Search

Your search keyword '"Trygg, Johan"' showing total 1,191 results
Start Over Author "Trygg, Johan"
1,191 results on '"Trygg, Johan"'

1. CellSpot: Deep Learning-Based Efficient Cell Center Detection in Microscopic Images

Author

Khalid, Nabeel, Caroprese, Maria, Lovell, Gillian, Trygg, Johan, Dengel, Andreas, Ahmed, Sheraz, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wand, Michael, editor, Malinovská, Kristína, editor, Schmidhuber, Jürgen, editor, and Tetko, Igor V., editor

Published
2024
Full Text
View/download PDF

2. CellGenie: An End-to-End Pipeline for Synthetic Cellular Data Generation and Segmentation: A Use Case for Cell Segmentation in Microscopic Images

Author

Khalid, Nabeel, Koochali, Mohammadmahdi, Leon, Duway Nicolas Lesmes, Caroprese, Maria, Lovell, Gillian, Porto, Daniel A., Trygg, Johan, Dengel, Andreas, Ahmed, Sheraz, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yap, Moi Hoon, editor, Kendrick, Connah, editor, Behera, Ardhendu, editor, Cootes, Timothy, editor, and Zwiggelaar, Reyer, editor

Published
2024
Full Text
View/download PDF

3. Bounding Box Is All You Need: Learning to Segment Cells in 2D Microscopic Images via Box Annotations

Author

Khalid, Nabeel, Caroprese, Maria, Lovell, Gillian, Porto, Daniel A., Trygg, Johan, Dengel, Andreas, Ahmed, Sheraz, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yap, Moi Hoon, editor, Kendrick, Connah, editor, Behera, Ardhendu, editor, Cootes, Timothy, editor, and Zwiggelaar, Reyer, editor

Published
2024
Full Text
View/download PDF

5. Out-of-Distribution Example Detection in Deep Neural Networks using Distance to Modelled Embedding

Author

Sjögren, Rickard and Trygg, Johan

Subjects
Computer Science - Machine Learning
Abstract

Adoption of deep learning in safety-critical systems raise the need for understanding what deep neural networks do not understand after models have been deployed. The behaviour of deep neural networks is undefined for so called out-of-distribution examples. That is, examples from another distribution than the training set. Several methodologies to detect out-of-distribution examples during prediction-time have been proposed, but these methodologies constrain either neural network architecture, how the neural network is trained, suffer from performance overhead, or assume that the nature of out-of-distribution examples are known a priori. We present Distance to Modelled Embedding (DIME) that we use to detect out-of-distribution examples during prediction time. By approximating the training set embedding into feature space as a linear hyperplane, we derive a simple, unsupervised, highly performant and computationally efficient method. DIME allows us to add prediction-time detection of out-of-distribution examples to neural network models without altering architecture or training while imposing minimal constraints on when it is applicable. In our experiments, we demonstrate that by using DIME as an add-on after training, we efficiently detect out-of-distribution examples during prediction and match state-of-the-art methods while being more versatile and introducing negligible computational overhead., Comment: 15 pages, 5 figures

Published
2021

6. Deep Learning Architectures for the Prediction of YY1-Mediated Chromatin Loops

Author

Abbasi, Ahtisham Fazeel, Asim, Muhammad Nabeel, Trygg, Johan, Dengel, Andreas, Ahmed, Sheraz, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Guo, Xuan, editor, Mangul, Serghei, editor, Patterson, Murray, editor, and Zelikovsky, Alexander, editor

Published
2023
Full Text
View/download PDF

7. PACE: Point Annotation-Based Cell Segmentation for Efficient Microscopic Image Analysis

Author

Khalid, Nabeel, Froes, Tiago Comassetto, Caroprese, Maria, Lovell, Gillian, Trygg, Johan, Dengel, Andreas, Ahmed, Sheraz, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Iliadis, Lazaros, editor, Papaleonidas, Antonios, editor, Angelov, Plamen, editor, and Jayne, Chrisina, editor

