Article
Computer Science, Interdisciplinary Applications
Vishnu Preetham Revelli, Gauri Sharma, S. Kiruthika Devi
Summary: This project aims to extract text from braille text images and provide translated English text and audio output using a customized CNN model. The CNN model demonstrates robustness in image recognition and classification tasks, making it valuable for addressing challenges faced by visually impaired individuals.
ADVANCES IN ENGINEERING SOFTWARE
(2022)
Article
Computer Science, Artificial Intelligence
Sergey N. Pozdnyakov, Michele Ceriotti
Summary: Graph neural networks (GNN) are popular in machine learning and have been successful in predicting properties of molecules and materials. However, first-order GNNs are known to be incomplete, leading to the design of more complex schemes. The construction of graph representations for molecules adds a geometric dimension, with the most common approach being to consider atoms as vertices and connect them with bonds. This approach, known as distance graph NNs (dGNN), has shown excellent resolving power in chemical ML. However, the authors present a counterexample that proves dGNNs are not complete even for fully-connected graphs induced by 3D atom clouds.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Nemanja Milosevic, Milos Rackovic
Summary: Convolutional neural networks have become indispensable in various machine learning applications, particularly in image classification, but research on their robustness and susceptibility to adversarial attacks is crucial. A new classification method based on missing features has been proposed, showing improved robustness compared to traditional models, although the enhancement in validation accuracy may come at the cost of losing important knowledge. Proposed solutions are being validated against the CIFAR-10 image classification dataset.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Engineering, Marine
K. Janas, I. A. Milne, J. R. Whelan
Summary: This study demonstrates a novel application of CNN for identifying mooring line failure of turret-moored FPSO. The CNN successfully distinguished turret responses associated with intact and broken mooring, with classification accuracy improved through additional hidden layers and retraining, particularly in minimal offset response conditions. The CNN offers an effective and lower-cost alternative to existing mooring failure detection approaches for the offshore industry.
Article
Geochemistry & Geophysics
Miguel Liu-Schiaffini, Gregory Ng, Cyril Grima, Duncan Young
Summary: The article presents a deep learning model for automated ice bed identification, which can capture fine-grained basal detail and detect basal echoes automatically. The proposed method shows comparable or superior performance to the manual approach in certain applications.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Biochemistry & Molecular Biology
Heiko Dunkel, Henning Wehrmann, Lars R. Jensen, Andreas W. Kuss, Stefan Simm
Summary: Non-coding RNA (ncRNA) classes play important roles in cell regulation and identification of diagnostic and therapeutic biomarkers. Researchers utilized machine learning models, including different neural network architectures, to improve the classification and prediction of ncRNAs.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Agronomy
Astrid Tempelaere, Bart De Ketelaere, Jiaqi He, Ioannis Kalfas, Michiel Pieters, Wouter Saeys, Remi Van Belleghem, Leen Van Doorselaer, Pieter Verboven, Bart M. Nicolai
Summary: This article introduces recent developments in artificial intelligence for extracting information on postharvest disorders from complex image data obtained by modern imaging systems. Machine vision inspection using RGB imaging and advanced techniques such as spectral cameras, X-ray, and MRI is increasingly used in the postharvest industry to nondestructively analyze disorders in horticultural products. However, challenges in the design of deep learning models, such as the need for large quantities of labeled data, model explainability, and generalizability, need to be addressed.
POSTHARVEST BIOLOGY AND TECHNOLOGY
(2023)
Review
Chemistry, Analytical
Bruno Debus, Hadi Parastar, Peter Harrington, Dmitry Kirsanov
Summary: Recent extensive research in Deep Learning has led to the development of machine learning algorithms dedicated to solving complex tasks, drawing attention from various fields including analytical chemistry. These powerful algorithms can extract qualitative and quantitative information from complex chemical measurements.
TRAC-TRENDS IN ANALYTICAL CHEMISTRY
(2021)
Article
Mathematical & Computational Biology
Donald E. Brown, Suchetha Sharma, James A. Jablonski, Arthur Weltman
Summary: This study investigates the use of neural network techniques to predict patient health conditions with CPET data and finds that these techniques provide higher levels of accuracy compared to traditional flowchart methods.
Article
Computer Science, Information Systems
Manuel Torres, Rafael Alvarez, Miguel Cazorla
Summary: Cybercriminals constantly develop new techniques to evade security measures, resulting in rapid evolution of malware. Detecting malware across multiple systems is challenging due to unique characteristics of each computing environment. Traditional signature-based malware detection has been replaced by modern approaches, such as machine learning and behavior-based threat detection. Researchers use these techniques to extract features from various data sources and feed them to models for accurate prediction.
