superior efficacy across diverse applications. Consequently, these proposed concepts pave the way for a circular economy that curtails waste materials, lowers process costs, and mitigates the environmental footprint.
Keywords	lithium-ion battery separator ; porous membrane ; polymer ; polyethylene ; polypropylene ; poly(vinylidene fluoride) ; Organic chemistry ; QD241-441
Subject code	620
Language	English
Publishing date	2023-09-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Role of DDX1 in the oxidative response of ataxia telangiectasia patient-derived fibroblasts

Mansi Garg / Lei Li / Roseline Godbout

Redox Biology, Vol 69, Iss , Pp 102988- (2024)

1481

Abstract: Ataxia Telangiectasia (A-T) is an inherited autosomal recessive disorder characterized by cerebellar neurodegeneration, radiosensitivity, immunodeficiency and a high incidence of lymphomas. A-T is caused by mutations in the ATM gene. While loss of ATM ... ...

Abstract	Ataxia Telangiectasia (A-T) is an inherited autosomal recessive disorder characterized by cerebellar neurodegeneration, radiosensitivity, immunodeficiency and a high incidence of lymphomas. A-T is caused by mutations in the ATM gene. While loss of ATM function in DNA repair explains some aspects of A-T pathophysiology such as radiosensitivity and cancer predisposition, other A-T features such as neurodegeneration imply additional roles for ATM outside the nucleus. Emerging evidence suggests that ATM participates in cellular response to oxidative stress, failure of which contributes to the neurodegeneration associated with A-T. Here, we use fibroblasts derived from A-T patients to investigate whether DEAD Box 1 (DDX1), an RNA binding/unwinding protein that functions downstream of ATM in DNA double strand break repair, also plays a role in ATM-dependent cellular response to oxidative stress. Focusing on DDX1 target RNAs that are associated with neurological disorders and oxidative stress response, we show that ATM is required for increased binding of DDX1 to its target RNAs in the presence of arsenite-induced oxidative stress. Our results indicate that DDX1 functions downstream of ATM by protecting specific mRNAs in the cytoplasm of arsenite-treated cells. In keeping with a role for ATM and DDX1 in oxidative stress, levels of reactive oxygen species (ROS) are increased in ATM-deficient as well as DDX1-depleted cells. We propose that reduced levels of cytoplasmic DDX1 RNA targets sensitizes ATM-deficient cells to oxidative stress resulting in increased cell death. This sensitization would be especially detrimental to long-lived highly metabolically active cells such as neurons providing a possible explanation for the neurodegenerative defects associated with A-T.
Keywords	Ataxia Telangiectasia ; Oxidative stress ; Neurodegeneration ; DEAD box protein 1 ; ATM ; RNA protection ; Medicine (General) ; R5-920 ; Biology (General) ; QH301-705.5
Subject code	500
Language	English
Publishing date	2024-02-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A study on the impact of tourism destination image and local attachment on the revisit intention

Jiahua Wei / Lewei Zhou / Lei Li

PLoS ONE, Vol 19, Iss 1, p e

The moderating effect of perceived risk.

2024 Volume 0296524

Abstract: The revisit intention of tourists is an important guarantee for the sustainable and healthy development of tourism destination, and has also received attention from the current academic community. However, there is still insufficient research on the ... ...

Abstract	The revisit intention of tourists is an important guarantee for the sustainable and healthy development of tourism destination, and has also received attention from the current academic community. However, there is still insufficient research on the antecedents of revisit intention from the perspectives of tourism destination, image and nostalgia emotion. This study takes China's ecological tourism scenic area (Guilin Lijiang Scenic Area) as a case study, and uses questionnaire survey method to obtain research data for empirical research. The results of this study confirm that tourism destination image has a positive impact on nostalgia emotions and local attachment, nostalgia emotion has a positive impact on local attachment, and local attachment has a positive impact on revisit intention. Perceived risk plays a negative moderating effect between local attachment and revisit intention. In addition, this study also examined the mediating effect of nostalgia emotion and local attachment. This study is beneficial for enriching the theory of the influence mechanism of revisit intention from the perspective of consumer psychology. It is an interdisciplinary research result of management and psychology, providing theoretical reference for improving revisit intention in tourism destinations and promoting their healthy development.
Keywords	Medicine ; R ; Science ; Q
Subject code	910
Language	English
Publishing date	2024-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Study on Safety Management Assessment of Coal Mine Roofs Based on the DEMATEL-ANP Method

Lei Li / Youpeng Ouyang

Frontiers in Earth Science, Vol

2022 Volume 10

Abstract: Coal mine roof accidents are one of the main single risks faced by coal miners. According to the statistical data of coal mine accidents in China, there were 40 roof accidents and 55 deaths in 2020 alone, accounting for 32.8 and 24.4% of the total, ... ...

Abstract	Coal mine roof accidents are one of the main single risks faced by coal miners. According to the statistical data of coal mine accidents in China, there were 40 roof accidents and 55 deaths in 2020 alone, accounting for 32.8 and 24.4% of the total, respectively. Therefore, we can see its danger. To realize the comprehensive scientific assessment of coal mine roof accidents, first, through the collation and analysis of relevant literature reviews and accident investigation reports, combined with the expert investigation method, an assessment index system of coal mine roof accidents is constructed. Then, based on the analysis of the characteristics of the influencing factors of coal mine roof accidents, the assessment model of coal mine roof accidents is established by using the DEMATEL-ANP method. Finally, the established assessment model is applied to a coal mine to verify the rationality of the model.
Keywords	roof accident ; index system ; DEMATEL method ; ANP method ; assessment model ; Science ; Q
Subject code	380
Language	English
Publishing date	2022-05-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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