| 1. |
|
|
| 2. |
|
|
| 3. |
- Almgren, Karin, et al.
(författare)
-
Role of fibre-fibre and fibre-matrix adhesion in stress transfer in composites made from resin-impregnated paper sheets.
- 2009
-
Ingår i: International Journal of Adhesion and Adhesives. - : Elsevier BV. - 0143-7496 .- 1879-0127. ; 29:5, s. 551-557
-
Tidskriftsartikel (refereegranskat)abstract
- Paper-reinforced plastics are gaining increased interest as packaging materials, where mechanical properties are of great importance. Strength and stress transfer in paper sheets are controlled by fibre-fibre bonds. In paper-reinforced plastics, where the sheet is impregnated with a polymer resin, other stress-transfer mechanisms may be more important. The influence of fibre-fibre bonds on the strength of paper-reinforced plastics was therefore investigated. Paper sheets with different degrees of fibre-fibre bonding were manufactured and used as reinforcement in a polymeric matrix. Image analysis tools were used to verify that the difference in the degree of fibre-fibre bonding had been preserved in the composite materials. Strength and stiffness of the composites were experimentally determined and showed no correlation to the degree of fibre-fibre bonding, in contrast to the behaviour of unimpregnated paper sheets. The degree of fibre-fibre bonding is therefore believed to have little importance in this type of material, where stress is mainly transferred through the fibre-matrix interface.
|
|
| 4. |
- Andersson, Axel, et al.
(författare)
-
Cell Segmentation of in situ Transcriptomics Data using Signed Graph Partitioning
- 2023
-
Ingår i: Graph-Based Representations in Pattern Recognition. - Cham : Springer. - 9783031427947 - 9783031427954 ; , s. 139-148
-
Konferensbidrag (refereegranskat)abstract
- The locations of different mRNA molecules can be revealed by multiplexed in situ RNA detection. By assigning detected mRNA molecules to individual cells, it is possible to identify many different cell types in parallel. This in turn enables investigation of the spatial cellular architecture in tissue, which is crucial for furthering our understanding of biological processes and diseases. However, cell typing typically depends on the segmentation of cell nuclei, which is often done based on images of a DNA stain, such as DAPI. Limiting cell definition to a nuclear stain makes it fundamentally difficult to determine accurate cell borders, and thereby also difficult to assign mRNA molecules to the correct cell. As such, we have developed a computational tool that segments cells solely based on the local composition of mRNA molecules. First, a small neural network is trained to compute attractive and repulsive edges between pairs of mRNA molecules. The signed graph is then partitioned by a mutex watershed into components corresponding to different cells. We evaluated our method on two publicly available datasets and compared it against the current state-of-the-art and older baselines. We conclude that combining neural networks with combinatorial optimization is a promising approach for cell segmentation of in situ transcriptomics data. The tool is open-source and publicly available for use at https://github.com/wahlby-lab/IS3G.
|
|
| 5. |
- Andersson, Axel
(författare)
-
Computational Methods for Image-Based Spatial Transcriptomics
- 2024
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Why does cancer develop, spread, grow, and lead to mortality? To answer these questions, one must study the fundamental building blocks of all living organisms — cells. Like a well-calibrated manufacturing unit, cells follow precise instructions by gene expression to initiate the synthesis of proteins, the workforces that drive all living biochemical processes.Recently, researchers have developed techniques for imaging the expression of hundreds of unique genes within tissue samples. This information is extremely valuable for understanding the cellular activities behind cancer-related diseases. These methods, collectively known as image-based spatial transcriptomics (IST) techniques, use fluorescence microscopy to combinatorically label mRNA species (corresponding to expressed genes) in tissue samples. Here, automatic image analysis is required to locate fluorescence signals and decode the combinatorial code. This process results in large quantities of points, marking the location of expressed genes. These new data formats pose several challenges regarding visualization and automated analysis.This thesis presents several computational methods and applications related to data generated from IST methods. Key contributions include: (i) A decoding method that jointly optimizes the detection and decoding of signals, particularly beneficial in scenarios with low signal-to-noise ratios or densely packed signals; (ii) a computational method for automatically delineating regions with similar gene compositions — efficient, interactive, and scalable for exploring patterns across different scales; (iii) a software enabling interactive visualization of millions of gene markers atop Terapixel-sized images (TissUUmaps); (iv) a tool utilizing signed-graph partitioning for the automatic identification of cells, independent of the complementary nuclear stain; (v) A fast and analytical expression for a score that quantifies co-localization between spatial points (such as located genes); (vi) a demonstration that gene expression markers can train deep-learning models to classify tissue morphology.In the final contribution (vii), an IST technique features in a clinical study to spatially map the molecular diversity within tumors from patients with colorectal liver metastases, specifically those exhibiting a desmoplastic growth pattern. The study unveils novel molecular patterns characterizing cellular diversity in the transitional region between healthy liver tissue and the tumor. While a direct answer to the initial questions remains elusive, this study sheds illuminating insights into the growth dynamics of colorectal cancer liver metastases, bringing us closer to understanding the journey from development to mortality in cancer.
|
|
| 6. |
- Andersson, Axel, et al.
