| 1. |
- Barucca, G., et al.
(författare)
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Study of excited Ξ baryons with the P¯ ANDA detector
- 2021
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Ingår i: European Physical Journal A. - : Springer Nature. - 1434-6001 .- 1434-601X. ; 57:4
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Tidskriftsartikel (refereegranskat)abstract
- The study of baryon excitation spectra provides insight into the inner structure of baryons. So far, most of the world-wide efforts have been directed towards N∗ and Δ spectroscopy. Nevertheless, the study of the double and triple strange baryon spectrum provides independent information to the N∗ and Δ spectra. The future antiproton experiment P¯ANDA will provide direct access to final states containing a Ξ¯ Ξ pair, for which production cross sections up to μb are expected in p¯p reactions. With a luminosity of L= 10 31 cm- 2 s- 1 in the first phase of the experiment, the expected cross sections correspond to a production rate of ∼106events/day. With a nearly 4 π detector acceptance, P¯ANDA will thus be a hyperon factory. In this study, reactions of the type p¯p → Ξ¯ +Ξ∗ - as well as p¯p → Ξ¯ ∗ +Ξ- with various decay modes are investigated. For the exclusive reconstruction of the signal events a full decay tree fit is used, resulting in reconstruction efficiencies between 3 and 5%. This allows high statistics data to be collected within a few weeks of data taking.
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| 2. |
- Barucca, G., et al.
(författare)
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The potential of Λ and Ξ- studies with PANDA at FAIR
- 2021
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Ingår i: European Physical Journal A. - : Springer Nature. - 1434-6001 .- 1434-601X. ; 57:4
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Tidskriftsartikel (refereegranskat)abstract
- The antiproton experiment PANDA at FAIR is designed to bring hadron physics to a new level in terms of scope, precision and accuracy. In this work, its unique capability for studies of hyperons is outlined. We discuss ground-state hyperons as diagnostic tools to study non-perturbative aspects of the strong interaction, and fundamental symmetries. New simulation studies have been carried out for two benchmark hyperon-antihyperon production channels: p¯ p→ Λ¯ Λ and p¯ p→ Ξ¯ +Ξ-. The results, presented in detail in this paper, show that hyperon-antihyperon pairs from these reactions can be exclusively reconstructed with high efficiency and very low background contamination. In addition, the polarisation and spin correlations have been studied, exploiting the weak, self-analysing decay of hyperons and antihyperons. Two independent approaches to the finite efficiency have been applied and evaluated: one standard multidimensional efficiency correction approach, and one efficiency independent approach. The applicability of the latter was thoroughly evaluated for all channels, beam momenta and observables. The standard method yields good results in all cases, and shows that spin observables can be studied with high precision and accuracy already in the first phase of data taking with PANDA.
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| 3. |
- Fredin Haslum, Johan, et al.
(författare)
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Cell Painting-based bioactivity prediction boosts high-throughput screening hit-rates and compound diversity
- 2024
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Ingår i: Nature Communications. - : Springer Nature. - 2041-1723. ; 15:1
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Tidskriftsartikel (refereegranskat)abstract
- Identifying active compounds for a target is a time- and resource-intensive task in early drug discovery. Accurate bioactivity prediction using morphological profiles could streamline the process, enabling smaller, more focused compound screens. We investigate the potential of deep learning on unrefined single-concentration activity readouts and Cell Painting data, to predict compound activity across 140 diverse assays. We observe an average ROC-AUC of 0.744 ± 0.108 with 62% of assays achieving ≥0.7, 30% ≥0.8, and 7% ≥0.9. In many cases, the high prediction performance can be achieved using only brightfield images instead of multichannel fluorescence images. A comprehensive analysis shows that Cell Painting-based bioactivity prediction is robust across assay types, technologies, and target classes, with cell-based assays and kinase targets being particularly well-suited for prediction. Experimental validation confirms the enrichment of active compounds. Our findings indicate that models trained on Cell Painting data, combined with a small set of single-concentration data points, can reliably predict the activity of a compound library across diverse targets and assays while maintaining high hit rates and scaffold diversity. This approach has the potential to reduce the size of screening campaigns, saving time and resources, and enabling primary screening with more complex assays.
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| 4. |
- Fredin Haslum, Johan
(författare)
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Machine Learning Methods for Image-based Phenotypic Profiling in Early Drug Discovery
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In the search for new therapeutic treatments, strategies to make the drug discovery process more efficient are crucial. Image-based phenotypic profiling, with its millions of pictures of fluorescent stained cells, is a rich and effective means to capture the morphological effects of potential treatments on living systems. Within this complex data await biological insights and new therapeutic opportunities – but computational tools are needed to unlock them.This thesis examines the role of machine learning in improving the utility and analysis of phenotypic screening data. It focuses on challenges specific to this domain, such as the lack of reliable labels that are essential for supervised learning, as well as confounding factors present in the data that are often unavoidable due to experimental variability. We explore transfer learning to boost model generalization and robustness, analyzing the impact of domain distance, initialization, dataset size, and architecture on the effectiveness of applying natural domain pre-trained weights to biomedical contexts. Building upon this, we delve into self-supervised pretraining for phenotypic image data, but find its direct application is inadequate in this context as it fails to differentiate between various biological effects. To overcome this, we develop new self-supervised learning strategies designed to enable the network to disregard confounding experimental noise, thus enhancing its ability to discern the impacts of various treatments. We further develop a technique that allows a model trained for phenotypic profiling to be adapted to new, unseen data without the need for any labels or supervised learning. Using this approach, a general phenotypic profiling model can be readily adapted to data from different sites without the need for any labels. Beyond our technical contributions, we also show that bioactive compounds identified using the approaches outlined in this thesis have been subsequently confirmed in biological assays through replication in an industrial setting. Our findings indicate that while phenotypic data and biomedical imaging present complex challenges, machine learning techniques can play a pivotal role in making early drug discovery more efficient and effective.
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| 5. |
- Fredin Haslum, Johan, et al.
(författare)
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Metadata-guided Consistency Learning for High Content Images
- 2023
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Ingår i: PLMR: Volume 227: Medical Imaging with Deep Learning, 10-12 July 2023, Nashville, TN, USA.
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Konferensbidrag (refereegranskat)abstract
- High content imaging assays can capture rich phenotypic response data for large sets of compound treatments, aiding in the characterization and discovery of novel drugs. However, extracting representative features from high content images that can capture subtle nuances in phenotypes remains challenging. The lack of high-quality labels makes it difficult to achieve satisfactory results with supervised deep learning. Self-Supervised learning methods have shown great success on natural images, and offer an attractive alternative also to microscopy images. However, we find that self-supervised learning techniques underperform on high content imaging assays. One challenge is the undesirable domain shifts present in the data known as batch effects, which are caused by biological noise or uncontrolled experimental conditions. To this end, we introduce Cross-Domain Consistency Learning (CDCL), a self-supervised approach that is able to learn in the presence of batch effects. CDCL enforces the learning of biological similarities while disregarding undesirable batch-specific signals, leading to more useful and versatile representations. These features are organised according to their morphological changes and are more useful for downstream tasks – such as distinguishing treatments and mechanism of action.
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