Published
2023
Full Text
View/download PDF

10. Point2Mask: A Weakly Supervised Approach for Cell Segmentation Using Point Annotation

Author

Khalid, Nabeel, Schmeisser, Fabian, Koochali, Mohammadmahdi, Munir, Mohsin, Edlund, Christoffer, Jackson, Timothy R, Trygg, Johan, Sjögren, Rickard, Dengel, Andreas, Ahmed, Sheraz, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yang, Guang, editor, Aviles-Rivero, Angelica, editor, Roberts, Michael, editor, and Schönlieb, Carola-Bibiane, editor

Published
2022
Full Text
View/download PDF

14. Statistical analysis in metabolic phenotyping

Author

Blaise, Benjamin J., Correia, Gonçalo D. S., Haggart, Gordon A., Surowiec, Izabella, Sands, Caroline, Lewis, Matthew R., Pearce, Jake T. M., Trygg, Johan, Nicholson, Jeremy K., Holmes, Elaine, and Ebbels, Timothy M. D.

Published
2021
Full Text
View/download PDF

17. Feasibility and performance of cross-clone Raman calibration models in CHO cultivation

Author

Machleid, Rafael, Hoehse, Marek, Scholze, Steffi, Mazarakis, Kleanthis, Nilsson, David, Johansson, Erik, Zehe, Christoph, Trygg, Johan, Grimm, Christian, Surowiec, Izabella, Machleid, Rafael, Hoehse, Marek, Scholze, Steffi, Mazarakis, Kleanthis, Nilsson, David, Johansson, Erik, Zehe, Christoph, Trygg, Johan, Grimm, Christian, and Surowiec, Izabella

Abstract

Raman spectroscopy is widely used in monitoring and controlling cell cultivations for biopharmaceutical drug manufacturing. However, its implementation for culture monitoring in the cell line development stage has received little attention. Therefore, the impact of clonal differences, such as productivity and growth, on the prediction accuracy and transferability of Raman calibration models is not yet well described. Raman OPLS models were developed for predicting titer, glucose and lactate using eleven CHO clones from a single cell line. These clones exhibited diverse productivity and growth rates. The calibration models were evaluated for clone-related biases using clone-wise linear regression analysis on cross validated predictions. The results revealed that clonal differences did not affect the prediction of glucose and lactate, but titer models showed a significant clone-related bias, which remained even after applying variable selection methods. The bias was associated with clonal productivity and lead to increased prediction errors when titer models were transferred to cultivations with productivity levels outside the range of their training data. The findings demonstrate the feasibility of Raman-based monitoring of glucose and lactate in cell line development with high accuracy. However, accurate titer prediction requires careful consideration of clonal characteristics during model development.

Published
2024
Full Text
View/download PDF

18. A novel data mining framework to investigate causes of boiler failures in waste-to-energy plants

Author

Wang, Dong, Jiang, Lili, Kjellander, Måns, Weidemann, Eva, Trygg, Johan, Tysklind, Mats, Wang, Dong, Jiang, Lili, Kjellander, Måns, Weidemann, Eva, Trygg, Johan, and Tysklind, Mats

