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Establishing cell suitability for high-level production of licorice triterpenoids in yeast

Acta Pharmaceutica Sinica B 2024年第9期14卷 4134-4148页

作者： Wentao Sun Shengtong Wan Chuyan Liu Ruwen Wang Haocheng Zhang Lei Qin Runming Wang Bo Lv Chun Li Department of Chemical Engineering Tsinghua UniversityBeijing 100084China Key Laboratory of Medical Molecule Science and Pharmaceutics Engineering Ministry of Industry and Information TechnologyInstitute of Biochemical EngineeringSchool of Chemistry and Chemical EngineeringBeijing Institute of TechnologyBeijing 100081China The University of Chicago ChicagoIL 60637USA Key Lab for Industrial Biocatalysis Ministry of EducationTsinghua UniversityBeijing 100084China Center for Synthetic and Systems Biology Tsinghua UniversityBeijing 100084China Institute of Biopharmaceutical and Health Engineering Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055China

Yeast has been an indispensable host for synthesizing complex plant-derived naturalcompounds, yet the yields remained largely constrained. This limitation mainly arises from overlookingthe importance of cell and pathway suitability during the optimization of enzymes and pathways. Herein,beyond conventional enzyme engineering, we dissected metabolic suitability with a framework forsimultaneously augmenting cofactors and carbon flux to enhance the biosynthesis of heterogenoustriterpenoids. We further developed phospholipid microenvironment engineering strategies, dramaticallyimproving yeast’s suitability for the high performance of endoplasmic reticulum (ER)-localized, ratelimitingplant P450s. Combining metabolic and microenvironment suitability by manipulating only threegenes, NHMGR (NADH-dependent HMG-CoA reductase), SIP4 (a DNA-binding transcription factor)andGPP1 (Glycerol-1-phosphate phosphohydrolase 1), we enabled the high-level production of 4.92 g/L rarelicorice triterpenoids derived from consecutive oxidation of b-amyrin by two P450 enzymes afterfermentation optimization. This production holds substantial commercial value, highlighting the criticalrole of establishing cell suitability in enhancing triterpenoid biosynthesis and offering a versatileframework applicable to various plant natural product biosynthetic pathways.

关键词： Cell suitability Phospholipid microenvironment Cytochrome P450 Triterpenoids Saccharomyces cerevisiae

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Toward Transparent Deep Image Aesthetics Assessment with Tag-Based Content Descriptors

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IEEE Transactions on Image Processing 2025年 34卷 3070-3085页

作者： Hou, Jingwen Lin, Weisi Fang, Yuming Wu, Haoning Chen, Chaofeng Liao, Liang Liu, Weide Nanyang Technological University School of Computer Science and Engineering Jurong West Singapore630798 Singapore Nanyang Technological University S-lab School of Computer Science and Engineering Jurong West Singapore630798 Singapore Jiangxi University of Finance and Economics School of Information Technology Nanchang330013 China Nanyang Technological University S-Lab Nanchang330013 China Connexis Singapore138632 Singapore

Deep learning approaches for Image Aesthetics Assessment (IAA) have shown promising results in recent years, but the internal mechanisms of these models remain unclear. Previous studies have demonstrated that image aesthetics can be predicted using semantic features, such as pre-trained object classification features. However, these semantic features are learned implicitly, and therefore, previous works have not elucidated what the semantic features are representing. In this work, we aim to create a more transparent deep learning framework for IAA by introducing explainable semantic features. To achieve this, we propose Tag-based Content Descriptors (TCDs), where each value in a TCD describes the relevance of an image to a human-readable tag that refers to a specific type of image content. This allows us to build IAA models from explicit descriptions of image contents. We first propose the explicit matching process to produce TCDs that adopt predefined tags to describe image contents. We show that a simple MLP-based IAA model with TCDs only based on predefined tags can achieve an SRCC of 0.767, which is comparable to most state-of-the-art methods. However, predefined tags may not be sufficient to describe all possible image contents that the model may encounter. Therefore, we further propose the implicit matching process to describe image contents that cannot be described by predefined tags. By integrating components obtained from the implicit matching process into TCDs, the IAA model further achieves an SRCC of 0.817, which significantly outperforms existing IAA methods. Both the explicit matching process and the implicit matching process are realized by the proposed TCD generator. To evaluate the performance of the proposed TCD generator in matching images with predefined tags, we also labeled 5101 images with photography-related tags to form a validation set. And experimental results show that the proposed TCD generator can meaningfully assign photography-related

