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Extracellular respiration is a latent energy metabolism in Escherichia coli

Cell 2025年第11期188卷 2907-2924.e23页

作者： Kundu, Biki Bapi Krishnan, Jayanth Szubin, Richard Patel, Arjun Palsson, Bernhard O. Zielinski, Daniel C. Ajo-Franklin, Caroline M. PhD Program in Systems Synthetic and Physical Biology Rice University Houston 77005 TX United States Department of Bioengineering University of California San Diego La Jolla 92093 CA United States Novo Nordisk Foundation Center for Biosustainability Technical University of Denmark Kemitorvet Building 220 Kgs. Lyngby 2800 Denmark Departments of Biosciences Bioengineering Chemical and Biomolecular Engineering and The Rice Synthetic Biology Institute Rice University Houston 77005 TX United States

Diverse microbes utilize redox shuttles to exchange electrons with their environment through mediated extracellular electron transfer (EET), supporting anaerobic survival. Although mediated EET has been leveraged for bioelectrocatalysis for decades, fundamental questions remain about how these redox shuttles are reduced within cells and their role in cellular bioenergetics. Here, we integrate genome editing, electrochemistry, and systems biology to investigate the mechanism and bioenergetics of mediated EET in Escherichia coli, elusive for over two decades. In the absence of alternative electron sinks, the redox cycling of 2-hydroxy-1,4-naphthoquinone (HNQ) via the cytoplasmic nitroreductases NfsB and NfsA enables E. coli respiration on an extracellular electrode. E. coli also exhibits rapid genetic adaptation in the outer membrane porin OmpC, enhancing HNQ-mediated EET levels coupled to growth. This work demonstrates that E. coli can grow independently of classic electron transport chains and fermentation, unveiling a potentially widespread new type of anaerobic energy metabolism. © 2025 Elsevier Inc.

关键词： adaptive laboratory evolution anaerobic metabolism electron shuttles extracellular electron transfer flux balance analysis iModulon analysis nitroreductases redox homeostasis

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Emotional Requirements for Well-being Applications : The Customer Journey

Emotional Requirements for Well-being Applications : The Cus...

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Requirements engineering for Well-Being, Aging, and Health (REWBAH), International Workshop on

作者： Meira Levy School of Industrial Engineering and Management Shenkar College of Engineering Design and Art Ramat-Gan Israel Department of Information Systems University of Haifa Haifa Israel

ISBN: (数字)9781728183541

ISBN: (纸本)9781728183558

This study discusses the importance of well-being (WB) and the role of applications in promoting WB. While there are many applications that aim to promote WB in various aspects of daily life (e.g. healthy eating, mindfulness), most of them fail to engage users over time. This paper posits that understanding the users' emotional needs and creating attachment between the users and the applications, are critical factors for determining user engagement. The paper shows how applying the design thinking methodology, and in particular its customer journey map (CJM) tool, to present offline and online journeys, can help scholars understand how a specific application can be used in everyday life, enabling users to become attached and engaged. The paper presents how this approach helped students cope with a challenge of creating applications for reducing stress among mentally challenged persons during a multidisciplinary workshop attended by engineering and design students.

关键词： Conferences Games Tools Stress Stakeholders Art Organizations

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MULTI-TARGET EMOTIONAL VOICE CONVERSION WITH NEURAL VOCODERS

arXiv

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arXiv 2020年

作者： Liu, Songxiang Cao, Yuewen Meng, Helen Human-Computer Communications Laboratory Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Shatin N.T. Hong Kong

Emotional voice conversion (EVC) is one way to generate expressive synthetic speech. Previous approaches mainly focused on modeling one-to-one mapping, i.e., conversion from one emotional state to another emotional state, with Mel-cepstral vocoders. In this paper, we investigate building a multi-target EVC (MTEVC) architecture, which combines a deep bidirectional long-short term memory (DBLSTM)based conversion model and a neural vocoder. Phonetic posteriorgrams (PPGs) containing rich linguistic information are incorporated into the conversion model as auxiliary input features, which boost the conversion performance. To leverage the advantages of the newly emerged neural vocoders, we investigate the conditional WaveNet and flow-based WaveNet (FloWaveNet) as speech generators. The vocoders take in additional speaker information and emotion information as auxiliary features and are trained with a multi-speaker and multi-emotion speech corpus. Objective metrics and subjective evaluation of the experimental results verify the efficacy of the proposed MTEVC architecture for EVC. Copyright © 2020, The Authors. All rights reserved.

