| 1. |
- Surowiec, Izabella, et al.
(författare)
-
Joint and unique multiblock analysis of biological data : multiomics malaria study
- 2019
-
Ingår i: Faraday discussions. - Cambridge : Royal Society of Chemistry. - 1359-6640 .- 1364-5498. ; 218, s. 268-283
-
Tidskriftsartikel (refereegranskat)abstract
- Modern profiling technologies enable obtaining large amounts of data which can be later used for comprehensive understanding of the studied system. Proper evaluation of such data is challenging, and cannot be faced by bare analysis of separate datasets. Integrated approaches are necessary, because only data integration allows finding correlation trends common for all studied data sets and revealing hidden structures not known a priori. This improves understanding and interpretation of the complex systems. Joint and Unique MultiBlock Analysis (JUMBA) is an analysis method based on the OnPLS-algorithm that decomposes a set of matrices into joint parts containing variation shared with other connected matrices and variation that is unique for each single matrix. Mapping unique variation is important from a data integration perspective, since it certainly cannot be expected that all variation co-varies. In this work we used JUMBA for integrated analysis of lipidomic, metabolomic and oxylipin datasets obtained from profiling of plasma samples from children infected with P. falciparum malaria. P. falciparum is one of the primary contributors to childhood mortality and obstetric complications in the developing world, what makes development of the new diagnostic and prognostic tools, as well as better understanding of the disease, of utmost importance. In presented work JUMBA made it possible to detect already known trends related to disease progression, but also to discover new structures in the data connected to food intake and personal differences in metabolism. By separating the variation in each data set into joint and unique, JUMBA reduced complexity of the analysis, facilitated detection of samples and variables corresponding to specific structures across multiple datasets and by doing this enabled fast interpretation of the studied system. All this makes JUMBA a perfect choice for multiblock analysis of systems biology data.
|
|
| 2. |
- Sjögren, Rickard, 1989-
(författare)
-
Synergies between Chemometrics and Machine Learning
- 2021
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Thanks to digitization and automation, data in all shapes and forms are generated in ever-growing quantities throughout society, industry and science. Data-driven methods, such as machine learning algorithms, are already widely used to benefit from all these data in all kinds of applications, ranging from text suggestion in smartphones to process monitoring in industry. To ensure maximal benefit to society, we need workflows to generate, analyze and model data that are performant as well as robust and trustworthy.There are several scientific disciplines aiming to develop data-driven methodologies, two of which are machine learning and chemometrics. Machine learning is part of artificial intelligence and develops algorithms that learn from data. Chemometrics, on the other hand, is a subfield of chemistry aiming to generate and analyze complex chemical data in an optimal manner. There is already a certain overlap between the two fields where machine learning algorithms are used for predictive modelling within chemometrics. Although, since both fields aims to increase value of data and have disparate backgrounds, there are plenty of possible synergies to benefit both fields. Thanks to its wide applicability, there are many tools and lessons learned within machine learning that goes beyond the predictive models that are used within chemometrics today. On the other hand, chemometrics has always been application-oriented and this pragmatism has made it widely used for quality assurance within regulated industries. This thesis serves to nuance the relationship between the two fields and show that knowledge in either field can be used to benefit the other. We explore how tools widely used in applied machine learning can help chemometrics break new ground in a case study of text analysis of patents in Paper I. We then draw inspiration from chemometrics and show how principles of experimental design can help us optimize large-scale data processing pipelines in Paper II and how a method common in chemometrics can be adapted to allow artificial neural networks detect outlier observations in Paper III. We then show how experimental design principles can be used to ensure quality in the core of concurrent machine learning, namely generation of large-scale datasets in Paper IV. Lastly, we outline directions for future research and how state-of-the-art research in machine learning can benefit chemometric method development.
|
|
| 3. |
- Rentoft, Matilda, et al.
(författare)
-
A geographically matched control population efficiently limits the number of candidate disease-causing variants in an unbiased whole-genome analysis
- 2019
-
Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203 .- 1932-6203. ; 14:3
-
Tidskriftsartikel (refereegranskat)abstract
- Whole-genome sequencing is a promising approach for human autosomal dominant disease studies. However, the vast number of genetic variants observed by this method constitutes a challenge when trying to identify the causal variants. This is often handled by restricting disease studies to the most damaging variants, e. g. those found in coding regions, and overlooking the remaining genetic variation. Such a biased approach explains in part why the genetic causes of many families with dominantly inherited diseases, in spite of being included in whole-genome sequencing studies, are left unsolved today. Here we explore the use of a geographically matched control population to minimize the number of candidate disease-causing variants without excluding variants based on assumptions on genomic position or functional predictions. To exemplify the benefit of the geographically matched control population we apply a typical disease variant filtering strategy in a family with an autosomal dominant form of colorectal cancer. With the use of the geographically matched control population we end up with 26 candidate variants genome wide. This is in contrast to the tens of thousands of candidates left when only making use of available public variant datasets. The effect of the local control population is dual, it (1) reduces the total number of candidate variants shared between affected individuals, and more importantly (2) increases the rate by which the number of candidate variants are reduced as additional affected family members are included in the filtering strategy. We demonstrate that the application of a geographically matched control population effectively limits the number of candidate disease-causing variants and may provide the means by which variants suitable for functional studies are identified genome wide.