关键词： Photography

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Parameter-Sharing-Based Average-Consensus Time Synchronization in IoT Networks

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IEEE Internet of Things Journal 2023年第9期10卷 8215-8227页

作者： Shi, Fanrong Yang, Simon X. Mukherjee, Mithun Jiang, Hong Da Costa, Daniel Benevides Wong, Wing-Kwong Southwest University of Science and Technology Robot Technology Used for Special Environment Key Laboratory of Sichuan Province School of Information Engineering Mianyang621010 China School of Engineering University of Guelph Advanced Robotics and Intelligent Systems Laboratory GuelphONN1G 2W1 Canada Nanjing University of Information Science and Technology School of Artificial Intelligence School of Future Technology Nanjing210044 China Technology Innovation Institute Abu Dhabi United Arab Emirates National Yunlin University of Science and Technology Future Technology Research Center Douliu64002 Taiwan

Average-consensus protocol is one of the ways to develop distributed time-synchronization algorithms in Internet of Things (IoT) networks. However, the large number of iteration leads to a common time notion issue in nodes. This poses a critical challenge in the convergence of the time-synchronization algorithm and resulting asymptotic convergence in the average consensus protocol. In this article, a parameter-sharing-based average-consensus time-synchronization (PACTS) algorithm is proposed. For fast convergence, the proposed PACTS quickly forwards the time information to multihop nodes and employs multihop average-consensus instead of single-hop average consensus. Specifically, a node asynchronously and periodically broadcasts the relative clock offset estimation of neighbors with its local time information. Meanwhile, the relative clock offset estimation of the multihop node is calculated and used to estimate the average value. Consequently, an average consensus among local multihop nodes is obtained. As a result, the iteration number and convergence time are significantly reduced over the network. Finally, the experimental results indicate that the proposed PACTS algorithm has low complexity, high accuracy, and quick convergence. © 2023 IEEE.

关键词： Topology

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Intelligent Additive Manufacturing Architecture for Enhancing Uniformity of Surface Roughness and Mechanical Properties of Laser Powder Bed Fusion Components

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IEEE Transactions on Automation science and engineering 2023年第4期20卷 2527-2538页

作者： Tran, Hong-Chuong Lo, Yu-Lung Yang, Haw-Ching Hsiao, Hung-Chang Cheng, Fan-Tien Kuo, Tsung-Han Southern Taiwan University of Science and Technology Department of Mechanical Engineering Tainan71005 Taiwan National Cheng Kung University Department of Mechanical Engineering Tainan70101 Taiwan National Kaohsiung First University of Science and Technology Department of Electrical Engineering Kaohsiung82445 Taiwan National Cheng Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan Institute of Manufacturing Information and Systems National Cheng Kung University Tainan70101 Taiwan

In the Laser Powder Bed Fusion (L-PBF) process, 3D components with complex geometries are fabricated in a layer-by-layer fashion by using a controlled laser beam to selectively melt particular regions of the metal powder bed. However, due to the stochastic nature of the L-PBF process, the top surface roughness of each solidified layer tends to be different even when the optimal processing conditions for the different positions on the build plate are employed. As a result, the mechanical properties of the built components frequently vary from one component to the next. Accordingly, this study proposes an Intelligent Additive Manufacturing Architecture (IAMA) for controlling the surface roughness of each build layer through an appropriate adjustment of the laser re-melting parameters. The IAMA architecture comprises five modules, namely In-Situ Metrology (ISM), Ex-Situ Metrology (ESM), Automatic Virtual Metrology (AVM), Additive Manufacturing Simulation (AMS) and Intelligent Compensator (IC). The feasibility of the proposed architecture is demonstrated by comparing the top surface roughness of cubic and mechanical strengths of tensile test samples built using the proposed method with those built using a traditional L-PBF approach without surface roughness control. It is found that the samples fabricated using the IAMA approach have an average top surface roughness of 1.6μm and a standard deviation is 0.7μm. By contrast, the samples produced using the traditional L-PBF approach have an average surface roughness of 13.45μm and a standard deviation of 2.5μm. In addition, the specimens produced with the assistance of IAMA architecture have an average tensile strength of 1013 MPa with a standard deviation of 69.5 MPa, while those printed without surface roughness control have an average tensile strength of 903 MPa with a standard deviation of 101.4 MPa Note to Practitioners - As L-PBF produce part in a layer-by-layer manner, therefore, the roughness on the top surface of p

