The HASTE project was featured in the June 2022 issue of Framtidens Forskning. It includes the statements from Carolina Wählby, the principal investigator of the project, Salman Toor, senior researcher in the project, and Ida-Maria Sintorn, the industry partner from Vironova AB.
Ebba Bergman presented a poster at the 11th Symposium on Pharmaceutical Profiling in Drug Discovery and Development with the title “Conformal Prediction applied to Cell Painting: Confidence in MoA Prediction”. The conference was held online on January 27th, 2022.
We are happy to announce that our paper “Proactive Autoscaling for Edge Computing Systems with Kubernetes” is now accepted at the 14th IEEE/ACM International Conference on Utility and Cloud Computing UCC 2021.
With the emergence of the Internet of Things and 5G technologies, the edge computing paradigm is playing increasingly important roles with better availability, latency-control and performance. However, existing autoscaling tools for edge computing applications do not utilize heterogeneous resources of edge systems efficiently, leaving scope for performance improvement. In this work, we propose a Proactive Pod Autoscaler (PPA) for edge computing applications on Kubernetes. The proposed PPA is able to forecast workloads in advance with multiple user-defined/customized metrics and to scale edge computing applications up and down correspondingly. The PPA is optimized and evaluated on an example CPU-intensive edge computing application further. It can be concluded that the proposed PPA outperforms the default pod autoscaler of Kubernetes on both efficiencies of resource utilization and application performance. The article also highlights future possible improvements on the proposed PPA.
We are happy to present the joining of Xiaobo Zhao as our newest member of HASTE. Xiaobo Zhao is joining the group of Andreas Hellander to work as a PostDoctoral Researcher. Xiaobo will be working on research and development of intelligent stream data processing pipelines, and the development of intelligent and efficient cloud systems capable of mapping data and compute to a variety of cloud computing and data storage e-infrastructure based on the quality and interestingness of the data.
Xiaobo Zhao received the M.S. degree in Communications and Information System from Northwestern Polytechnical University, Xi’an, China in 2015. He later received a Ph.D. degree in Electrical and Computer Engineering from Aarhus University, Aarhus, Denmark in 2020. Before joining the Hellander lab, he was a Research Assistant at Aarhus University and focused on efficient ML/DL service offloading to Edge/Cloud servers.
We are happy to announce that our paper “Deep learning models for lipid-nanoparticle-based drug delivery” is now available ahead of print and open access in the journal Nanomedicine.
Authors: Harrison PJ, Wieslander H, Sabirsh A, Karlsson J, Malmsjö V, Hellander A, Wählby C & Spjuth O.
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.
In the figure below we show a schematic for the modelling approach used in the paper that combined convolutional and recurrent neural networks (long short-term memory, LSTM). This model is used for predicting information only present in the GFP channel at the end of the experiment from other imaging channels captured during the early time points of the experiment, prior to any GFP expression.
Following the win at the Adipocyte Imaging Challenge organized by AstraZeneca, two PhD students from the team, Ankit Gupta, and Håkan Wieslander were asked to comment in a technical report in Nature on the topic of virtual staining.
Fluorescence imaging is a valuable tool for biological analysis but is time-consuming and toxic to the cells. Using deep learning to virtually stain bright-field images is an active field of research that can alleviate these problems. Phil Harrison, a PhD student in the HASTE group, presented a poster at the Swedish Symposium on Deep Learning (SSDL) 2021 based on the HASTE team’s winning solution for the Adipocyte Cell Imaging Challenge. The poster presented our approach and results.
We are happy to present our newest member of HASTE: Dan Rosén! Dan is joining the group of Ola Spjuth to work as a Data Engineer. In his projects he will work with data pipelines and interact closely with microscopes to help reaching the goals of HASTE to act on collected image streams and make intelligent decisions and control microscopes to prioritize collecting the most interesting data.
AI Sweden and AstraZeneca organised the Adipocyte Cell Imaging Challenge, a two-week-long hackathon to help AstraZeneca accelerate the drug development process. The task was to use machine learning in solving the problem of labelling cell images without requiring toxic preprocessing of cell cultures by predicting the content of the fluorescence images from the corresponding bright-field images.
The solution used the Learning Under Privileged Information (LUPI) paradigm to solve the problem. LUPI enables the inclusion of additional (privileged) information when training machine learning models, data that is not available when making predictions. In this case, the segmentation masks of the nuclei were used as the privileged information during the training of machine learning models. Our solution will help AstraZeneca to speed up the drug discovery process and bring drugs to market quicker.
We are looking for a skilled Data Engineer to join the HASTE team!
In collaboration with other researchers, develop, implement and test systems for AI-controlled automated microscopes. The task includes interacting directly with the microscope and establishing pipelines where models trained on previously taken images decide and control where the microscope should take images in the next step to reach a specific goal. We are looking for a candidate with a genuine interest in technology and automation, and who enjoys solving problems including both practical interaction with hardware (robots, microscopes) and different types of software. Since our microscopes generate large amounts of images, the position will also include large-scale data management and -analysis. You will work with researchers in AI modeling and biological laboratory sciences, and contribute to implementing methods and evaluating them for different types of biological problems.
This is a 2-year position that is part of the HASTE project, funded by the Swedish Foundation for Strategic Research (SSF) aiming at developing new, intelligent ways of processing and managing very large amounts of microscopy images in order to be able to leverage the imminent explosion of image data from modern experimental setups in the biosciences. Industry collaborators are Vironova AB and AstraZeneca AB.
A master’s degree in engineering or a university degree in a relevant field is a requirement. Good programming skills in Python and preferably more programming languages is a requirement. Experience in AI modeling, Linux systems as well as developing REST services and APIs is a requirement. Experience of AI modeling on image data, practical handling of automated microscopes and working with software containers (e.g. Docker/Singularity) is meriting.