Article
Computer Science, Artificial Intelligence
Sanjeev Kumar, Kajal Panda
Summary: This paper proposes a novel malware detection and classification architecture based on image visualization using fine-tuned convolutional neural networks. The methodology involves using a pre-trained VGG16 model as a feature extractor and different feature selection methods to construct a feature map. The MLP classifier achieves the best accuracy in detecting malware.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Information Systems
Dawid Grzelak, Krzysztof Podlaski, Grzegorz Wiatrowski
Summary: An approach using deep learning technique to generalize OCR task for recognizing Polish letters is presented. By extending the dataset with Polish diacritics A and C, the study shows that convolutional neural network can properly recognize shadows and noises of Polish characters and distinguish between similar letters A and Ą. A neural network trained without Polish characters, however, fails to treat letters A and Ą correctly.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2021)
Article
Geosciences, Multidisciplinary
Raymond Sellevold, Miren Vizcaino
Summary: This study combines artificial neural networks with CESM2 and RCM simulations to establish relationships between global climate model output and Greenland ice sheet melt. The models estimate an increasing trend in Greenland ice sheet melt in the future, with climate model sensitivity being the primary source of uncertainty throughout the 21st century.
GEOPHYSICAL RESEARCH LETTERS
(2021)
Article
Computer Science, Artificial Intelligence
Felipe Buitrago, Luis Fernando Castillo Ossa, Jeferson Arango-Lopez
Summary: Surface defects in industrial refrigerator manufacturing processes can cause production losses and compromise product quality. Visual quality inspection is currently a subjective process that requires expert intervention, limiting efficiency and leading to errors. This paper proposes a novel approach using CNN and deflectometry for automatic surface defect detection, which shows promise for quality control in refrigerator manufacturing and other industries. The method combines the accuracy of CNNs in image classification and the sensitivity of deflectometry to detect subtle surface variations.
Article
Engineering, Biomedical
Shima Nofallah, Sachin Mehta, Ezgi Mercan, Stevan Knezevich, Caitlin J. May, Donald Weaver, Daniela Witten, Joann G. Elmore, Linda Shapiro
Summary: This study evaluated the performance of two state-of-the-art CNNs, ESPNet and DenseNet, for mitosis classification in skin biopsies. DenseNet showed higher sensitivity and specificity compared to ESPNet on the primary melanoma dataset, and both models performed well on the MITOS dataset. DenseNet outperformed other architectures in terms of F-score and recall on the MITOS dataset, despite having longer inference times.
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
(2021)
Article
Biochemistry & Molecular Biology
Andrea Thorn
Summary: Machine learning methods, especially convolutional neural networks, have been used in cryo-EM and macromolecular crystallographic structure solution. However, their acceptance in the academic community remains limited. The potential for exploiting machine learning fully, particularly in protein fold prediction using Artificial Intelligence, depends on the formulation of training data, appropriate methods, and critical assessment of outputs, possibly even requiring explanation of AI decisions.
CURRENT OPINION IN STRUCTURAL BIOLOGY
(2022)
Article
Medicine, Research & Experimental
Mujgan Yilmaz, Andrea Thorn, Michala Skovlund Sorensen, Claus Lindkaer Jensen, Michael Mork Petersen
Summary: Sarcomas are rare malignant tumors treated with surgery, where negative pressure wound therapy (NPWT) has shown to reduce postoperative complications. A study compared NPWT with conventional wound dressing in surgical removal of deep-seated high-malignant STS, aiming to reduce wound complications.
Review
Chemistry, Multidisciplinary
Barbara Schamberger, Ricardo Ziege, Karine Anselme, Martine Ben Amar, Michal Bykowski, Andre P. G. Castro, Amaia Cipitria, Rhoslyn A. Coles, Rumiana Dimova, Michaela Eder, Sebastian Ehrig, Luis M. Escudero, Myfanwy E. Evans, Paulo R. Fernandes, Peter Fratzl, Liesbet Geris, Notburga Gierlinger, Edouard Hannezo, Ales Iglic, Jacob J. K. Kirkensgaard, Philip Kollmannsberger, Lucja Kowalewska, Nicholas A. Kurniawan, Ioannis Papantoniou, Laurent Pieuchot, Tiago H. V. Pires, Lars D. Renner, Andrew O. Sageman-Furnas, Gerd E. Schroder-Turk, Anupam Sengupta, Vikas R. Sharma, Antonio Tagua, Caterina Tomba, Xavier Trepat, Sarah L. Waters, Edwina F. Yeo, Andreas Roschger, Cecile M. Bidan, John W. C. Dunlop
Summary: Surface curvature plays an important role in biological systems, from cell membranes to tissues and organs. Experimental and theoretical investigations have supported the relevance of surface curvature in biology. This review provides an introduction to the key concepts of surface curvature in biological systems and discusses the challenges in measuring it. The review also highlights the response of cells, tissues, and organisms to curvature, as well as the interplay between the distribution of morphogens or micro-organisms and the emergence of curvature.
