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A guide to Strategic Partnerships : Structure collaboration between academia and wider society
- 2020
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Editorial collection (pop. science, debate, etc.)abstract
- To facilitate work on strategic partnerships, 16 higher education institutions in Sweden have produced a guide, within a partnership with funding from Vinnova. The “Strategic Partnership Guide” is a description of and a guide to strategic partnerships. It is formulated from the perspective of a higher education institution. The target group is staff working with collaboration in the area of operational support at Swedish universities and colleges, to provide support in their professional role. The guide may also be useful for vice-chancellors, management teams at higher education institutions, executive boards or academic leaders, to help familiarise themselves with this kind of collaboration. The guide provides an introduction to strategic partnerships and compiles experiences and lessons learned that can facilitate work when getting started with or further developing partnerships.
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| 2. |
- Blamey, Ben, et al.
(author)
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Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit
- 2021
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In: GigaScience. - : Oxford University Press. - 2047-217X. ; 10:3, s. 1-14
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Journal article (peer-reviewed)abstract
- BACKGROUND: Large streamed datasets, characteristic of life science applications, are often resource-intensive to process, transport and store. We propose a pipeline model, a design pattern for scientific pipelines, where an incoming stream of scientific data is organized into a tiered or ordered "data hierarchy". We introduce the HASTE Toolkit, a proof-of-concept cloud-native software toolkit based on this pipeline model, to partition and prioritize data streams to optimize use of limited computing resources.FINDINGS: In our pipeline model, an "interestingness function" assigns an interestingness score to data objects in the stream, inducing a data hierarchy. From this score, a "policy" guides decisions on how to prioritize computational resource use for a given object. The HASTE Toolkit is a collection of tools to adopt this approach. We evaluate with 2 microscopy imaging case studies. The first is a high content screening experiment, where images are analyzed in an on-premise container cloud to prioritize storage and subsequent computation. The second considers edge processing of images for upload into the public cloud for real-time control of a transmission electron microscope.CONCLUSIONS: Through our evaluation, we created smart data pipelines capable of effective use of storage, compute, and network resources, enabling more efficient data-intensive experiments. We note a beneficial separation between scientific concerns of data priority, and the implementation of this behaviour for different resources in different deployment contexts. The toolkit allows intelligent prioritization to be `bolted on' to new and existing systems - and is intended for use with a range of technologies in different deployment scenarios.
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| 4. |
- Gupta, Ankit, et al.
(author)
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Is brightfield all you need for MoA prediction?
- 2022
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Conference paper (peer-reviewed)abstract
- Fluorescence staining techniques, such as Cell Painting, together with fluorescence microscopy have proven invaluable for visualizing and quantifying the effects that drugs and other perturbations have on cultured cells. However, fluorescence microscopy is expensive, time-consuming, and labor-intensive, and the stains applied can be cytotoxic, interfering with the activity under study. The simplest form of microscopy, brightfield microscopy, lacks these downsides, but the images produced have low contrast and the cellular compartments are difficult to discern. Nevertheless, by harnessing deep learning, these brightfield images may still be sufficient for various predictive purposes. In this study, we compared the predictive performance of models trained on fluorescence images to those trained on brightfield images for predicting the mechanism of action (MoA) of different drugs. We also extracted CellProfiler features from the fluorescence images and used them to benchmark the performance. Overall, we found comparable and correlated predictive performance for the two imaging modalities. This is promising for future studies of MoAs in time-lapse experiments.
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Handledning för strategiska partnerskap : Strukturerad samverkan mellan akademin och det omgivande samhället
- 2020
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Editorial collection (pop. science, debate, etc.)abstract
- För att underlätta arbetet med strategiska partnerskap har 16 lärosäten i Sverige tagit fram en handledning, inom ett samarbete med finansiering från Vinnova. “Handledning för strategiska partnerskap” är en beskrivning av och vägledning till strategiska partnerskap. Den är formulerad ur ett lärosätes perspektiv. Målgruppen är personal som arbetar med samverkan inom verksamhetsstöd vid svenska universitet och högskolor, som ett stöd i deras yrkesutövning. Men även rektorer, lärosätesledningar, styrelser och akademiska ledare kan ha användning av handledningen, för att sätta sig in i villkoren för denna typ av samverkan. Handledningen ger en introduktion till strategiska partnerskap och samlar erfarenheter och lärdomar som kan underlätta arbetet att komma igång med eller vidareutveckla partnerskap.
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| 6. |
- Harrison, Philip J., et al.
