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In this study, we developed the autonomous lab (ANL), which is a system based on robotics and artificial intelligence (AI) to conduct biotechnology experiments and formulate scientific hypotheses. This system was designed with modular devices and Bayesian optimization algorithms, allowing it to effectively run a closed loop from culturing to preprocessing, measurement, analysis, and hypothesis formulation. As a case study, we used the ANL to optimize medium conditions for a recombinant Escherichia coli strain, which overproduces glutamic acid. The results demonstrated that our autonomous system successfully replicated the experimental techniques, such as sample preparation and data measurement, and improved both the cell growth rate and the maximum cell growth. The ANL offers a versatile and scalable solution for various applications in the field of bioproduction, with the potential to improve efficiency and reliability of experimental processes in the future.
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http://dx.doi.org/10.1038/s41598-025-89069-y | DOI Listing |
Int J Biol Macromol
September 2025
Natural Composites Research Group Lab, Department of Materials and Production Engineering, The Sirindhorn International Thai-German Graduate School of Engineering (TGGS), King Mongkut's University of Technology North Bangkok (KMUTNB), Bangkok, Thailand.
This review critically examines the rapidly advancing field of cellulosic natural fibre-reinforced polymer (NFRP) composites, with a particular emphasis on material innovation aligned with sustainability and environmental responsibility. The review presents a systematic analysis of recent literature evaluating the mechanical, thermal, water absorption, wear, and machining characteristics of NFRP composites, as well as the influence of advanced processing approaches such as additive manufacturing. Special attention is given to the structure-property relationships and hybridisation strategies employed to address limitations such as relatively lower mechanical performance and durability compared to synthetic fibre composites.
View Article and Find Full Text PDFOpen Res Eur
July 2025
REQUIMTE LAQV Porto, Porto, Porto District, Portugal.
The 2024 Nobel Prizes in Chemistry and Physics mark a watershed moment in the convergence of artificial intelligence (AI) and molecular biology. This article explores how AI, particularly deep learning and neural networks, has revolutionized protein science through breakthroughs in structure prediction and computational design. It highlights the contributions of 2024 Nobel laureates John Hopfield, Geoffrey Hinton, David Baker, Demis Hassabis, and John Jumper, whose foundational work laid the groundwork for AI tools such as AlphaFold.
View Article and Find Full Text PDFJ Sci Comput
August 2025
Department of Mathematics, The Pennsylvania State University, Pennsylvania, USA.
Neural network-based approaches have recently shown significant promise in solving partial differential equations (PDEs) in science and engineering, especially in scenarios featuring complex domains or incorporation of empirical data. One advantage of the neural network methods for PDEs lies in its automatic differentiation (AD), which necessitates only the sample points themselves, unlike traditional finite difference (FD) approximations that require nearby local points to compute derivatives. In this paper, we quantitatively demonstrate the advantage of AD in training neural networks.
View Article and Find Full Text PDFJ Pain
September 2025
Department of Clinical and Health Psychology, Autonomous University of Barcelona, Cerdanyola del Vallès, Spain; Teaching, Research & Innovation Unit, Parc Sanitari Sant Joan de Déu, St. Boi de Llobregat, Spain; Centre for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Sp
Stigma is common in people with chronic pain. At present, however, the measurement of stigma in Spanish-speaking individuals remains a challenge due to a lack of validated measures in Spanish. The present study examines the psychometric properties of the Spanish version of the Stigma Scale for Chronic Illnesses 8-item version (SSCI-8) in people with chronic pain, focusing on dimensionality, factorial invariance, reliability (internal consistency and test-retest), and construct validity.
View Article and Find Full Text PDFAngew Chem Int Ed Engl
September 2025
Key Lab for Special Functional Materials of Ministry of Education, National & Local Joint Engineering Research Center for High-efficiency Display and Lighting Technology, School of Nanoscience and Materials Engineering, and Collaborative Innovation Center of Nano Functional Materials and Application
Metal halide perovskite solar cells (PSCs) hold promise for next-generation photovoltaics but are restricted by suboptimal efficiency and poor long-term stability. In inverted PSC architectures, self-assembled monolayers (SAMs) are widely employed as hole-selective layers (HSLs) due to their favorable energy-level alignment and negligible parasitic absorption. However, traditional SAMs often exhibit weak intermolecular interactions, leading to film aggregation, poor interfacial contact, and severe nonradiative recombination.
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