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Enhancing image quality in fast neutron-based range verification of proton therapy using a deep learning-based prior in LM-MAP-EM reconstruction

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Physics in medicine and biology 2025年第12期70卷 2025 Jun 5页

作者： Lena Marie Setterdahl Kyrre Skjerdal Hunter Nathaniel Ratliff Kristian Smeland Ytre-Hauge William R B Lionheart Sean Holman Helge Egil Seime Pettersen Francesco Blangiardi Danny Lathouwers Ilker Meric Department of Computer science Electrical engineering and Mathematical sciences Western Norway University of Applied Sciences Inndalsveien 28 Bergen Hordaland 5063 NORWAY. Department of Physics and Technology University of Bergen Allégaten 55 Bergen Hordaland 5007 NORWAY. Department of Mathematics The University of Manchester Alan Turing Building Upper Brook St Manchester England M13 9SR UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND. Department of Oncology and Medical Physics Haukeland Universitetssjukehus Jonas Lies veg 65 Postboks 1400 Bergen Hordaland 5021 NORWAY. Technology Methods and Systems Data Based Methods Fraunhofer Institute for Electronic Nano Systems ENAS Technologie-Campus 3 Chemnitz SN 09126 GERMANY. Department of Radiation Science and Technology Delft University of Technology Mekelweg 15 Delft 2629 JD NETHERLANDS. Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Inndalsveien 28 Bergen Hordaland 5063 NORWAY.

This study investigates the use of list-mode (LM) maximum a posteriori (MAP) expectation maximization (EM) incorporating prior information predicted by a convolutional neural network for image reconstruction in fast neutron (FN)-based proton therapy range verification. Approach. A conditional generative adversarial network (pix2pix) was trained on progressively noisier data, where detector resolution effects were introduced gradually to simulate realistic conditions. FN data were generated using Monte Carlo simulations of an 85 MeV proton pencil beam in a computed tomography (CT)-based lung cancer patient model, with range shifts emulating weight gain and loss. The network was trained to estimate the expected two-dimensional (2D) ground truth FN production distribution from simple back-projection images. Performance was evaluated using mean squared error (MSE), structural similarity index (SSIM), and the correlation between shifts in predicted distributions and true range shifts. Main results. Our results show that pix2pix performs well on noise-free data but suffers from significant degradation when detector resolution effects are introduced. Among the LM-MAP-EM approaches tested, incorporating a mean prior estimate into the reconstruction process improved performance, with LM-MAP-EM using a mean prior estimate outperforming naïve LM maximum likelihood EM (LM-MLEM) and conventional LM-MAP-EM with a smoothing quadratic energy function in terms of SSIM. Significance. Findings suggest that deep learning techniques can enhance iterative reconstruction for range verification in proton therapy. However, the effectiveness of the model is highly dependent on data quality, limiting its robustness in high-noise scenarios. .

关键词： LM-MAP-EM Monte Carlo simulation fast neutron imaging generative adversarial network organic scintillators proton therapy range verification

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Multi-scale stamps for real-time classification of alert streams

arXiv

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arXiv 2023年

作者： Reyes-Jainaga, Ignacio Förster, Francisco Muñoz Arancibia, Alejandra M. Cabrera-Vives, Guillermo Bayo, Amelia Bauer, Franz E. Arredondo, Javier Reyes, Esteban Pignata, Giuliano Mourão, A.M. Silva-Farfán, Javier Galbany, Lluís Álvarez, Alex Astorga, Nicolás Castellanos, Pablo Gallardo, Pedro Moya, Alberto Rodríguez, Diego Center for Mathematical Modeling Universidad de Chile Beauchef 851 Santiago8370456 Chile Millennium Institute of Astrophysics Nuncio Monseñor Sótero Sanz 100 Providencia Santiago Chile Faculty of Physical and Mathematical Sciences Universidad de Chile Chile Departamento de Astronomía Universidad de Chile Chile Department of Computer Science Universidad de Concepción Concepción Chile Data Science Unit Universidad de Concepción Concepción Chile Instituto de Física y Astronomía Universidad de Valparaíso Chile European Southern Observatory Karl-Schwarzschild-Strasse 2 Garching bei München85748 Germany Instituto de Astrofísica Centro de Astroingeniería Facultad de Física Pontificia Universidad Católica de Chile Casilla 306 Santiago22 Chile NeuralWorks Santiago Chile Fintual Administradora General de Fondos S.A. Santiago Chile Instituto de Astrofísica Facultad de Ciencias Exactas Universidad Andres Bello Avda. Fernéndez Concha 700 Las Condes Chile Departamento de Física and CENTRA Center for Astrophysics and Gravitation Instituto Superior Técnico Universidade de Lisboa Portugal Campus UAB Carrer de Can Magrans s/n BarcelonaE-08193 Spain BarcelonaE-08034 Spain Department of Electrical Engineering Universidad de Chile Av. Tupper 2007 Santiago8320000 Chile University of Chile Chile Data Observatory Foundation Santiago Chile

