Natalio Krasnogor

Prof. at Newcastle University

Newcastle University
Country flagUnited Kingdom

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Natalio Krasnogor is an Associate Professor at Newcastle University in the United Kingdom. His research focuses on engineering biology, artificial intelligence applications in immunohistochemistry, and DNA origami nanostructures. Recent publications include studies on fuzzy neural networks, environmental solutions, and strain engineering for biochemical production.

Positions (2)

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Natalio Krasnogor

University Name
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Newcastle University

Newcastle University’s School of Computing is advertising a PhD studentship in engineering biology, enzyme engineering, sustainability, synthetic biology, bioinformatics, AI, and laboratory automation . The project is part of the NERC-funded EngBio4Env initiative and focuses on discovering and improving PET-degrading enzymes to support a more circular plastics economy . The work appears to be highly interdisciplinary, bringing together expertise across computing and biological sciences. Eligibility highlights: applicants should have an MSc in Chemistry, Microbiology, Synthetic Biology, or a similar field . The post is aimed at students interested in research at the interface of biology, engineering, and sustainability. Funding: the post is described as a studentship funded by the Natural Environment Research Council (NERC) . No stipend amount, tuition coverage, or additional financial details are stated in the post. How to apply: use the application link in the post to open the full advert and application instructions. Check the linked Newcastle University funding page if you need more context on postgraduate funding. Apply through the official route provided by the university. Institution: Newcastle University, School of Computing.

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Natalio Krasnogor

University Name
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Newcastle University

Funded PhD opportunity at Newcastle University in Programming Matter: Dynamic DNA Data Structures for Molecular Information Storage and Computing . This interdisciplinary PhD sits within the EnDROIDS programme and brings together computer science , synthetic biology , engineering biology , and DNA/RNA nanotechnology . The project combines computational design , modelling , and optimisation with experimental implementation of DNA/RNA systems and molecular circuits. The supervisor/poster is Natalio Krasnogor , Professor of Computing Science & Synthetic Biology at Newcastle University. The post welcomes candidates with either computational or experimental experience; you do not need to arrive with expertise in both. Strong applicants from computing, computational biology, synthetic/engineering biology, molecular biology, chemistry, DNA/RNA nanotechnology, or related areas are encouraged to apply. Eligibility: UK Home students, or EU applicants with settled/pre-settled status who meet the relevant residency criteria. Deadline: 30 September 2026. Apply: Use the application link provided in the post for full details and submission instructions.

3 weeks ago

Articles (10)

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2024

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2023

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2023

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2023

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2023

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2023

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2022

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2022

Background The application of artificial intelligence (AI) to whole slide images has the potential to improve research reliability and ultimately diagnostic efficiency and service capacity. Image annotation plays a key role in AI and digital pathology. However, the work‐streams required for tissue‐specific (skin) and immunostain‐specific annotation has not been extensively studied compared with the development of AI algorithms. Objectives The objective of this study is to develop a common workflow for annotating whole slide images of biopsies from inflammatory skin disease immunostained with a variety of epidermal and dermal markers prior to the development of the AI‐assisted analysis pipeline. Methods A total of 45 slides containing 3–5 sections each were scanned using Aperio AT2 slide scanner (Leica Biosystems). These slides were annotated by hand using a commonly used image analysis tool which resulted in more than 4000 images blocks. We used deep learning (DL) methodology to first sequentially segment (epidermis and upper dermis), with the exclusion of common artefacts and second to quantify the immunostained signal in those two compartments of skin biopsies and the ratio of positive cells. Results We validated two DL models using 10‐fold validation runs and by comparing to ground truth manually annotated data. The models achieved an average (global) accuracy of 95.0% for the segmentation of epidermis and dermis and 86.1% for the segmentation of positive/negative cells. Conclusions The application of two DL models in sequence facilitates accurate segmentation of epidermal and dermal structures, exclusion of common artefacts and enables the quantitative analysis of the immunostained signal. However, inaccurate annotation of the slides for training the DL model can decrease the accuracy of the output. Our open source code will facilitate further external validation across different immunostaining platforms and slide scanners.

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2022

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2022

Collaborators (17)

JICHUN LI

Senior Lecturer (Associate Professor)

Newcastle University

UNITED KINGDOM
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Tao Lyu

Lecturer

Cranfield University

UNITED KINGDOM
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Louise Horsfall

Professor at School of Biological Sciences

University of Edinburgh

UNITED KINGDOM
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Nick J. Reynolds

-

UNITED KINGDOM
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Jamie Twycross

Associate Professor

University of Nottingham

UNITED KINGDOM
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Julia Santiago

University of Lausanne

SWITZERLAND
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Savas Konur

University of Bradford

UNITED KINGDOM
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Pedro L. Rodriguez

-

SPAIN
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Emanuela Torelli

Lecturer

Newcastle University

UNITED KINGDOM
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Frederic Coulon

Prof

Cranfield University

UNITED KINGDOM
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Francis Hassard

Reader in Public Health Microbiology at Cranfield University

Cranfield University

UNITED KINGDOM
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Julio R. Banga

Research Professor

Consejo Superior de Investigaciones Cientificas

SPAIN
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Juan Elezgaray

University of Bordeaux

FRANCE
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Marisa MANZANO

Professor (Associate)

University of Udine

ITALY
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Thomas Curtis

-

UNITED KINGDOM
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Shouyong Jiang

Lecturer in Machine Learning

University of Lincoln

UNITED KINGDOM
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David Lea-Smith

Dr at University of East Anglia

University of East Anglia

UNITED KINGDOM
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