PURPOSE: Water removal is one of the computational bottlenecks in the processing of high-resolution MRSI data. The purpose of this work is to propose an approach to reduce the computing time required for water removal in large MRS data. METHODS: In this work, we describe a singular value decomposition-based approach that uses the partial position-time separability and the time-domain linear predictability of MRSI data to reduce the computational time required for water removal. Our approach arranges MRS signals in a Casorati matrix form, applies low-rank approximations utilizing singular value decomposition, removes residual water from the most prominent left-singular vectors, and finally reconstructs the water-free matrix using the processed left-singular vectors. RESULTS: We have demonstrated the effectiveness of our proposed algorithm for water removal using both simulated and in vivo data. The proposed algorithm encompasses a pip-installable tool ( https://pypi.org/project/CSVD/), available on GitHub ( https://github.com/amirshamaei/CSVD), empowering researchers to use it in future studies. Additionally, to further promote transparency and reproducibility, we provide comprehensive code for result replication. CONCLUSIONS: The findings of this study suggest that the proposed method is a promising alternative to existing water removal methods due to its low processing time and good performance in removing water signals.
This paper describes a comparative systems level analysis of the developmental proteome and transcriptome in the model antibiotic-producing eubacterium Streptomyces coelicolor, cultured on different media. The analysis formulates expression as the superposition of effects of regulatory networks and biological processes which can be identified using singular value decomposition (SVD) of a data matrix formed by time series measurements of expression of individual genes throughout the cell cycle of the bacterium. SVD produces linearly orthogonal factors, each of which can represent an independent system behavior defined by a linear combination of the genes/proteins highly correlated with the corresponding factor. By using SVD of the developmental time series of gene expression, as measured by both protein and RNA levels, we show that on the highest level of control (representing the basic kinetic behavior of the population), the results are identical, regardless of the type of experiment or cultivation method. The results show that this approach is capable of identifying basic regulatory processes independent of the environment in which the organism lives. It also shows that these processes are manifested equally on protein and RNA levels. Biological interpretation of the correlation of the genes and proteins with significant eigenprofiles (representing the highest level kinetic behavior of protein and/or RNA synthesis) revealed their association with metabolic processes, stress responses, starvation, and secondary metabolite production.
- MeSH
- Bacterial Proteins genetics metabolism MeSH
- RNA, Bacterial genetics metabolism MeSH
- Citric Acid Cycle MeSH
- Financing, Organized MeSH
- Data Interpretation, Statistical MeSH
- RNA, Messenger genetics metabolism MeSH
- Heat-Shock Proteins genetics metabolism MeSH
- Proteomics methods statistics & numerical data MeSH
- Gene Expression Profiling methods statistics & numerical data MeSH
- Streptomyces genetics metabolism growth & development MeSH
- Systems Biology MeSH
- Publication type
- Comparative Study MeSH
Závěrečná zpráva o řešení grantu Interní grantové agentury MZ ČR
Přeruš. str. ; 32 cm
Cílem projektu je posouzení významu elektrokard. mapování, resp. analýzy získaných map pomocí rozkladu na singulární hodnoty, pro předpověď indukovatelnosti maligních komorových arytmií u nemocných po IM ve srovnání dalšími vybranými strat. metodami.; The research project is focused on a study of body surface mapping and singular value decomposition analysis of electrocardiographic maps in particular in order to predict inducibility of malignant ventricular arrhythmias in survivors of m. infarction.
- MeSH
- Baroreflex MeSH
- Defibrillators, Implantable contraindications utilization MeSH
- Diagnostic Techniques, Cardiovascular utilization MeSH
- Electrocardiography utilization methods MeSH
- Myocardial Infarction diagnosis MeSH
- Tachycardia, Ventricular diagnosis MeSH
- Patient Selection MeSH
- Conspectus
- Patologie. Klinická medicína
- NML Fields
- kardiologie
- angiologie
- chemie, klinická chemie
- NML Publication type
- závěrečné zprávy o řešení grantu IGA MZ ČR
Závěrečná zpráva o řešení grantu Agentury pro zdravotnický výzkum MZ ČR
Nestr.
