Browse
Submit Data
Databases
API
Help

Dataset Information

29 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Estimation of nonclassical independent Gaussian processes by classical interferometry.

ABSTRACT: We propose classical interferometry with low-intensity thermal radiation for the estimation of nonclassical independent Gaussian processes in material samples. We generally determine the mean square error of the phase-independent parameters of an unknown Gaussian process, considering a noisy source of radiation the phase of which is not locked to the pump of the process. We verify the sufficiency of passive optical elements in the interferometer, active optical elements do not improve the quality of the estimation. We also prove the robustness of the method against the noise and loss in both interferometric channels and the sample. The proposed method is suitable even for the case when a source of radiation sufficient for homodyne detection is not available.

SUBMITTER: Ruppert L

PROVIDER: S-EPMC5209653 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Estimation of nonclassical independent Gaussian processes by classical interferometry.

Ruppert László L Filip Radim R

Scientific reports 20170104

We propose classical interferometry with low-intensity thermal radiation for the estimation of nonclassical independent Gaussian processes in material samples. We generally determine the mean square error of the phase-independent parameters of an unknown Gaussian process, considering a noisy source of radiation the phase of which is not locked to the pump of the process. We verify the sufficiency of passive optical elements in the interferometer, active optical elements do not improve the qualit ...[more]

PMID: 28051094

Similar Datasets

A classical view on nonclassical nucleation.

Project description:Understanding and controlling nucleation is important for many crystallization applications. Calcium carbonate (CaCO3) is often used as a model system to investigate nucleation mechanisms. Despite its great importance in geology, biology, and many industrial applications, CaCO3 nucleation is still a topic of intense discussion, with new pathways for its growth from ions in solution proposed in recent years. These new pathways include the so-called nonclassical nucleation mechanism via the assembly of thermodynamically stable prenucleation clusters, as well as the formation of a dense liquid precursor phase via liquid-liquid phase separation. Here, we present results from a combined experimental and computational investigation on the precipitation of CaCO3 in dilute aqueous solutions. We propose that a dense liquid phase (containing 4-7 H2O per CaCO3 unit) forms in supersaturated solutions through the association of ions and ion pairs without significant participation of larger ion clusters. This liquid acts as the precursor for the formation of solid CaCO3 in the form of vaterite, which grows via a net transfer of ions from solution according to z Ca2+ + z CO32- ? z CaCO3 The results show that all steps in this process can be explained according to classical concepts of crystal nucleation and growth, and that long-standing physical concepts of nucleation can describe multistep, multiphase growth mechanisms.

| S-EPMC5617248 | biostudies-literature

Multiple Matrix Gaussian Graphs Estimation.

Project description:Matrix-valued data, where the sampling unit is a matrix consisting of rows and columns of measurements, are emerging in numerous scientific and business applications. Matrix Gaussian graphical model is a useful tool to characterize the conditional dependence structure of rows and columns. In this article, we employ nonconvex penalization to tackle the estimation of multiple graphs from matrix-valued data under a matrix normal distribution. We propose a highly efficient nonconvex optimization algorithm that can scale up for graphs with hundreds of nodes. We establish the asymptotic properties of the estimator, which requires less stringent conditions and has a sharper probability error bound than existing results. We demonstrate the efficacy of our proposed method through both simulations and real functional magnetic resonance imaging analyses.

| S-EPMC6261498 | biostudies-literature

Characterization of Classical and Nonclassical Fabry Disease: A Multicenter Study.

Project description:Fabry disease leads to renal, cardiac, and cerebrovascular manifestations. Phenotypic differences between classically and nonclassically affected patients are evident, but there are few data on the natural course of classical and nonclassical disease in men and women. To describe the natural course of Fabry disease stratified by sex and phenotype, we retrospectively assessed event-free survival from birth to the first clinical visit (before enzyme replacement therapy) in 499 adult patients (mean age 43 years old; 41% men; 57% with the classical phenotype) from three international centers of excellence. We classified patients by phenotype on the basis of characteristic symptoms and enzyme activity. Men and women with classical Fabry disease had higher event rate than did those with nonclassical disease (hazard ratio for men, 5.63, 95% confidence interval, 3.17 to 10.00; P<0.001; hazard ratio for women, 2.88, 95% confidence interval, 1.54 to 5.40; P<0.001). Furthermore, men with classical Fabry disease had lower eGFR, higher left ventricular mass, and higher plasma globotriaosylsphingosine concentrations than men with nonclassical Fabry disease or women with either phenotype (P<0.001). In conclusion, before treatment with enzyme replacement therapy, men with classical Fabry disease had a history of more events than men with nonclassical disease or women with either phenotype; women with classical Fabry disease were more likely to develop complications than women with nonclassical disease. These data may support the development of new guidelines for the monitoring and treatment of Fabry disease and studies on the effects of intervention in subgroups of patients.

| S-EPMC5407735 | biostudies-literature

Motor Cortex Mapping using Active Gaussian Processes.