ADVANCED MATERIALS
(2023)
Article
Engineering, Biomedical
Ryosuke Matsuzawa, Akira Matsuo, Shuya Fukamachi, Sho Shimada, Midori Takeuchi, Takuya Nishina, Philip Kollmannsberger, Ryo Sudo, Satoru Okuda, Tadahiro Yamashita
Summary: Tissue engineers have used 3D scaffolds to control multicellular dynamics and tissue microstructures, but the detachment of cells from scaffolds has become a problem. By developing a new computational simulation method and a particle-based model, this study successfully reproduced the detachment processes observed in experiments and revealed the mechanism behind collective cellular detachment.
ACTA BIOMATERIALIA
(2023)
Review
Crystallography
Yunyun Gao, Johannes Kaub, Gianluca Santoni, Nicholas Pearce, Andrea Thorn
Summary: The article reviews the role of the main protease of SARS-CoV-1/SARS-CoV-2 in cleaving viral polyproteins and its significance as a drug target. It highlights the structure-based design of inhibitors and suggests potential future research directions.
CRYSTALLOGRAPHY REVIEWS
(2023)
Editorial Material
Biochemical Research Methods
Yunyun Gao, Volker Thorn, Andrea Thorn
Summary: During the COVID-19 pandemic, the structural biology community played a crucial role in solving urgent questions through macromolecular structure determination. However, errors in measurement, data processing, and modeling are present in structures deposited in the Protein Data Bank, requiring a change in error culture to minimize their impact. By addressing issues early and investigating their source, risks can be reduced, benefiting both experimental structural biologists and downstream users who rely on structural models for new discoveries in biology and medicine.
ACTA CRYSTALLOGRAPHICA SECTION D-STRUCTURAL BIOLOGY
(2023)
Article
Biochemical Research Methods
Jon Agirre, Mihaela Atanasova, Haroldas Bagdonas, Charles B. Ballard, Arnaud Basle, James Beilsten-Edmands, Rafael J. Borges, David G. Brown, J. Javier Burgos-Marmol, John M. Berrisford, Paul S. Bond, Iracema Caballero, Lucrezia Catapano, Grzegorz Chojnowski, Atlanta G. Cook, Kevin D. Cowtan, Tristan I. Croll, Judit E. Debreczeni, Nicholas E. Devenish, Eleanor J. Dodson, Tarik R. Drevon, Paul Emsley, Gwyndaf Evans, Phil R. Evans, Maria Fando, James Foadi, Luis Fuentes-Montero, Elspeth F. Garman, Markus Gerstel, Richard J. Gildea, Kaushik Hatti, Maarten L. Hekkelman, Philipp Heuser, Soon Wen Hoh, Michael A. Hough, Huw T. Jenkins, Elisabet Jimenez, Robbie P. Joosten, Ronan M. Keegan, Nicholas Keep, Eugene B. Krissinel, Petr Kolenko, Oleg Kovalevskiy, Victor S. Lamzin, David M. Lawson, Andrey A. Lebedev, Andrew G. W. Leslie, Bernhard Lohkamp, Fei Long, Martin Maly, Airlie J. McCoy, Stuart J. McNicholas, Ana Medina, Claudia Millan, James W. Murray, Garib N. Murshudov, Robert A. Nicholls, Martin E. M. Noble, Robert Oeffner, Navraj S. Pannu, James M. Parkhurst, Nicholas Pearce, Joana Pereira, Anastassis Perrakis, Harold R. Powell, Randy J. Read, Daniel J. Rigden, William Rochira, Massimo Sammito, Filomeno Sanchez Rodriguez, George M. Sheldrick, Kathryn L. Shelley, Felix Simkovic, Adam J. Simpkin, Pavol Skubak, Egor Sobolev, Roberto A. Steiner, Kyle Stevenson, Ivo Tews, Jens M. H. Thomas, Andrea Thorn, Josep Trivino Valls, Ville Uski, Isabel Uson, Alexei Vagin, Sameer Velankar, Melanie Vollmar, Helen Walden, David Waterman, Keith S. Wilson, Martyn D. Winn, Graeme Winter, Marcin Wojdyr, Keitaro Yamashita
Summary: The Collaborative Computational Project No. 4 (CCP4) is an international collective led by the UK, dedicated to the development, testing, distribution, and promotion of software for macromolecular crystallography. The CCP4 suite is a multiplatform collection of programs, unified by familiar execution routines, common libraries, and graphical interfaces. This article serves as a general literature citation for the use of the CCP4 software suite, providing an overview of its recent changes, new features, and future developments, while also highlighting the individual programs within the suite and providing up-to-date references for crystallographers worldwide.