|
|
| 4. |
- Svensson, Daniel, et al.
(författare)
-
doepipeline : a systematic approach to optimizing multi-level and multi-step data processing workflows
- 2019
-
Ingår i: BMC Bioinformatics. - : BioMed Central. - 1471-2105. ; 20:1
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Selecting the proper parameter settings for bioinformatic software tools is challenging. Not only will each parameter have an individual effect on the outcome, but there are also potential interaction effects between parameters. Both of these effects may be difficult to predict. To make the situation even more complex, multiple tools may be run in a sequential pipeline where the final output depends on the parameter configuration for each tool in the pipeline. Because of the complexity and difficulty of predicting outcomes, in practice parameters are often left at default settings or set based on personal or peer experience obtained in a trial and error fashion. To allow for the reliable and efficient selection of parameters for bioinformatic pipelines, a systematic approach is needed.Results: We present doepipeline, a novel approach to optimizing bioinformatic software parameters, based on core concepts of the Design of Experiments methodology and recent advances in subset designs. Optimal parameter settings are first approximated in a screening phase using a subset design that efficiently spans the entire search space, then optimized in the subsequent phase using response surface designs and OLS modeling. Doepipeline was used to optimize parameters in four use cases; 1) de-novo assembly, 2) scaffolding of a fragmented genome assembly, 3) k-mer taxonomic classification of Oxford Nanopore Technologies MinION reads, and 4) genetic variant calling. In all four cases, doepipeline found parameter settings that produced a better outcome with respect to the characteristic measured when compared to using default values. Our approach is implemented and available in the Python package doepipeline.Conclusions: Our proposed methodology provides a systematic and robust framework for optimizing software parameter settings, in contrast to labor- and time-intensive manual parameter tweaking. Implementation in doepipeline makes our methodology accessible and user-friendly, and allows for automatic optimization of tools in a wide range of cases. The source code of doepipeline is available at https://github.com/clicumu/doepipeline and it can be installed through conda-forge.
|
|
| 5. |
- Arvidsson, Björn, et al.
(författare)
-
Online capillary solid phase extraction and liquid chromatographic separation with quantitative tandem mass spectrometric detection (SPE-LC-MS/MS) of ximelagatran and its metabolites in a complex matrix.
- 2009
-
Ingår i: Journal of chromatography. B. - : Elsevier BV. - 1570-0232 .- 1873-376X. ; 877:3, s. 291-297
-
Tidskriftsartikel (refereegranskat)abstract
- This work presents the development and validation of a fully automated quantitative analysis method of melagatran, its prodrug ximelagatran, and its major metabolites for the study of drug behavior in biofluids. The method involves online sample clean-up and enrichment on a C4 capillary column followed by separation on a capillary C18 column. Electrospray ionization tandem mass spectrometric detection in positive ion mode was performed with multiple reactions monitoring of eight different transients, divided into two time segments with four transients each. The structural similarity, the complexity of the matrix (pig liver extract) and the formation of isobaric fragment ions, made efficient chromatographic separation necessary. The analysis method provides valid accuracy (<9%; RSD%), precision (<8%; RSD%), linearity (<1.2 nM–1 μM; R2 > 0.999), limit of quantitation (<3.6 nM), retention repeatability (<1.2%; RSD%), selectivity, as well as analyte and column stabilities over a wide concentration range.
|
|
| 6. |
- Al Jebali, Ramsey, et al.