Project description:One important application of transcranial magnetic stimulation (TMS) is to map cortical motor topography by spatially sampling the motor cortex, and recording motor evoked potentials (MEP) with surface electromyography. Standard approaches to TMS mapping involve repetitive stimulations at different loci spaced on a (typically 1 cm) grid on the scalp. These mappings strategies are time consuming and responsive sites are typically sparse. Furthermore, the long time scale prevents measurement of transient cortical changes, and is poorly tolerated in clinical populations. An alternative approach involves using the TMS mapper expertise to exploit the map's sparsity through the use of feedback of MEPs to decide which loci to stimulate. In this investigation, we propose a novel active learning method to automatically infer optimal future stimulus loci in place of user expertise. Specifically, we propose an active Gaussian Process (GP) strategy with loci selection criteria such as entropy and mutual information (MI). The proposed method twists the usual entropy- and MI-based selection criteria by modeling the estimated MEP field, i.e., the GP mean, as a Gaussian random variable itself. By doing so, we include MEP amplitudes in the loci selection criteria which would be otherwise completely independent of the MEP values. Experimental results using real data shows that the proposed strategy can greatly outperform competing methods when the MEP variations are mostly conned in a sub-region of the space.

| S-EPMC7433704 | biostudies-literature

Improving the remote estimation of soil organic carbon in complex ecosystems with Sentinel-2 and GIS using Gaussian processes regression.

Project description:Background and aimsThe quantitative retrieval of soil organic carbon (SOC) storage, particularly for soils with a large potential for carbon sequestration, is of global interest due to its link with the carbon cycle and the mitigation of climate change. However, complex ecosystems with good soil qualities for SOC storage are poorly studied.MethodsThe interrelation between SOC and various vegetation remote sensing drivers is understood to demonstrate the link between the carbon stored in the vegetation layer and SOC of the top soil layers. Based on the mapping of SOC in two horizons (0-30 cm and 30-60 cm) we predict SOC with high accuracy in the complex and mountainous heterogeneous páramo system in Ecuador. A large SOC database (in weight % and in Mg/ha) of 493 and 494 SOC sampling data points from 0-30 cm and 30-60 cm soil profiles, respectively, were used to calibrate GPR models using Sentinel-2 and GIS predictors (i.e., Temperature, Elevation, Soil Taxonomy, Geological Unit, Slope Length and Steepness (LS Factor), Orientation and Precipitation).ResultsIn the 0-30 cm soil profile, the models achieved a R2 of 0.85 (SOC%) and a R2 of 0.79 (SOC Mg/ha). In the 30-60 cm soil profile, models achieved a R2 of 0.86 (SOC%), and a R2 of 0.79 (SOC Mg/ha).ConclusionsThe used Sentinel-2 variables (FVC, CWC, LCC/Cab, band 5 (705 nm) and SeLI index) were able to improve the estimation accuracy between 3-21% compared to previous results of the same study area. CWC emerged as the most relevant biophysical variable for SOC prediction.Supplementary informationThe online version contains supplementary material available at 10.1007/s11104-022-05506-1.

| S-EPMC7613806 | biostudies-literature

Functionalized Multiwalled CNTs in Classical and Nonclassical CaCO<sub>3</sub> Crystallization.

Project description:Multiwalled carbon nanotubes (MWCNTs) are interesting high-tech nanomaterials. MWCNTs oxidized and functionalized with itaconic acid and monomethylitaconate were demonstrated to be efficient additives for controlling nucleation of calcium carbonate (CaCO3) via gas diffusion (GD) in classical as well as nonclassical crystallization, yielding aragonite and truncated calcite. For the first time, all amorphous calcium carbonate (ACC) proto-structures, such as proto calcite-ACC, proto vaterite-ACC and proto aragonite-ACC, were synthesized via prenucleation cluster (PNC) intermediates and stabilized at room temperature. The MWCNTs also showed concentration-dependent nucleation promotion and inhibition similar to biomolecules in nature. Incorporation of fluorescein-5-thiosemicarbazide (5-FTSC) dye-labeled MWCNTs into the CaCO3 lattice resulted in fluorescent hybrid nanosized CaCO3. We demonstrate that functionalized MWCNTs offer a good alternative for controlled selective crystallization and for understanding an inorganic mineralization process.

| S-EPMC6723705 | biostudies-literature

Solid-to-Solid Crystallization of Organic Thin Films: Classical and Nonclassical Pathways.