关键词： Surface roughness

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Improving OTP Authentication with PQC Algorithms 14

Improving OTP Authentication with PQC Algorithms

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14th Global information Infrastructure and Networking Symposium, GIIS 2024

作者： Khorkheli, L. Bourne, D. Chakravarty, V. Abraham, S. Satrya, Gandeva Bayu Mnaouer, Adel Ben School of Engineering Applied Science and Technology Canadian University Dubai Dubai United Arab Emirates College of Computer and Information Systems Prince Sultan University Riyadh Saudi Arabia

ISBN: (纸本)9798350384789

Over the past few years, two-factor authentication (2FA) has emerged as a reliable defense against various cyber-attacks, such as Man in the Middle (MITM) and cookie theft. By combining passphrase and one-time password (OTP) authentication, it has successfully preserved the privacy of users across multiple applications. However, recent advancements in quantum computing have posed a threat to the security of credentials. Quantum computers now possess the capability to penetrate standard encryption algorithms. To address this concern, this research aims to implement new post-quantum algorithms for encryption and secure transference of authentication credentials. Specifically, this research will explore NTRU, CRYSTALS-Dilithium, SPHINCS+, and FALCON while evaluating their performance benchmarks against each other within an OTP authentication system. The goal is to determine which algorithm best suits the scenario with enhanced resistance against quantum-related threats. © 2024 IEEE.

关键词： Benchmarking

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Incorporating metapath interaction on heterogeneous information network for social recommendation

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Frontiers of Computer science 2024年第1期18卷 33-48页

作者： Yanbin JIANG Huifang MA Xiaohui ZHANG Zhixin LI Liang CHANG College of Computer Science and Engineering Northwest Normal UniversityLanzhou 730070China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal UniversityGuilin 541001China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China

Heterogeneous information network(HIN)has recently been widely adopted to describe complex graph structure in recommendation systems,proving its effectiveness in modeling complex graph *** existing HIN-based recommendation studies have achieved great success by performing message propagation between connected nodes on the defined metapaths,they have the following major *** works mainly convert heterogeneous graphs into homogeneous graphs via defining metapaths,which are not expressive enough to capture more complicated dependency relationships involved on the ***,the heterogeneous information is more likely to be provided by item attributes while social relations between users are not adequately *** tackle these limitations,we propose a novel social recommendation model MPISR,which models MetaPath Interaction for Social Recommendation on heterogeneous information ***,our model first learns the initial node representation through a pretraining module,and then identifies potential social friends and item relations based on their similarity to construct a unified *** then develop the two-way encoder module with similarity encoder and instance encoder to capture the similarity collaborative signals and relational dependency on different *** experiments on five real datasets demonstrate the effectiveness of our method.

关键词： heterogeneous information network social reco-mmender system metapath interaction attention mechanism

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Positive and Unlabeled Learning with Controlled Probability Boundary Fence 41

Positive and Unlabeled Learning with Controlled Probability ...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Changchun Dai, Yuanchao Feng, Lei Li, Ximing Wang, Bing Ouyang, Jihong College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

Positive and Unlabeled (PU) learning refers to a special case of binary classification, and technically, it aims to induce a binary classifier from a few labeled positive training instances and loads of unlabeled instances. In this paper, we derive a theorem indicating that the probability boundary of the asymmetric disambiguation-free expected risk of PU learning is controlled by its asymmetric penalty, and we further empirically evaluated this theorem. Inspired by the theorem and its empirical evaluations, we propose an easy-to-implement two-stage PU learning method, namely Positive and Unlabeled Learning with Controlled Probability Boundary Fence (PUL-CPBF). In the first stage, we train a set of weak binary classifiers concerning different probability boundaries by minimizing the asymmetric disambiguation-free empirical risks with specific asymmetric penalty values. We can interpret these induced weak binary classifiers as a probability boundary fence. For each unlabeled instance, we can use the predictions to locate its class posterior probability and generate a stochastic label. In the second stage, we train a strong binary classifier over labeled positive training instances and all unlabeled instances with stochastic labels in a self-training manner. Extensive empirical results demonstrate that PUL-CPBF can achieve competitive performance compared with the existing PU learning baselines. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Experimental Testing of the Phased Array Antenna Beamsteering for the V2X Applications 25

Experimental Testing of the Phased Array Antenna Beamsteerin...