关键词： Vocoders

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EMOTIONAL VOICE CONVERSION WITH CYCLE-CONSISTENT ADVERSARIAL NETWORK

arXiv

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arXiv 2020年

Emotional Voice Conversion, or emotional VC, is a technique of converting speech from one emotion state into another one, keeping the basic linguistic information and speaker identity. Previous approaches for emotional VC need parallel data and use dynamic time warping (DTW) method to temporally align the source-target speech parameters. These approaches often define a minimum generation loss as the objective function, such as L1 or L2 loss, to learn model parameters. Recently, cycle-consistent generative adversarial networks (CycleGAN) have been used successfully for non-parallel VC. This paper investigates the efficacy of using CycleGAN for emotional VC tasks. Rather than attempting to learn a mapping between parallel training data using a frame-to-frame minimum generation loss, the CycleGAN uses two discriminators and one classifier to guide the learning process, where the discriminators aim to differentiate between the natural and converted speech and the classifier aims to classify the underlying emotion from the natural and converted speech. The training process of the CycleGAN models randomly pairs source-target speech parameters, without any temporal alignment operation. The objective and subjective evaluation results confirm the effectiveness of using CycleGAN models for emotional VC. The non-parallel training for a CycleGAN indicates its potential for non-parallel emotional VC. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A novel hybrid optimization approach for cost-efficient pump scheduling in water supply systems

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Omega 2026年 138卷

作者： Marlene Brás Ana Moura António Andrade-Campos Department of Economics Management Industrial Engineering and Tourism Research Unit on Governance Competitiveness and Public Policies (GOVCOPP) University of Aveiro Aveiro Portugal Department of Mechanical Engineering Centre for Mechanical Technology & Automation (DEM-TEMA) University of Aveiro Aveiro Portugal Intelligent Systems Associate Laboratory (LASI) Guimarães Portugal

Optimizing pump scheduling in water supply systems (WSS) is crucial for reducing energy costs and improving operational efficiency. This paper presents a detailed analysis of the duty-cycles formulation, a mathematical model of the Pump Scheduling Problem (PSP) that enables a flexible pump operation over the total time horizon. Combined with the Sequential Least Squares Quadratic programming (SLSQP) gradient-based method, this approach has shown superior computational efficiency and cost savings in previous studies. However, problems, such as a multiplicity of optimal solutions, local minima, and size scalability, were encountered. In addition, this paper introduces a new hybrid method, the Smart Dynamic Local Search (Smart-DLS), designed to overcome the identified challenges. This new approach integrates a deterministic local search with an intelligent shaking process to explore the solution space and avoid local optima efficiently. The framework’s performance is demonstrated through three case studies, including a real-world WSS, achieving significant cost reductions and showing strong generalizability across diverse scenarios. For the AnyTown network, it reaches more than 5%, and for the real network, 3% of cost reduction.

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Enhanced Graphene-Based Metasurface Biosensor for Brain Tumor Detection and Behavior Prediction Using Random Forest Regression

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Journal of The Electrochemical Society 2025年第6期172卷 067517-067517页

作者： Sarankumar R Hashim Elshafie Azath Mubarakali Pankaj Pathak Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore 641032 Tamilnadu India Department of Computer Engineering College of Computer Science King Khalid University Main Campus Al Farah Abha 61421 Saudi Arabia Department of Informatics and Computer Systems College of Computer Science King Khalid University Abha Saudi Arabia Symbiosis Institute of Digital and Telecom Management (SIDTM) Symbiosis International (Deemed University) Pune India

This manuscript introduces a terahertz biosensor that integrates graphene-enhanced metasurfaces with a dual-resonator configuration for the accurate identification of brain cancers. The sensor comprises concentric circular resonators encircled by rectangular components on a silicon dioxide substrate, using graphene's adjustable chemical potential to improve its efficacy. A thorough analysis of the sensor's geometric variables is conducted to optimize its design. The results show an optimum sensitivity of 769 GHz RIU⁻¹ and a figure of merit of 28.49 RIU⁻¹, demonstrating its potential for accurate biomarker detection. Additionally, random forest regression is used to model and predict the sensor's response under different biochemical conditions, integrating machine learning with the metasurface platform to improve detection accuracy and provide a robust framework for real-time diagnostics.

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A Unified Analysis on the Subgradient Upper Bounds for the Subgradient Methods Minimizing Composite Nonconvex, Nonsmooth and Non-Lipschitz Functions

arXiv

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arXiv 2023年

作者： Zhu, Daoli Zhao, Lei Zhang, Shuzhong Antai College of Economics and Management Shanghai Jiao Tong University Shanghai200030 China School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen518172 China Institute of Translational Medicine National Center for Translational Medicine Shanghai Jiao Tong University Shanghai200030 China Xiangfu Laboratory Jiashan314000 China Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States

This paper presents a unified analysis for the proximal subgradient method (Prox-SubGrad) type approach to minimize an overall objective of f(x) + r(x), subject to convex constraints, where both f and r are weakly convex, nonsmooth, and non-Lipschitz. Leveraging on the properties of the Moreau envelope of weakly convex functions, we are able to relate error-bound conditions, the growth conditions of the subgradients of the objective, and the behavior of the proximal subgradient iterates on some remarkably broad classes of objective functions. Various existing as well as new bounding conditions are studied, leading to novel iteration complexity results. The terrain of our exploration expands to stochastic proximal subgradient *** Codes 90C33, 65K15, 90C46, 49K35, 90C26 Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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Best arm identification for cascading bandits in the fixed confidence setting 37

Best arm identification for cascading bandits in the fixed c...