(författare)
-
Points2Regions : Fast, interactive clustering of imaging-based spatial transcriptomics data
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Imaging-based spatial transcriptomics techniques generate image data that, once processed, results in a set of spatial points with categorical labels for different mRNA species. A crucial part of analyzing downstream data involves the analysis of these point patterns. Here, biologically interesting patterns can be explored at different spatial scales. Molecular patterns on a cellular level would correspond to cell types, whereas patterns on a millimeter scale would correspond to tissue-level structures. Often, clustering methods are employed to identify and segment regions with distinct point-patterns. Traditional clustering techniques for such data are constrained by reliance on complementary data or extensive machine learning, limiting their applicability to tasks on a particular scale. This paper introduces 'Points2Regions', a practical tool for clustering spatial points with categorical labels. Its flexible and computationally efficient clustering approach enables pattern discovery across multiple scales, making it a powerful tool for exploratory analysis. Points2Regions has demonstrated efficient performance in various datasets, adeptly defining biologically relevant regions similar to those found by scale-specific methods. As a Python package integrated into TissUUmaps and a Napari plugin, it offers interactive clustering and visualization, significantly enhancing user experience in data exploration. In essence, Points2Regions presents a user-friendly and simple tool for exploratory analysis of spatial points with categorical labels.
|
|
| 7. |
- Andersson, Jonas, et al.
(författare)
-
Exact analysis of One-Warehouse-Multiple-Retailer inventory systems with quantity restricted deliveries
- 2023
-
Ingår i: European Journal of Operational Research. - : Elsevier BV. - 0377-2217. ; 309:3, s. 1161-1172
-
Tidskriftsartikel (refereegranskat)abstract
- Joint consideration and coordination of inventory and shipment decisions is an important challenge in the quest for sustainable distribution systems. This paper addresses this issue by presenting a method for exact analysis of the inventory level distributions in a centralized One-Warehouse-Multiple-Retailer (OWMR) inventory system with quantity restricted deliveries from the warehouse. The system is characterized by continuous review (R,nQ) policies, complete backordering, and compound Poisson demand. The class of quantity restricted delivery policies we consider is motivated by a common desire in practice to ship full load carriers. It generalizes the assumption of unrestricted partial deliveries often used in the literature, and allows the warehouse to only ship retailer specific delivery batch quantities to the different retailers. The delivery batches typically represent package or pallet sizes, containers, or full truckloads. An attractive feature of our approach is that it does not add to the computational effort of analyzing the special case of unrestricted partial deliveries available in the existing literature. A numerical study shows that accounting for quantitative delivery restrictions used in transportation and handling operations when optimizing the reorder points in multi-echelon systems can be very important. Failure to do so can lead to high backorder costs and inadequate customer service.
|
|
| 8. |
|
|
| 9. |
- Ayyalasomayajula, Kalyan Ram, 1980-, et al.
(författare)
-
CalligraphyNet: Augmenting handwriting generation with quill based stroke width
- 2019
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Realistic handwritten document generation garners a lot ofinterest from the document research community for its abilityto generate annotated data. In the current approach we haveused GAN-based stroke width enrichment and style transferbased refinement over generated data which result in realisticlooking handwritten document images. The GAN part of dataaugmentation transfers the stroke variation introduced by awriting instrument onto images rendered from trajectories cre-ated by tracking coordinates along the stylus movement. Thecoordinates from stylus movement are augmented with thelearned stroke width variations during the data augmentationblock. An RNN model is then trained to learn the variationalong the movement of the stylus along with the stroke varia-tions corresponding to an input sequence of characters. Thismodel is then used to generate images of words or sentencesgiven an input character string. A document image thus cre-ated is used as a mask to transfer the style variations of the inkand the parchment. The generated image can capture the colorcontent of the ink and parchment useful for creating annotated data.
|
|
| 10. |
- Ayyalasomayajula, Kalyan Ram, 1980-
(författare)
-
Learning based segmentation and generation methods for handwritten document images
- 2019
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Computerized analysis of handwritten documents is an active research area in image analysis and computer vision. The goal is to create tools that can be available for use at university libraries and for researchers in the humanities. Working with large collections of handwritten documents is very time consuming and many old books and letters remain unread for centuries. Efficient computerized methods could help researchers in history, philology and computer linguistics to cost-effectively conduct a whole new type of research based on large collections of documents. The thesis makes a contribution to this area through the development of methods based on machine learning. The passage of time degrades historical documents. Humidity, stains, heat, mold and natural aging of the materials for hundreds of years make the documents increasingly difficult to interpret. The first half of the dissertation is therefore focused on cleaning the visual information in these documents by image segmentation methods based on energy minimization and machine learning. However, machine learning algorithms learn by imitating what is expected of them. One prerequisite for these methods to work is that ground truth is available. This causes a problem for historical documents because there is a shortage of experts who can help to interpret and interpret them. The second part of the thesis is therefore about automatically creating synthetic documents that are similar to handwritten historical documents. Because they are generated from a known text, they have a given facet. The visual content of the generated historical documents includes variation in the writing style and also imitates degradation factors to make the images realistic. When machine learning is trained on synthetic images of handwritten text, with a known facet, in many cases they can even give an even better result for real historical documents.
|
|