Nealkoholická steatóza jater (NAFLD) je nejčastější chronické onemocnění jater v západních civilizacích (prevalence ~30%). Onemocnění často přechází do steatohepatitidy (NASH) a v některých případech vede k cirhóze a hepatocelulárnímu karcinomu. Steatóza je výsledkem porušení rovnováhy mezi přijatými, katabolizovanými nebo odstraňovanými triglyceridy v játrech. Naše studie je založena na 1H MR lokalizované spektroskopii a dvou MRI technikách, které budou testovány a použity pro porovnání obsahu tuku v játrech: metodou “singular value decomposition matrix pencil method” a standardní modifikací multi echo sekvence. Abychom charakterizovali dynamiku akumulace tuků v játrech, budou za pečlivě kontrolovaných metabolických podmínek provedeny experimenty po požití nadměrného množství tuku. V těch využijeme toho, že dostatečně vysoká tuková zátěž se projeví detekovatelným přírůstkem množství jaterního tuku. Změny obsahu a prostorového rozložení tuku v játrech budou měřeny různými in vivo MR metodami.; Non-alcoholic fatty acid liver disease (NAFLD, steatosis) is the most frequent chronic liver disease in western countries (prevalence ~30%). The disease often progresses to steatohepatitis (NASH) and further even to cirrhosis and hepatocellular carcinoma. Steatosis results from an imbalance between the acquired, catabolized or removed triglycerides in the liver. Our study is based on 1H MR single voxel spectroscopy and two MRI techniques which will be tested and used for the comparison of fat content in the liver: a singular value decomposition matrix pencil method and standard modification of multi-echo sequence. To understand the dynamics of lipid accumulation in the liver, experiments using overload of dietary fat will be carried out under well controlled metabolic conditions. These experiments take advantage of the fact that sufficiently high load of fat results in detectable increment of hepatic fat. Changes in content and spatial distribution of hepatic fat will be measured in vivo by different MR techniques.
- MeSH
- Liver metabolism MeSH
- Humans MeSH
- Magnetic Resonance Spectroscopy MeSH
- Magnetic Resonance Imaging MeSH
- Monitoring, Physiologic MeSH
- Non-alcoholic Fatty Liver Disease diagnostic imaging MeSH
- Triglycerides metabolism MeSH
- Adipose Tissue metabolism MeSH
- Check Tag
- Humans MeSH
- Conspectus
- Patologie. Klinická medicína
- NML Fields
- hepatologie
- NML Publication type
- závěrečné zprávy o řešení grantu AZV MZ ČR
... Systems of Equations 56 -- 2.5 Iterative Improvement of a Solution to Linear Equations 61 -- 2.6 Singular ... ... Value Decomposition 65 -- 2.7 Sparse Linear Systems 75 -- 2.8 Vandermonde Matrices and Toeplitz Matrices ... ... 93 -- 2.9 Cholesky Decomposition 100 -- 2.10 Decomposition 102 -- 2.11 Is Matrix Inversion an V3 Process ... ... Problems 1031 -- 20.2 Diffusive Initial Value Problems 1043 -- 20.3 Initial Value Problems in Multidimensions ... ... Methods for Boundary Value Problems 1059 -- 20.6 Multigrid Methods for Boundary Value Problems 1066 ...
3rd ed. xxi, 1235 s. : il. ; 27 cm + 1 CD-ROM
- MeSH
- Mathematical Computing MeSH
- Mathematics MeSH
- Numerical Analysis, Computer-Assisted * MeSH
- Publication type
- Monograph MeSH
- Conspectus
- Počítačová věda. Výpočetní technika. Informační technologie
- NML Fields
- přírodní vědy
- přírodní vědy
This paper explores regularization options for the ill-posed spline coefficient equations in the realistic Laplacian computation. We investigate the use of the Tikhonov regularization, truncated singular value decomposition, and the so-called lambda-correction with the regularization parameter chosen by the L-curve, generalized cross-validation, quasi-optimality, and the discrepancy principle criteria. The provided range of regularization techniques is much wider than in the previous works. The improvement of the realistic Laplacian is investigated by simulations on the three-shell spherical head model. The conclusion is that the best performance is provided by the combination of the Tikhonov regularization and the generalized cross-validation criterion-a combination that has never been suggested for this task before.
This paper reports on the structure of the autonomous station for monitoring artificial gamma activity in surface water bodies for the purposes of emergency preparedness of the Czech Republic. A simple design based on the NaI(Tl) submersible detector powered by a combined solar and wind source has been employed. Data transfer is provided by a satellite connection. The detection capabilities of the device have been tested for various unfavourable conditions, and the detection limits have been lowered by using the noise adjustment singular value decomposition (NASVD) method. The detection capabilities of the device fulfil the legal requirements for emergency monitoring, and are almost equal to the detection capabilities of other available devices with a more complicated and less versatile structure.
INTRODUCTION: Recent advances in machine learning provide new possibilities to process and analyse observational patient data to predict patient outcomes. In this paper, we introduce a data processing pipeline for cardiogenic shock (CS) prediction from the MIMIC III database of intensive cardiac care unit patients with acute coronary syndrome. The ability to identify high-risk patients could possibly allow taking pre-emptive measures and thus prevent the development of CS. METHODS: We mainly focus on techniques for the imputation of missing data by generating a pipeline for imputation and comparing the performance of various multivariate imputation algorithms, including k-nearest neighbours, two singular value decomposition (SVD)-based methods, and Multiple Imputation by Chained Equations. After imputation, we select the final subjects and variables from the imputed dataset and showcase the performance of the gradient-boosted framework that uses a tree-based classifier for cardiogenic shock prediction. RESULTS: We achieved good classification performance thanks to data cleaning and imputation (cross-validated mean area under the curve 0.805) without hyperparameter optimization. CONCLUSION: We believe our pre-processing pipeline would prove helpful also for other classification and regression experiments.
- Publication type
- Journal Article MeSH