In recent years, automatic classifiers of image cutouts (also called "stamps") have shown to be key for fast supernova discovery. The Vera C. Rubin Observatory will distribute about ten million alerts with their respective stamps each night, enabling the discovery of approximately one million supernovae each year. A growing source of confusion for these classifiers is the presence of satellite glints, sequences of point-like sources produced by rotating satellites or debris. The currently planned Rubin stamps will have a size smaller than the typical separation between these point sources. Thus, a larger field of view stamp could enable the automatic identification of these sources. However, the distribution of larger stamps would be limited by network bandwidth restrictions. We evaluate the impact of using image stamps of different angular sizes and resolutions for the fast classification of events (AGNs, asteroids, bogus, satellites, SNe, and variable stars), using data from the Zwicky Transient Facility. We compare four scenarios: three with the same number of pixels (small field of view with high resolution, large field of view with low resolution, and a multi-scale proposal) and a scenario with the full stamp that has a larger field of view and higher resolution. Compared to small field of view stamps, our multi-scale strategy reduces misclassifications of satellites as asteroids or supernovae, performing on par with high-resolution stamps that are 15 times heavier. We encourage Rubin and its science Collaborations to consider the benefits of implementing multi-scale stamps as a possible update to the alert specification. © 2023, CC BY.

关键词： Supernovae

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An international perspective on the future of systemic sclerosis research (vol 21, pg 174, 2025)

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NATURE REVIEWS RHEUMATOLOGY 2025年第4期21卷 249-249页

作者： Abraham, David J. Black, Carol M. Denton, Christopher P. Distler, Joerg H. W. Domsic, Robyn Feghali-Bostwick, Carol Gourh, Pravitt Hinchcliff, Monique Kolling, Fred Kuwana, Masataka Lafyatis, Robert Landegren, Ulf Mahoney, J. Matthew Martin, Javier Matucci-Cerinic, Marco Mcmahan, Zsuzsanna H. Mora, Ana L. Mouthon, Luc Rabinovitch, Marlene Rojas, Mauricio Rubin, Kristofer Trojanowska, Maria Varga, John Whitfield, Michael L. Gabrielli, Armando Krieg, Thomas Department of Inflammation and Rare Diseases UCL Centre for Rheumatology UCL Division of Medicine Royal Free Hospital Campus London UK Department of Rheumatology University Hospital Düsseldorf Medical Faculty of the Heinrich-Heine University Düsseldorf Germany Hiller Research Center University Hospital Düsseldorf Medical Faculty of Heinrich Heine University Düsseldorf Germany Foundation of Molecular Medicine and Cellular Therapy Polytechnic University of Marche Via Tronto Ancona Italy Division of Rheumatology Department of Internal Medicine University of Pittsburgh Pittsburgh PA USA Department of Medicine Division of Rheumatology and Immunology Medical University of South Carolina Charleston SC USA Scleroderma Genomics and Health Disparities Unit National Institute of Arthritis and Musculoskeletal and Skin Diseases National Institutes of Health Bethesda MD USA Division of Rheumatology Allergy and Immunology Department of Internal Medicine Yale School of Medicine New Haven CT USA Geisel School of Medicine at Dartmouth Hanover NH USA Department of Allergy and Rheumatology. Nippon Medical School Graduate School of Medicine Tokyo Japan Division of Rheumatology and Clinical Immunology. University of Pittsburgh School of Medicine Pittsburgh PA USA Department of Immunology Genetics and Pathology Research programme: Molecular Tools and Functional Genomics Uppsala University Uppsala Sweden The Jackson Laboratory Bar Harbour ME USA Department of Cell Biology and Immunology Institute of Parasitology and Biomedicine López-Neyra CSIC Granada Spain Unit of Immunology Rheumatology Allergy and Rare Diseases and Inflammation fibrosis and aging Initiative IRCCS Ospedle San Raffaele and Vita Salute University San Raffaele Milan Italy Department of Internal Medicine Division of Rheumatology UTHealth Houston Houston TX USA Division of Pulmonary Critical Care and Sleep Medicine Davis Heart and Lung research Institute The Ohio State University College of Medicine C

