Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

MRAC Control with Prior Model Knowledge for Asymmetric Damaged Aircraft.

ABSTRACT: This paper develops a novel state-tracking multivariable model reference adaptive control (MRAC) technique utilizing prior knowledge of plant models to recover control performance of an asymmetric structural damaged aircraft. A modification of linear model representation is given. With prior knowledge on structural damage, a polytope linear parameter varying (LPV) model is derived to cover all concerned damage conditions. An MRAC method is developed for the polytope model, of which the stability and asymptotic error convergence are theoretically proved. The proposed technique reduces the number of parameters to be adapted and thus decreases computational cost and requires less input information. The method is validated by simulations on NASA generic transport model (GTM) with damage.

SUBMITTER: Xu X

PROVIDER: S-EPMC4477241 | biostudies-other | 2015

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Similar Datasets

Prior Knowledge Transfer Across Transcriptional Datasets Using Compositional Statistics

Project description:This SuperSeries is composed of the SubSeries listed below.

2016-11-08 | GSE73638 | GEO

Prior Knowledge Transfer Across Transcriptional Datasets Using Compositional Statistics [Tumor]

Project description:An expert-pathologist-reviewed epithelial ovarian cancer reference library (n = 50) used to assign the histopathology of epithelial ovarian cell lines using compositional statistics and random gene-sets

2016-11-08 | GSE73551 | GEO

Prior Knowledge Transfer Across Transcriptional Datasets Using Compositional Statistics [Cell lines]

Project description:Compositional statistics and random gene-sets were used to assign the tumor site of origin and histopathology of 18 epithelial ovarian cancer cell lines

2016-11-08 | GSE73637 | GEO

Boosting probabilistic graphical model inference by incorporating prior knowledge from multiple sources.

Project description:Inferring regulatory networks from experimental data via probabilistic graphical models is a popular framework to gain insights into biological systems. However, the inherent noise in experimental data coupled with a limited sample size reduces the performance of network reverse engineering. Prior knowledge from existing sources of biological information can address this low signal to noise problem by biasing the network inference towards biologically plausible network structures. Although integrating various sources of information is desirable, their heterogeneous nature makes this task challenging. We propose two computational methods to incorporate various information sources into a probabilistic consensus structure prior to be used in graphical model inference. Our first model, called Latent Factor Model (LFM), assumes a high degree of correlation among external information sources and reconstructs a hidden variable as a common source in a Bayesian manner. The second model, a Noisy-OR, picks up the strongest support for an interaction among information sources in a probabilistic fashion. Our extensive computational studies on KEGG signaling pathways as well as on gene expression data from breast cancer and yeast heat shock response reveal that both approaches can significantly enhance the reconstruction accuracy of Bayesian Networks compared to other competing methods as well as to the situation without any prior. Our framework allows for using diverse information sources, like pathway databases, GO terms and protein domain data, etc. and is flexible enough to integrate new sources, if available.

| S-EPMC3691143 | biostudies-literature

Asymmetric author-topic model for knowledge discovering of big data in toxicogenomics.

Project description:The advancement of high-throughput screening technologies facilitates the generation of massive amount of biological data, a big data phenomena in biomedical science. Yet, researchers still heavily rely on keyword search and/or literature review to navigate the databases and analyses are often done in rather small-scale. As a result, the rich information of a database has not been fully utilized, particularly for the information embedded in the interactive nature between data points that are largely ignored and buried. For the past 10 years, probabilistic topic modeling has been recognized as an effective machine learning algorithm to annotate the hidden thematic structure of massive collection of documents. The analogy between text corpus and large-scale genomic data enables the application of text mining tools, like probabilistic topic models, to explore hidden patterns of genomic data and to the extension of altered biological functions. In this paper, we developed a generalized probabilistic topic model to analyze a toxicogenomics dataset that consists of a large number of gene expression data from the rat livers treated with drugs in multiple dose and time-points. We discovered the hidden patterns in gene expression associated with the effect of doses and time-points of treatment. Finally, we illustrated the ability of our model to identify the evidence of potential reduction of animal use.

| S-EPMC4403303 | biostudies-literature

A model for cell population size control using asymmetric division.

Project description:In multicellular organisms one can find examples where a growing tissue divides up until some final fixed cell number. Asymmetric division plays a prevalent feature in tissue differentiation in these organisms, where the daughters of each asymmetric division inherit unequal amounts of a fate determining molecule and as a result follow different developmental fates. In some tissues the accumulation or decrease of cell cycle regulators acts as an intrinsic timing mechanism governing proliferation. Here we present a minimal model based on asymmetric division and dilution of a cell-cycle regulator that can generate any final population size that might be needed. We show that within the model there are a variety of growth mechanisms from linear to non-linear that can lead to the same final cell count. Interestingly, when we include noise at division we find that there are special final cell population sizes that can be generated with high confidence that are flanked by population sizes that are less robust to division noise. When we include further perturbations in the division process we find that these special populations can remain relatively stable and in some cases even improve in their fidelity.

| S-EPMC3764109 | biostudies-literature

The asymmetric segregation of damaged proteins is stem cell-type dependent.