Project description:The solid-to-solid crystallization processes of organic molecules have been poorly understood in view of the complexity and the instability of organic crystals. Here, we studied the crystallization of a π-conjugated small molecular semiconductor, bis-(8-hydroxyquinoline) copper (CuQ2), by annealing the thin films at different temperatures. We observed a classical film-to-nanorods crystallization at 80 °C, a coexistence of classical and nonclassical nucleation and particle growth at 120 °C, and a nonclassical crystal growth at 150 °C. We found that the growth of the crystals followed the following processes: particle nucleation, particle growth, particle migration, nondirectional particle attachment, and structure reconstruction. We notice that the growth of CuQ2 particles follows an outside-to-inside process. More interestingly, our experiments suggest that the submicron CuQ2 particles are able to migrate dozens of micrometers at 150 °C.

| S-EPMC6711356 | biostudies-literature

Efficient algorithms for Bayesian Nearest Neighbor Gaussian Processes.

Project description:We consider alternate formulations of recently proposed hierarchical Nearest Neighbor Gaussian Process (NNGP) models (Datta et al., 2016a) for improved convergence, faster computing time, and more robust and reproducible Bayesian inference. Algorithms are defined that improve CPU memory management and exploit existing high-performance numerical linear algebra libraries. Computational and inferential benefits are assessed for alternate NNGP specifications using simulated datasets and remotely sensed light detection and ranging (LiDAR) data collected over the US Forest Service Tanana Inventory Unit (TIU) in a remote portion of Interior Alaska. The resulting data product is the first statistically robust map of forest canopy for the TIU.

| S-EPMC6753955 | biostudies-literature

Navigating the protein fitness landscape with Gaussian processes.

Project description:Knowing how protein sequence maps to function (the "fitness landscape") is critical for understanding protein evolution as well as for engineering proteins with new and useful properties. We demonstrate that the protein fitness landscape can be inferred from experimental data, using Gaussian processes, a Bayesian learning technique. Gaussian process landscapes can model various protein sequence properties, including functional status, thermostability, enzyme activity, and ligand binding affinity. Trained on experimental data, these models achieve unrivaled quantitative accuracy. Furthermore, the explicit representation of model uncertainty allows for efficient searches through the vast space of possible sequences. We develop and test two protein sequence design algorithms motivated by Bayesian decision theory. The first one identifies small sets of sequences that are informative about the landscape; the second one identifies optimized sequences by iteratively improving the Gaussian process model in regions of the landscape that are predicted to be optimized. We demonstrate the ability of Gaussian processes to guide the search through protein sequence space by designing, constructing, and testing chimeric cytochrome P450s. These algorithms allowed us to engineer active P450 enzymes that are more thermostable than any previously made by chimeragenesis, rational design, or directed evolution.

| S-EPMC3549130 | biostudies-literature

Experimental measurement of the diamond nucleation landscape reveals classical and nonclassical features.

Project description:Nucleation is a core scientific concept that describes the formation of new phases and materials. While classical nucleation theory is applied across wide-ranging fields, nucleation energy landscapes have never been directly measured at the atomic level, and experiments suggest that nucleation rates often greatly exceed the predictions of classical nucleation theory. Multistep nucleation via metastable states could explain unexpectedly rapid nucleation in many contexts, yet experimental energy landscapes supporting such mechanisms are scarce, particularly at nanoscale dimensions. In this work, we measured the nucleation energy landscape of diamond during chemical vapor deposition, using a series of diamondoid molecules as atomically defined protonuclei. We find that 26-carbon atom clusters, which do not contain a single bulk atom, are postcritical nuclei and measure the nucleation barrier to be more than four orders of magnitude smaller than prior bulk estimations. These data support both classical and nonclassical concepts for multistep nucleation and growth during the gas-phase synthesis of diamond and other semiconductors. More broadly, these measurements provide experimental evidence that agrees with recent conceptual proposals of multistep nucleation pathways with metastable molecular precursors in diverse processes, ranging from cloud formation to protein crystallization, and nanoparticle synthesis.

| S-EPMC6099900 | biostudies-other

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