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37th International Conference on Machine Learning, ICML 2020

作者： Zhong, Zixin Cheung, Wang Chi Tan, Vincent Y. F. Department of Mathematics National University of Singapore Singapore Singapore Department of Industrial Systems and Management National University of Singapore Singapore Singapore Institute of Operations Research and Analytics National University of Singapore Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

ISBN: (纸本)9781713821120

We design and analyze CASCADEBAI, an algorithm for finding the best set of K items, also called an arm, within the framework of cascading bandits. An upper bound on the time complexity of CASCADEBAI is derived by overcoming a crucial analytical challenge, namely, that of probabilistically estimating the amount of available feedback at each step. To do so, we define a new class of random variables (r.v. s) which we term as left-sided sub-Gaussian r.v. s;this class is a relaxed version of the sub-Gaussian r.v. s. This enables the application of a sufficiently tight Bernstein-Type concentration inequality. We show, through the derivation of a lower bound on the time complexity, that the performance of CASCADEBAI is optimal in some practical regimes. Finally, extensive numerical simulations corroborate the efficacy of CASCADEBAI as well as the tightness of our upper bound on its time complexity. © 2020 by the Authors All rights reserved.

关键词： Machine learning

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Syntactic representation learning for neural network based TTS with syntactic parse tree traversal

arXiv

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arXiv 2020年

作者： Song, Changhe Li, Jingbei Zhou, Yixuan Wu, Zhiyong Meng, Helen Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong

Syntactic structure of a sentence text is correlated with the prosodic structure of the speech that is crucial for improving the prosody and naturalness of a text-to-speech (TTS) system. Nowadays TTS systems usually try to incorporate syntactic structure information with manually designed features based on expert knowledge. In this paper, we propose a syntactic representation learning method based on syntactic parse tree traversal to automatically utilize the syntactic structure information. Two constituent label sequences are linearized through left-first and right-first traversals from constituent parse tree. Syntactic representations are then extracted at word level from each constituent label sequence by a corresponding uni-directional gated recurrent unit (GRU) network. Meanwhile, nuclear-norm maximization loss is introduced to enhance the discriminability and diversity of the embeddings of constituent labels. Upsampled syntactic representations and phoneme embeddings are concatenated to serve as the encoder input of Tacotron2. Experimental results demonstrate the effectiveness of our proposed approach, with mean opinion score (MOS) increasing from 3.70 to 3.82 and ABX preference exceeding by 17% compared with the baseline. In addition, for sentences with multiple syntactic parse trees, prosodic differences can be clearly perceived from the synthesized speeches. Copyright © 2020, The Authors. All rights reserved.

关键词： Syntactics

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On the social and cognitive dimensions of wicked environmental problems characterized by conceptual and solution uncertainty

arXiv

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arXiv 2021年

作者： Arroyave, Felber J. Goyeneche, Oscar Yandy Romero Gore, Meredith Heimeriks, Gaston Jenkins, Jeffrey Petersen, Alexander M. Department of Management of Complex Systems Ernest and Julio Gallo Management Program School of Engineering University of California MercedCA95343 United States Utrecht University Centre for Global Challenges Netherlands Department of Geographical Sciences University of Maryland College ParkMD20742 United States Copernicus Institute of Sustainable Development Utrecht University Netherlands

We develop a quantitative framework for understanding the class of wicked problems that emerge at the intersections of natural, social, and technological complex systems. Wicked problems reflect our incomplete understanding of interdependent global systems and the systemic risk they pose;such problems escape solutions because they are often ill-defined, and thus mis-identified and under-appreciated by communities of problem-solvers. While there are well-documented benefits to tackling boundary-crossing problems from various viewpoints, the integration of diverse approaches can nevertheless contribute confusion around the collective understanding of the core concepts and feasible solutions. We explore this paradox by analyzing the development of both scholarly (social) and topical (cognitive) communities - two facets of knowledge production studies here that contribute towards the evolution of knowledge in and around a problem, termed a knowledge trajectory - associated with three wicked problems: deforestation, invasive species, and wildlife trade. We posit that saturation in the dynamics of social and cognitive diversity growth is an indicator of reduced uncertainty in the evolution of the comprehensive knowledge trajectory emerging around each wicked problem. Informed by comprehensive bibliometric data capturing both social and cognitive dimensions of each problem domain, we thereby develop a framework that assesses the stability of knowledge trajectory dynamics as an indicator of wickedness associated with conceptual and solution uncertainty. As such, our results identify wildlife trade as a wicked problem that may be difficult to address given recent instability in its knowledge trajectory. © 2021, CC BY.

关键词： Deforestation

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