Systemic sclerosis (SSc) remains a challenging and enigmatic systemic autoimmune disease, owing to its complex pathogenesis, clinical and molecular heterogeneity, and the lack of effective disease-modifying treatments. Despite a century of research in SSc, the interconnections among microvascular dysfunction, autoimmune phenomena and tissue fibrosis in SSc remain unclear. The absence of validated biomarkers and reliable animal models complicates diagnosis and treatment, contributing to high morbidity and mortality. Advances in the past 5 years, such as single-cell RNA sequencing, next-generation sequencing, spatial biology, transcriptomics, genomics, proteomics, metabolomics, microbiome profiling and artificial intelligence, offer new avenues for identifying the early pathogenetic events that, once treated, could change the clinical history of SSc. Collaborative global efforts to integrate these approaches are crucial to developing a comprehensive, mechanistic understanding and enabling personalized therapies. Challenges include disease classification, clinical heterogeneity and the establishment of robust biomarkers for disease activity and progression. Innovative clinical trial designs and patient-centred approaches are essential for developing effective treatments. Emerging therapies, including cell-based and fibroblast-targeting treatments, show promise. Global cooperation, standardized protocols and interdisciplinary research are vital for advancing SSc research and improving patient outcomes. The integration of advanced research techniques holds the potential for important breakthroughs in the diagnosis, treatment and care of individuals with SSc.

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Navigating the challenges in creating complex data systems: a development philosophy

arXiv

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arXiv 2022年

作者： Dittmer, Sören Roberts, Michael Gilbey, Julian Biguri, Ander Preller, Jacobus Rudd, James H.F. Aston, John A.D. Schönlieb, Carola-Bibiane Selby, Ian Breger, Anna Thorpe, Matthew Weir-McCall, Jonathan R. Gkrania-Klotsas, Effrossyni Korhonen, Anna Jefferson, Emily Langs, Georg Yang, Guang Prosch, Helmut Stanczuk, Jan Tang, Jing Babar, Judith Sánchez, Lorena Escudero Teare, Philip Patel, Mishal Wassin, Marcel Holzer, Markus Walton, Nicholas Lió, Pietro Shadbahr, Tolou Sala, Evis Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Addenbrooke’s Hospital Cambridge University Hospitals NHS Trust Cambridge United Kingdom Department of Medicine University of Cambridge Cambridge United Kingdom Department of Pure Mathematics and Mathematical Statistics University of Cambridge Cambridge United Kingdom ZeTeM University of Bremen Bremen Germany Department of Radiology University of Cambridge Cambridge United Kingdom Faculty of Mathematics University of Vienna Austria Department of Mathematics University of Manchester Manchester United Kingdom Royal Papworth Hospital Cambridge Royal Papworth Hospital NHS Foundation Trust Cambridge United Kingdom Language Technology Laboratory University of Cambridge Cambridge United Kingdom Population Health and Genomics School of Medicine University of Dundee Dundee United Kingdom Department of Biomedical Imaging and Image-guided Therapy Computational Imaging Research Lab Medical University of Vienna Vienna Austria National Heart and Lung Institute Imperial College London London United Kingdom Research Program in Systems Oncology Faculty of Medicine University of Helsinki Helsinki Finland Data Science & Artificial Intelligence AstraZeneca Cambridge United Kingdom Clinical Pharmacology & Safety Sciences AstraZeneca Cambridge United Kingdom Contextflow GmbH Vienna Austria Institute of Astronomy University of Cambridge Cambridge United Kingdom Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom

In this perspective, we argue that despite the democratization of powerful tools for data science and machine learning over the last decade, developing the code for a trustworthy and effective data science system (DSS) is getting harder. Perverse incentives and a lack of widespread software engineering (SE) skills are among many root causes we identify that naturally give rise to the current systemic crisis in reproducibility of DSSs. We analyze why SE and building large complex systems is, in general, hard. Based on these insights, we identify how SE addresses those difficulties and how we can apply and generalize SE methods to construct DSSs that are fit for purpose. We advocate two key development philosophies, namely that one should incrementally grow – not biphasically plan and build – DSSs, and one should always employ two types of feedback loops during development: one which tests the code’s correctness and another that evaluates the code’s efficacy. Copyright © 2022, The Authors. All rights reserved.