Project description:Asymmetric segregation of damaged proteins (DPs) during mitosis has been linked in yeast and bacteria to the protection of one cell from aging. Recent evidence suggests that stem cells may use a similar mechanism; however, to date there is no in vivo evidence demonstrating this effect in healthy adult stem cells. We report that stem cells in larval (neuroblast) and adult (female germline and intestinal stem cell) Drosophila melanogaster asymmetrically segregate DPs, such as proteins with the difficult-to-degrade and age-associated 2,4-hydroxynonenal (HNE) modification. Surprisingly, of the cells analyzed only the intestinal stem cell protects itself by segregating HNE to differentiating progeny, whereas the neuroblast and germline stem cells retain HNE during division. This led us to suggest that chronological life span, and not cell type, determines the amount of DPs a cell receives during division. Furthermore, we reveal a role for both niche-dependent and -independent mechanisms of asymmetric DP division.

| S-EPMC3653353 | biostudies-literature

Incorporating prior knowledge into regularized regression.

Project description:MotivationAssociated with genomic features like gene expression, methylation and genotypes, used in statistical modeling of health outcomes, there is a rich set of meta-features like functional annotations, pathway information and knowledge from previous studies, that can be used post hoc to facilitate the interpretation of a model. However, using this meta-feature information a priori rather than post hoc can yield improved prediction performance as well as enhanced model interpretation.ResultsWe propose a new penalized regression approach that allows a priori integration of external meta-features. The method extends LASSO regression by incorporating individualized penalty parameters for each regression coefficient. The penalty parameters are, in turn, modeled as a log-linear function of the meta-features and are estimated from the data using an approximate empirical Bayes approach. Optimization of the marginal likelihood on which the empirical Bayes estimation is performed using a fast and stable majorization-minimization procedure. Through simulations, we show that the proposed regression with individualized penalties can outperform the standard LASSO in terms of both parameters estimation and prediction performance when the external data is informative. We further demonstrate our approach with applications to gene expression studies of bone density and breast cancer.Availability and implementationThe methods have been implemented in the R package xtune freely available for download from https://cran.r-project.org/web/packages/xtune/index.html.

| S-EPMC8599719 | biostudies-literature

Reconstruction of gene networks using prior knowledge.

Project description:Reconstructing gene regulatory networks (GRNs) from expression data is a challenging task that has become essential to the understanding of complex regulatory mechanisms in cells. The major issues are the usually very high ratio of number of genes to sample size, and the noise in the available data. Integrating biological prior knowledge to the learning process is a natural and promising way to partially compensate for the lack of reliable expression data and to increase the accuracy of network reconstruction algorithms.In this manuscript, we present PriorPC, a new algorithm based on the PC algorithm. PC algorithm is one of the most popular methods for Bayesian network reconstruction. The result of PC is known to depend on the order in which conditional independence tests are processed, especially for large networks. PriorPC uses prior knowledge to exclude unlikely edges from network estimation and introduces a particular ordering for the conditional independence tests. We show on synthetic data that the structural accuracy of networks obtained with PriorPC is greatly improved compared to PC.PriorPC improves structural accuracy of inferred gene networks by using soft priors which assign to edges a probability of existence. It is robust to false prior which is not avoidable in the context of biological data. PriorPC is also fast and scales well for large networks which is important for its applicability to real data.

| S-EPMC4654848 | biostudies-other

The development of autonomous unmanned aircraft systems for mosquito control.

Project description:We constructed an electric multi-rotor autonomous unmanned aerial system (UAS) to perform mosquito control activities. The UAS can be equipped with any of four modules for spraying larvicides, dropping larvicide tablets, spreading larvicide granules, and ultra-low volume spraying of adulticides. The larvicide module sprayed 124 μm drops at 591 mL/min over a 14 m swath for a total application rate of 1.6 L/ha. The tablet module was able to repeatedly deliver 40-gram larvicide tablets within 1.1 m of the target site. The granular spreader covered a 6 m swath and treated 0.76 ha in 13 min at an average rate of 1.8 kg/ha. The adulticide module produced 16 μm drops with an average deposition of 2.6 drops/mm2. UAS pesticide applications were made at rates prescribed for conventional aircraft, limited only by the payload capacity and flight time. Despite those limitations, this system can deliver pesticides with much greater precision than conventional aircraft, potentially reducing pesticide use. In smaller, congested environments or in programs with limited resources, UAS may be a preferable alternative to conventional aircraft.

| S-EPMC7500627 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data