(author)
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Deep-learning models for lipid nanoparticle-based drug delivery
- 2021
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In: Nanomedicine. - : Future Medicine. - 1743-5889 .- 1748-6963. ; 16:13, s. 1097-1110
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Journal article (peer-reviewed)abstract
- Background: Early prediction of time-lapse microscopy experiments enables intelligent data management and decision-making. Aim: Using time-lapse data of HepG2 cells exposed to lipid nanoparticles loaded with mRNA for expression of GFP, the authors hypothesized that it is possible to predict in advance whether a cell will express GFP. Methods: The first modeling approach used a convolutional neural network extracting per-cell features at early time points. These features were then combined and explored using either a long short-term memory network (approach 2) or time series feature extraction and gradient boosting machines (approach 3). Results: Accounting for the temporal dynamics significantly improved performance. Conclusion: The results highlight the benefit of accounting for temporal dynamics when studying drug delivery using high-content imaging.
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| 7. |
- Harrison, Philip John, et al.
(author)
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Evaluating the utility of brightfield image data for mechanism of action prediction
- 2023
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In: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 19:7
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Journal article (peer-reviewed)abstract
- Fluorescence staining techniques, such as Cell Painting, together with fluorescence microscopy have proven invaluable for visualizing and quantifying the effects that drugs and other perturbations have on cultured cells. However, fluorescence microscopy is expensive, time-consuming, labor-intensive, and the stains applied can be cytotoxic, interfering with the activity under study. The simplest form of microscopy, brightfield microscopy, lacks these downsides, but the images produced have low contrast and the cellular compartments are difficult to discern. Nevertheless, by harnessing deep learning, these brightfield images may still be sufficient for various predictive purposes. In this study, we compared the predictive performance of models trained on fluorescence images to those trained on brightfield images for predicting the mechanism of action (MoA) of different drugs. We also extracted CellProfiler features from the fluorescence images and used them to benchmark the performance. Overall, we found comparable and largely correlated predictive performance for the two imaging modalities. This is promising for future studies of MoAs in time-lapse experiments for which using fluorescence images is problematic. Explorations based on explainable AI techniques also provided valuable insights regarding compounds that were better predicted by one modality over the other.
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| 8. |
- Wieslander, Håkan
(author)
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Application, Optimisation and Evaluation of Deep Learning for Biomedical Imaging
- 2022
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Doctoral thesis (other academic/artistic)abstract
- Microscopy imaging is a powerful technique when studying biology at a cellular and sub-cellular level. When combined with digital image analysis it creates an invaluable tool for investigating complex biological processes and phenomena. However, imaging at the cell and sub-cellular level tends to generate large amounts of data which can be difficult to analyse, navigate and store. Despite these difficulties, large data volumes mean more information content which is beneficial for computational methods like machine learning, especially deep learning. The union of microscopy imaging and deep learning thus provides numerous opportunities for advancing our scientific understanding and uncovering interesting and useful biological insights.The work in this thesis explores various means for optimising information extraction from microscopy data utilising image analysis with deep learning. The focus is on three different imaging modalities: bright-field; fluorescence; and transmission electron microscopy. Within these modalities different learning-based image analysis and processing techniques are explored, ranging from image classification and detection to image restoration and translation. The main contributions are: (i) a computational method for diagnosing oral and cervical cancer based on smear samples and bright-field microscopy; (ii) a hierarchical analysis of whole-slide tissue images from fluorescence microscopy and introducing a confidence based measure for pixel classifications; (iii) an image restoration model for motion-degraded images from transmission electron microscopy with an evaluation of model overfitting on underlying textures; and (iv) an image-to-image translation (virtual staining) of cell images from bright-field to fluorescence microscopy, optimised for biological feature relevance. A common theme underlying all the investigations in this thesis is that the evaluation of the methods used is in relation to the biological question at hand.
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| 9. |
- Wieslander, Håkan, et al.
(author)
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Deep learning and conformal prediction for hierarchical analysis of large-scale whole-slide tissue images
- 2021
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In: IEEE journal of biomedical and health informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 2168-2194 .- 2168-2208. ; 25:2, s. 371-380
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Journal article (peer-reviewed)abstract
- With the increasing amount of image data collected from biomedical experiments there is an urgent need for smarter and more effective analysis methods. Many scientific questions require analysis of image subregions related to some specific biology. Finding such regions of interest (ROIs) at low resolution and limiting the data subjected to final quantification at high resolution can reduce computational requirements and save time. In this paper we propose a three-step pipeline: First, bounding boxes for ROIs are located at low resolution. Next, ROIs are subjected to semantic segmentation into sub-regions at mid-resolution. We also estimate the confidence of the segmented sub-regions. Finally, quantitative measurements are extracted at high resolution. We use deep learning for the first two steps in the pipeline and conformal prediction for confidence assessment. We show that limiting final quantitative analysis to sub regions with high confidence reduces noise and increases separability of observed biological effects.
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