关键词： Software engineering

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TOI-2005b: An Eccentric Warm Jupiter in Spin-Orbit Alignment

arXiv

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arXiv 2025年

作者： Bieryla, Allyson Dong, Jiayin Zhou, George Eastman, Jason D. Mayorga, L.C. Latham, David W. Carter, Brad Huang, Chelsea X. Quinn, Samuel N. Collins, Karen A. Abe, Lyu Beletsky, Yuri Brahm, Rafael Colón, Knicole D. Essack, Zahra Guillot, Tristan Henning, Thomas Hobson, Melissa J. Horne, Keith Jenkins, Jon M. Jones, Matías I. Jordán, Andrés Osip, David Ricker, George R. Rodriguez, Joseph E. Schulte, Jack Schwarz, Richard P. Seager, Sara Shporer, Avi Suarez, Olga Tan, Thiam-Guan Ting, Eric B. Triaud, Amaury Vanderburg, Andrew Villaseñor, Jesus Noel Vowell, Noah Watkins, Cristilyn N. Winn, Joshua N. Ziegler, Carl Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States University of Southern Queensland Centre for Astrophysics West Street ToowoombaQLD4350 Australia Center for Computational Astrophysics Flatiron Institute 162 Fifth Avenue New YorkNY10010 United States Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States The Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Rd LaurelMD20723 United States Observatoire de la Côte d’Azur UniCA Laboratoire Lagrange CNRS UMR 7293 CS 34229 06304 Nice cedex 4 France Las Campanas Observatory Carnegie Institution for Science Colina El Pino Casilla La Serena601 Chile Facultad de Ingeniería y Ciencias Universidad Adolfo Ibáñez Av. Diagonal las Torres 2640 Santiago Peñalolén Chile Millennium Institute for Astrophysics Chile Data Observatory Foundation Chile GreenbeltMD20771 United States Department of Physics and Astronomy The University of New Mexico 210 Yale Blvd NE AlbuquerqueNM87106 United States Max-Planck-Institut für Astronomie Königstuhl 17 Heidelberg69117 Germany SUPA Physics and Astronomy University of St. Andrews Fife ScotlandKY16 9SS United Kingdom NASA Ames Research Center Moffett FieldCA94035 United States European Southern Observatory Alonso de Córdova 3107 Vitacura Casilla Santiago19001 Chile Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Center for Data Intensive and Time Domain Astronomy Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Department of Earth Atmospheric and Planetary Sciences Massachusetts Institute of Technology CambridgeMA02139 United States Department of Aeronautics and Astronautics MIT 77 Massachusetts Avenue CambridgeMA02139 United States Observatoire de la Côte d’Azur UniCA CNRS CS 34229 Nice06304 cedex 4

We report the discovery and characterization of TOI-2005 b, a warm Jupiter on an eccentric (e ∼ 0.59), 17.3-day orbit around a Vmag = 9.867 rapidly rotating F-star. The object was detected as a candidate by TESS and the planetary nature of TOI-2005 b was then confirmed via a series of ground-based photometric, spectroscopic, and diffraction-limited imaging observations. The planet was found to reside in a low sky-projected stellar obliquity orbit (λ =4.8+2−235 degrees) via a transit spectroscopic observation using the Magellan MIKE spectrograph. TOI-2005 b is one of a few planets known to have a low-obliquity, high-eccentricity orbit, which may be the result of high-eccentricity coplanar migration. The planet has a periastron equilibrium temperature of ∼ 2100 K, similar to some highly irradiated hot Jupiters where atomic metal species have been detected in transmission spectroscopy, and varies by almost 1000 K during its orbit. Future observations of the atmosphere of TOI-2005b can inform us about its radiative timescales thanks to the rapid heating and cooling of the planet. © 2025, CC BY.

关键词： Photometry

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Deep learning - A first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact

arXiv

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arXiv 2020年

作者： Egger, Jan Pepe, Antonio Gsaxner, Christina Jin, Yuan Li, Jianning Kern, Roman Institute of Computer Graphics and Vision Faculty of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Computer Algorithms for Medicine Laboratory Graz Austria Department of Oral and Maxillofacial Surgery Medical University of Graz Graz Austria University Medicine Essen Essen Germany Research Center for Connected Healthcare Big Data Zhejiang Lab Zhejiang Hangzhou China Research Unit Experimental Neurotraumatology Department of Neurosurgery Medical University of Graz Graz Austria Knowledge Discovery Know-Center Graz Austria Institute of Interactive Systems and Data Science Graz University of Technology Graz Austria

Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by drawing inspiration from the learning of a human brain. Similar to the basic structure of a brain, which consists of (billions of) neurons and connections between them, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10,000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides already over 11,000 results in Q3 2020 for the search term 'deep learning', and around 90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines and outline the research impact that they already have during a short period of time. The categories (computer vision, language processing, medical informatics and additional works) have been chosen according to the underlying data sources (image,

关键词： Surveys

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Medical digital twins: enabling precision medicine and medical artificial intelligence

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The Lancet. Digital health 2025年 100864页

作者： Christoph Sadée Stefano Testa Thomas Barba Katherine Hartmann Maximilian Schuessler Alexander Thieme George M Church Ifeoma Okoye Tina Hernandez-Boussard Leroy Hood Ilya Shmulevich Ellen Kuhl Olivier Gevaert Stanford Center for Biomedical Informatics Research Department of Medicine Stanford CA USA. Division of Medical Oncology Department of Medicine Beth Israel Deaconess Medical Center Boston MA USA Department of Medicine Stanford University Stanford CA USA. Stanford Center for Biomedical Informatics Research Department of Medicine Stanford CA USA Department of Internal Medicine Édouard Herriot Hospital Lyon France. Department of Radiology Hospital of the University of Pennsylvania Philadelphia PA USA. Department of Biomedical Data Science Stanford University Stanford CA USA. Department of Radiation Oncology Charité-Universitätsmedizin Berlin Berlin Germany. Department of Molecular and Cellular Biology Harvard University Cambridge MA USA. University of Nigeria Teaching Hospital Ituku-Ozalla Enugu Nigeria University of Nigeria Nsukka Center of Excellence for Clinical Trials Enugu Nigeria. Institute for Systems Biology Seattle WA USA. Departments of Mechanical Engineering and Bioengineering Stanford University Stanford CA USA. Department of Biomedical Data Science Stanford University Stanford CA USA. Electronic address: ogevaert@stanford.edu.

The notion of medical digital twins is gaining popularity both within the scientific community and among the general public; however, much of the recent enthusiasm has occurred in the absence of a consensus on their fundamental make-up. Digital twins originate in the field of engineering, in which a constantly updating virtual copy enables analysis, simulation, and prediction of a real-world object or process. In this Health Policy paper, we evaluate this concept in the context of medicine and outline five key components of the medical digital twin: the patient, data connection, patient-in-silico, interface, and twin synchronisation. We consider how various enabling technologies in multimodal data, artificial intelligence, and mechanistic modelling will pave the way for clinical adoption and provide examples pertaining to oncology and diabetes. We highlight the role of data fusion and the potential of merging artificial intelligence and mechanistic modelling to address the limitations of either the AI or the mechanistic modelling approach used independently. In particular, we highlight how the digital twin concept can support the performance of large language models applied in medicine and its potential to address health-care challenges. We believe that this Health Policy paper will help to guide scientists, clinicians, and policy makers in creating medical digital twins in the future and translating this promising new paradigm from theory into clinical practice.

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Author Correction: Single-cell transcriptomics uncovers EGFR signaling-mediated gastric progenitor cell differentiation in stomach homeostasis

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Nature communications 2023年第1期14卷 6540页

作者： Hitomi Takada Yohei Sasagawa Mika Yoshimura Kaori Tanaka Yoshimi Iwayama Tetsutaro Hayashi Ayako Isomura-Matoba Itoshi Nikaido Akira Kurisaki Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan. Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan. Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan. Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan. itoshi.nikaido@riken.jp. Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan. itoshi.nikaido@riken.jp. Master's/Doctoral Program in Life Science Innovation (Bioinformatics) Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan. itoshi.nikaido@riken.jp. Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan. akikuri@bs.naist.jp.

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Discovery and affinity maturation of antibody fragments from an unfavorably enriched phage display selection by deep sequencing and machine learning

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Journal of Bioscience and Bioengineering 2025年