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25th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2024

作者： Saifullin, Talgat E. Grakhova, Elizaveta P. Ishmiyarov, Arsen A. Katerinkina, Ekaterina N. Letavin, Denis A. Kutluyarov, Ruslan V. Research Lab. "Sensor Systems Based on the Integrated Photonics Devices" Ufa University of Science and Technology Ufa Russia Telecommunication systems Dept. Ufa University of Science and Technology Ufa Russia Institute of Radio Electronics Information Technologies dept Ural Federal University Yekaterinburg Russia

ISBN: (纸本)9798350389234

The paper showcases experimental testing of a phased array antenna for connecting and tracking vehicles in Vehicle-to-Everything technology applications. The study involved measuring the performance of a microstrip half-octagon shaped emitter and a 14-element linear antenna array with a main lobe width of no more than 8 degrees. Both the emitter and antenna array were designed and manufactured using WL-CT350 laminate material. The antenna system operates within the frequency range of 5614 to 5943 MHz. To control the main lobe position of the antenna array, a beamforming circuit was developed, which consisted of a splitter and a phase control unit utilizing planar phase shifters. These phase shifters were controlled by Arduino Mega 2560 microcontrollers, which implemented machine-learning algorithms to determine the optimal service sector based on the location of the vehicle. Experimental tests were conducted in an anechoic chamber to demonstrate the antenna's ability to steer a focused radio beam across ten different sectors within a scanning range of 90 degrees. © 2024 IEEE.

关键词： Antenna lobes

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Robust-MSA: Understanding the Impact of Modality Noise on Multimodal Sentiment Analysis 37

Robust-MSA: Understanding the Impact of Modality Noise on Mu...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Mao, Huisheng Zhang, Baozheng Xu, Hua Yuan, Ziqi Liu, Yihe State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing100084 China Beijing100084 China School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050018 China

ISBN: (纸本)9781577358800

Improving model robustness against potential modality noise, as an essential step for adapting multimodal models to real-world applications, has received increasing attention among researchers. For Multimodal Sentiment Analysis (MSA), there is also a debate on whether multimodal models are more effective against noisy features than unimodal ones. Stressing on intuitive illustration and in-depth analysis of these concerns, we present Robust-MSA, an interactive platform that visualizes the impact of modality noise as well as simple defence methods to help researchers know better about how their models perform with imperfect real-world data. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Sentiment analysis

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Early Prediction and Diagnosis Cardiovascular Disease Using Deep Learning Models

Early Prediction and Diagnosis Cardiovascular Disease Using ...

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2024 IEEE International Conference on Emerging Technologies in Computer science for Interdisciplinary Applications, ICETCS 2024

作者： Rani, Rudra Yamini Prasad, Mudarakola Lakshmi Reddy, Induru Sunnihith Tayubi, Iftikhar Aslam Sultan, Ghazala Reddy, Pundru Chandra Shaker VNR Vignana Jyothi Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Computer Science and Engineering Institute of Aeronautical Engineering Telangana Dundigal India Computer Science and Business Systems RMK Engineering College Tamilnadu Tiruvallur India King Abdulaziz University Faculty of Computing and Information Technology Rabigh Jeddah Saudi Arabia Aligarh Muslim University Department of Computer Science Uttar Pradesh Aligarh India SR University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India

ISBN: (纸本)9798350372502

Cardiovascular disease (CVD) risk assessment and prognosis in otherwise healthy people is an important part of disease management. Early detection and diagnosis of CVD, supported by the extensive health data on the condition already stored in hospital systems, has the ability to greatly improve disease outcomes. Clinical practice for the management of Cardiovascular Diseases (CVDs) stands to benefit greatly from the use of machine learning approaches. Patients and the healthcare system can save money thanks to these methods, which can be used to create evidence-based clinical guidelines and management algorithms. This will cut down on expensive and time-consuming clinical and laboratory examinations. This study suggests creating new deep learning algorithms that can automatically identify important variables and detect early-stage heart disease;these algorithms should be resilient, effective, efficient, and unique in order to improve early anticipation and interference for CVDs. An F1-score of around 89% is produced by the suggested Catboost model. Hence, it outperformed numerous other state-of-the-art methods by maximizing classification efficiency with better correctness and precision. © 2024 IEEE.

关键词： Hospitals

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