(författare)
-
A helium gas scintillator active target for photoreaction measurements
- 2015
-
Ingår i: European Physical Journal A. Hadrons and Nuclei. - : Springer Science and Business Media LLC. - 1434-6001. ; 51:10
-
Tidskriftsartikel (refereegranskat)abstract
- A multi-cell He gas scintillator active target, designed for the measurement of photoreaction cross sections, is described. The target has four main chambers, giving an overall thickness of 0.103 g/cm(3) at an operating pressure of 2MPa. Scintillations are read out by photomultiplier tubes and the addition of small amounts of N-2 to the He, to shift the scintillation emission from UV to visible, is discussed. First results of measurements at the MAX IV Laboratory tagged-photon facility show that the target has a timing resolution of around 1 ns and can cope well with a high-flux photon beam. The determination of reaction cross sections from target yields relies on a Monte Carlo simulation, which considers scintillation light transport, photodisintegration processes in He-4, background photon interactions in target windows and interactions of the reaction-product particles in the gas and target container. The predictions of this simulation are compared to the measured target response.
|
|
| 7. |
|
|
| 8. |
- Dal Bello, F., et al.
(författare)
-
Improvement of the quality and shelf life of wheat bread by fermentation with the antifungal strain Lactobacillus plantarum FST 1.7
- 2007
-
Ingår i: Journal of Cereal Science. - London, United Kingdom : Elsevier. - 0733-5210 .- 1095-9963. ; 45:3, s. 309-318
-
Tidskriftsartikel (refereegranskat)abstract
- Lactobacillus plantarum FST 1.7 was screened for in vitro antimicrobial activity and was shown to be active against spoilage moulds and bacteria. Isolation of antimicrobial compounds from cell-free supernatant identified lactic acid, phenyllactic acid and the two cyclic dipeptides cyclo ((L)-Leu-(L)-Pro) and cyclo ((L)-Phe-(L)-Pro) as the major components responsible for this activity. L. plantarum FST 1.7 was tested for the ability to produce the antifungal compounds during sourdough fermentation and to produce bread of good quality and increased shelf-life. A rheofermentometer was used to examine the gaseous release and development characteristics of the dough. A range of parameters was determined including pH, TTA and specific loaf volume. The results were compared with those obtained using Lactobacillus sanfranciscensis, a chemically acidified and a non-acidified dough. The quality of sourdough and bread produced using L. plantarum FST 1.7 was comparable to that obtained using common sourdough starters, e.g. L. sanfranciscensis. Sourdoughs and breads were evaluated for the ability to retard growth of Fusarium culmorum and Fusarium graminearum two fungi found on breads. Sourdough and bread produced with strain FST 1.7 showed consistent ability to retard the growth of both Fusarium species, thus indicating that L. plantarum FST 1.7 has also the potential to improve the shelf-life of wheat bread.
|
|
| 9. |
- Delfin, C, et al.
(författare)
-
Influence of laser pulse duration on relativistic channels
- 2002
-
Ingår i: Physics of Plasmas. - : AIP Publishing. - 1070-664X .- 1089-7674. ; 9:3, s. 937-940
-
Tidskriftsartikel (refereegranskat)abstract
- A high-power (10 TW) laser is employed to generate relativistic channels in an underdense plasma. The lengths of the channels are measured by imaging the Thomson-scattered light, and the gas densities are determined through the forward Raman scattered light. The laser-pulse parameters are varied and their impact on the channel formation is studied. It is found that increasing the laser pulse duration in many cases produces longer channels, even as this implies reducing the laser peak power. A theoretical discussion is presented, proposing an explanation of the experimental results. (C) 2002 American Institute of Physics.
|
|
| 10. |
- Khalid, Nabeel, et al.
(författare)
-
DeepCeNS : An end-to-end Pipeline for Cell and Nucleus Segmentation in Microscopic Images
- 2021
-
Ingår i: Proceedings of the International Joint Conference on Neural Networks. - : IEEE. - 9780738133669 - 9781665439008 - 9781665445979
-
Konferensbidrag (refereegranskat)abstract
- With the evolution of deep learning in the past decade, more biomedical related problems that seemed strenuous, are now feasible. The introduction of U-net and Mask R-CNN architectures has paved a way for many object detection and segmentation tasks in numerous applications ranging from security to biomedical applications. In the cell biology domain, light microscopy imaging provides a cheap and accessible source of raw data to study biological phenomena. By leveraging such data and deep learning techniques, human diseases can be easily diagnosed and the process of treatment development can be greatly expedited. In microscopic imaging, accurate segmentation of individual cells is a crucial step to allow better insight into cellular heterogeneity. To address the aforementioned challenges, DeepCeNS is proposed in this paper to detect and segment cells and nucleus in microscopic images. We have used EVICAN2 dataset which contains microscopic images from a variety of microscopes having numerous cell cultures, to evaluate the proposed pipeline. DeepCeNS outperforms EVICAN-MRCNN by a significant margin on the EVICAN2 dataset.
|
|