Abstract

Examining boiler failure causes is crucial for thermal power plant safety and profitability. However, traditional approaches are complex and expensive, lacking precise operational insights. Although data-driven approaches hold substantial potential in addressing these challenges, there is a gap in systematic approaches for investigating failure root causes with unlabeled data. Therefore, we proffered a novel framework rooted in data mining methodologies to probe the accountable operational variables for boiler failures. The primary objective was to furnish precise guidance for future operations to proactively prevent similar failures. The framework was centered on two data mining approaches, Principal Component Analysis (PCA) + K-means and Deep Embedded Clustering (DEC), with PCA + K-means serving as the baseline against which the performance of DEC was evaluated. To demonstrate the framework’s specifics, a case study was performed using datasets obtained from a waste-to-energy plant in Sweden. The results showed the following: (1) The clustering outcomes of DEC consistently surpass those of PCA + K-means across nearly every dimension. (2) The operational temperature variables T-BSH3rm, T-BSH2l, T-BSH3r, T-BSH1l, T-SbSH3, and T-BSH1r emerged as the most significant contributors to the failures. It is advisable to maintain the operational levels of T-BSH3rm, T-BSH2l, T-BSH3r, T-BSH1l, T-SbSH3, and T-BSH1r around 527 °C, 432 °C, 482 °C, 338 °C, 313 °C, and 343 °C respectively. Moreover, it is crucial to prevent these values from reaching or exceeding 594 °C, 471 °C, 537 °C, 355 °C, 340 °C, and 359 °C for prolonged durations. The findings offer the opportunity to improve future operational conditions, thereby extending the overall service life of the boiler. Consequently, operators can address faulty tubes during scheduled annual maintenance without encountering failures and disrupting production.

Published
2024
Full Text
View/download PDF

23. Individual variation in lipidomic profiles of healthy subjects in response to omega-3 Fatty acids.

Author

Nording, Malin L, Yang, Jun, Georgi, Katrin, Hegedus Karbowski, Christine, German, J Bruce, Weiss, Robert H, Hogg, Ronald J, Trygg, Johan, Hammock, Bruce D, and Zivkovic, Angela M

Subjects
Humans, Lipids, Fatty Acids, Omega-3, Lipoproteins, Risk Factors, Pilot Projects, Dietary Supplements, Adult, Middle Aged, Female, Male, Lipid Metabolism, Oxylipins, Metabolomics, Young Adult, Fatty Acids, Omega-3, General Science & Technology
Abstract

IntroductionConflicting findings in both interventional and observational studies have resulted in a lack of consensus on the benefits of ω3 fatty acids in reducing disease risk. This may be due to individual variability in response. We used a multi-platform lipidomic approach to investigate both the consistent and inconsistent responses of individuals comprehensively to a defined ω3 intervention.MethodsThe lipidomic profile including fatty acids, lipid classes, lipoprotein distribution, and oxylipins was examined multi- and uni-variately in 12 healthy subjects pre vs. post six weeks of ω3 fatty acids (1.9 g/d eicosapentaenoic acid [EPA] and 1.5 g/d docosahexaenoic acid [DHA]).ResultsTotal lipidomic and oxylipin profiles were significantly different pre vs. post treatment across all subjects (p=0.00007 and p=0.00002 respectively). There was a strong correlation between oxylipin profiles and EPA and DHA incorporated into different lipid classes (r(2)=0.93). However, strikingly divergent responses among individuals were also observed. Both ω3 and ω6 fatty acid metabolites displayed a large degree of variation among the subjects. For example, in half of the subjects, two arachidonic acid cyclooxygenase products, prostaglandin E2 (PGE2) and thromboxane B2 (TXB2), and a lipoxygenase product, 12-hydroxyeicosatetraenoic acid (12-HETE) significantly decreased post intervention, whereas in the other half they either did not change or increased. The EPA lipoxygenase metabolite 12-hydroxyeicosapentaenoic acid (12-HEPE) varied among subjects from an 82% decrease to a 5,000% increase.ConclusionsOur results show that certain defined responses to ω3 fatty acid intervention were consistent across all subjects. However, there was also a high degree of inter-individual variability in certain aspects of lipid metabolism. This lipidomic based phenotyping approach demonstrated that individual responsiveness to ω3 fatty acids is highly variable and measurable, and could be used as a means to assess the effectiveness of ω3 interventions in modifying disease risk and determining metabolic phenotype.

Published
2013

43. Multiblock and Path Modeling with OnPLS

Author

Löfstedt, Tommy, Hanafi, Mohamed, Trygg, Johan, Abdi, Herve, editor, Chin, Wynne W., editor, Esposito Vinzi, Vincenzo, editor, Russolillo, Giorgio, editor, and Trinchera, Laura, editor

Published
2013
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results