ACTA CRYSTALLOGRAPHICA SECTION D-STRUCTURAL BIOLOGY
(2023)
Article
Multidisciplinary Sciences
Mario C. Benn, Simon A. Pot, Jens Moeller, Tadahiro Yamashita, Charlotte M. Fonta, Gertraud Orend, Philip Kollmannsberger, Viola Vogel
Summary: Controlled tissue growth requires precise control over cell proliferation and differentiation, and the reciprocal feedback between cells and their environment plays a crucial role in this process. Through studying de novo-grown microtissues, we have identified key factors involved in mechanoregulated events that orchestrate the transition from tissue growth to maturation. This knowledge is important for advancing regenerative strategies and combating fibrosis and cancer progression.
Review
Cell Biology
Xiao-chen Bai, Tamir Gonen, Angela M. Gronenborn, Anastassis Perrakis, Andrea Thorn, Jianyi Yang
Summary: Deciphering the intricate architecture of macromolecules is a formidable task, requiring a combination of empirical experimentation and artificial intelligence-based techniques. This Viewpoint discusses the key challenges and opportunities in this process.
NATURE REVIEWS MOLECULAR CELL BIOLOGY
(2023)
Review
Crystallography
Cameron Fyfe, Lea C. von Soosten, Gianluca Santoni, Andrea Thorn
Summary: Coronaviruses have a large genome that codes for proteins involved in RNA production. To maintain high fidelity, these viruses have evolved a proofreading exoribonuclease and utilize methylation to protect viral RNA from host immune response. Structural investigation of these enzymes has provided insights into their function and facilitated the development of drugs to treat COVID-19.
CRYSTALLOGRAPHY REVIEWS
(2023)
Review
Multidisciplinary Sciences
Sabine C. Fischer, George W. Bassel, Philip Kollmannsberger
Summary: Network analysis is a powerful tool in both molecular biology and developmental biology. It translates tissues into spatial networks, where cells are nodes and intercellular connections are edges. This allows mathematical approaches rooted in network science to be used for understanding tissue structure and function. In this article, we describe how tissue abstractions enable the uncovering of the principles behind tissue formation and function. We also discuss the application of network measures in developmental biology and the general developmental rules that govern tissue topology generation. Furthermore, we explore the use of generative models to connect developmental rules with tissue topologies, revealing general mechanisms of how local developmental rules give rise to observed topological properties in multicellular systems.
JOURNAL OF THE ROYAL SOCIETY INTERFACE
(2023)
Review
Crystallography
David C. Briggs, Luise Kandler, Lisa Schmidt, Gianluca Santoni, Andrea Thorn
Summary: The coronavirus SARS-CoV-2 is categorized into three classes of proteins: structural proteins, non-structural proteins, and accessory proteins. While accessory proteins were initially considered non-essential for viral replication, there is increasing evidence suggesting their crucial roles in virus-host interactions by interfering with host cell signaling pathways. This article summarizes the efforts to structurally characterize the accessory proteins of SARS-CoV-2.
CRYSTALLOGRAPHY REVIEWS
(2023)
Review
Crystallography
Oliver Kippes, Andrea Thorn, Gianluca Santoni
Summary: The spike protein and proteases are the main focus of drug development against COVID-19. However, due to mutations and harmful effects on cellular homologs, alternative drug targets are being explored. This review highlights the potential of the SARS-CoV-2 nucleocapsid protein as a drug target, which plays a role in ribonucleoprotein complex assembly and viral replication, as well as modulation of host cell cycle and other stages of infection.
CRYSTALLOGRAPHY REVIEWS
(2022)
Review
Crystallography
Sam Horrell, Gianluca Santoni, Andrea Thorn
Summary: This review summarizes the structural and functional information of SARS-CoV-2 nsp15, discusses structure-based drug design efforts and complementary knowledge related to other coronaviruses, providing a clear starting point for researchers interested in studying nsp15 as a novel drug target for treating COVID-19.
CRYSTALLOGRAPHY REVIEWS
(2022)