作者： Kawada, Sakiya Kurumida, Yoichi Ito, Tomoyuki Nguyen, Thuy Duong Nishi, Hafumi Nakazawa, Hikaru Saito, Yutaka Kameda, Tomoshi Tsuda, Koji Umetsu, Mitsuo Department of Biomolecular Engineering of Engineering Tohoku University 6-6-11 Aoba Aramaki Aoba-ku Sendai 980-8579 Japan Department of Data Science School of Frontier Engineering Kitasato University 1-15-1 Kitazato Minami-ku Kanagawa Sagamihara 252-0373 Japan Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology (AIST) 2-4-7 Aomi Koto-ku Tokyo 135-0064 Japan Department of Applied Information Sciences of Information Sciences Tohoku University 6-3-09 Aoba Aramaki Aoba-ku Sendai 980-8579 Japan Tohoku Medical Megabank Organization Tohoku University 2-1 Seiryo-machi Aoba-ku Sendai 980-8573 Japan Faculty of Core Research Ochanomizu University 2-1-1 Ohtsuka Bunkyo-ku Tokyo 112-8610 Japan Department of Computational Biology and Medical Sciences of Frontier Sciences The University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa 277-8561 Japan Center for Advanced Intelligence Project RIKEN 1-4-1 Nihombashi Chuo-ku Tokyo 103-0027 Japan Research and Services Division of Materials Data and Integrated Systems National Institute for Materials Science 1-2-1 Sengen Tsukuba Ibaraki 305-0047 Japan

Phage display selection has been used for directed evolution of antibody fragments. However, variants with binding affinity cannot be always identified due to undesirable enrichment of target-unrelated variants in the biopanning process. Here, our goal was to obtain functional variants by deep sequencing and machine learning from a phage display library where functional variants were not appropriately enriched. Deep sequencing of the previously biopanned pools revealed that amplification bias might have prevented the enrichment of target-binding phages. We performed a sequence similarity search based on the deep sequencing analysis so that the influence of bias was decreased, leading to discovery of a variant with binding affinity, which could not be discovered by a conventional screening method alone. We applied machine learning to the deep sequencing data;the machine learning proposed effective mutations for increasing affinity, allowing us to identify a variant with improved affinity (EC50 = 3.46 μM). In summary, we present the possibility of obtaining functional variants even from unfavorably enriched phage libraries by using deep sequencing and machine learning. © 2025 The Society for Biotechnology, Japan

关键词： Clustering analysis Deep sequencing Galectin-3 Machine learning Phage display VHH

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Tunable schiff-based networks with different bonding sites for enhanced photocatalytic activity under visible-light irradiation: The effects of steric hindrance

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Environmental research 2024年 246卷 118200页

作者： Han Wang Piao Xu Eydhah Almatrafi Ziwei Wang Chengyun Zhou Ting Xiong Hong Qin Yangzhuo He Hailan Yang Zhuotong Zeng Guangming Zeng School of Metallurgy and Environment Central South University Changsha 410083 China Department of Dermatology Second Xiangya Hospital Central South University Changsha 410011 China College of Environmental Science and Engineering and Key Laboratory of Environmental Biology and Pollution Control (Ministry of Education) Hunan University Changsha 410082 China Center of Research Excellence in Renewable Energy and Power Systems Center of Excellence in Desalination Technology Department of Mechanical Engineering King Abdulaziz University Jeddah 21589 Saudi Arabia. College of Environmental Science and Engineering and Key Laboratory of Environmental Biology and Pollution Control (Ministry of Education) Hunan University Changsha 410082 China. Institute of Big Data and Internet Innovation Hunan University of Technology and Business Changsha 410205 China. Center of Research Excellence in Renewable Energy and Power Systems Center of Excellence in Desalination Technology Department of Mechanical Engineering King Abdulaziz University Jeddah 21589 Saudi Arabia. Electronic address: zgming@***.

Organic polymers hold great potential in photocatalysis considering their low cost, structural tailorability, and well-controlled degree of conjugation for efficient electron transfer. Among the polymers, Schiff base networks (SNWs) with high nitrogen content have been noticed. Herein, a series of SNWs is synthesized based on the melamine units and dialdehydes with different bonding sites. The chemical and structural variation caused by steric hindrance as well as the related photoelectric properties of the SNW samples are investigated, along with the application exploration on photocatalytic degradation and energy production. The results demonstrate that only SNW-o based on o-phthalaldehyde responds to visible light, which extends to over 550 nm. SNW-o shows the highest tetracycline degradation rate of 0.02516 min, under 60-min visible light irradiation. Moreover, the HO production of SNW-o is 2.14 times higher than that of g-CN. The enhanced photocatalytic activity could be ascribed to the enlarged visible light adsorption and intramolecular electron transfer. This study indicates the possibility to regulate the optical and electrical properties of organic photocatalysts on a molecular level, providing an effective strategy for rational supramolecular engineering to the applications of organic materials in photocatalysis.

关键词： H(2)O(2) production Photocatalytic degradation Schiff-based networks Steric hindrance Structure tunability

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