Towards Identification of Relevant Variables in the observed Aerosol Optical Depth Bias between MODIS and AERONET observations

Measurements made by satellite remote sensing, Moderate Resolution Imaging Spectroradiometer (MODIS), and globally distributed Aerosol Robotic Network (AERONET) are compared. Comparison of the two datasets measurements for aerosol optical depth values show that there are biases between the two data products. In this paper, we present a general framework towards identifying relevant set of variables responsible for the observed bias. We present a general framework to identify the possible factors influencing the bias, which might be associated with the measurement conditions such as the solar and sensor zenith angles, the solar and sensor azimuth, scattering angles, and surface reflectivity at the various measured wavelengths, etc. Specifically, we performed analysis for remote sensing Aqua-Land data set, and used machine learning technique, neural network in this case, to perform multivariate regression between the ground-truth and the training data sets. Finally, we used mutual information between the observed and the predicted values as the measure of similarity to identify the most relevant set of variables. The search is brute force method as we have to consider all possible combinations. The computations involves a huge number crunching exercise, and we implemented it by writing a job-parallel program

Maximum Joint Entropy and Information-Based Collaboration of Automated Learning Machines

We are working to develop automated intelligent agents, which can act and react as learning machines with minimal human intervention. To accomplish this, an intelligent agent is viewed as a question-asking machine, which is designed by coupling the processes of inference and inquiry to form a model-based learning unit. In order to select maximally-informative queries, the intelligent agent needs to be able to compute the relevance of a question. This is accomplished by employing the inquiry calculus, which is dual to the probability calculus, and extends information theory by explicitly requiring context. Here, we consider the interaction between two question-asking intelligent agents, and note that there is a potential information redundancy with respect to the two questions that the agents may choose to pose. We show that the information redundancy is minimized by maximizing the joint entropy of the questions, which simultaneously maximizes the relevance of each question while minimizing the mutual information between them. Maximum joint entropy is therefore an important principle of information-based collaboration, which enables intelligent agents to efficiently learn together.Comment: 8 pages, 1 figure, to appear in the proceedings of MaxEnt 2011 held in Waterloo, Canad

The Spatial Sensitivity Function of a Light Sensor

The Spatial Sensitivity Function (SSF) is used to quantify a detector's sensitivity to a spatially-distributed input signal. By weighting the incoming signal with the SSF and integrating, the overall scalar response of the detector can be estimated. This project focuses on estimating the SSF of a light intensity sensor consisting of a photodiode. This light sensor has been used previously in the Knuth Cyberphysics Laboratory on a robotic arm that performs its own experiments to locate a white circle in a dark field (Knuth et al., 2007). To use the light sensor to learn about its surroundings, the robot's inference software must be able to model and predict the light sensor's response to a hypothesized stimulus. Previous models of the light sensor treated it as a point sensor and ignored its spatial characteristics. Here we propose a parametric approach where the SSF is described by a mixture of Gaussians (MOG). By performing controlled calibration experiments with known stimulus inputs, we used nested sampling to estimate the SSF of the light sensor using an MOG model with the number of Gaussians ranging from one to five. By comparing the evidence computed for each MOG model, we found that one Gaussian is sufficient to describe the SSF to the accuracy we require. Future work will involve incorporating this more accurate SSF into the Bayesian machine learning software for the robotic system and studying how this detailed information about the properties of the light sensor will improve robot's ability to learn.Comment: Published in MaxEnt 200

Homo-dimerization and ligand binding by the leucine-rich repeat domain at RHG1/RFS2 underlying resistance to two soybean pathogens

BACKGROUND: The protein encoded by GmRLK18-1 (Glyma_18_02680 on chromosome 18) was a receptor like kinase (RLK) encoded within the soybean (Glycine max L. Merr.) Rhg1/Rfs2 locus. The locus underlies resistance to the soybean cyst nematode (SCN) Heterodera glycines (I.) and causal agent of sudden death syndrome (SDS) Fusarium virguliforme (Aoki). Previously the leucine rich repeat (LRR) domain was expressed in Escherichia coli. RESULTS: The aims here were to evaluate the LRRs ability to; homo-dimerize; bind larger proteins; and bind to small peptides. Western analysis suggested homo-dimers could form after protein extraction from roots. The purified LRR domain, from residue 131–485, was seen to form a mixture of monomers and homo-dimers in vitro. Cross-linking experiments in vitro showed the H274N region was close (<11.1 A) to the highly conserved cysteine residue C196 on the second homo-dimer subunit. Binding constants of 20–142 nM for peptides found in plant and nematode secretions were found. Effects on plant phenotypes including wilting, stem bending and resistance to infection by SCN were observed when roots were treated with 50 pM of the peptides. Far-Western analyses followed by MS showed methionine synthase and cyclophilin bound strongly to the LRR domain. A second LRR from GmRLK08-1 (Glyma_08_g11350) did not show these strong interactions. CONCLUSIONS: The LRR domain of the GmRLK18-1 protein formed both a monomer and a homo-dimer. The LRR domain bound avidly to 4 different CLE peptides, a cyclophilin and a methionine synthase. The CLE peptides GmTGIF, GmCLE34, GmCLE3 and HgCLE were previously reported to be involved in root growth inhibition but here GmTGIF and HgCLE were shown to alter stem morphology and resistance to SCN. One of several models from homology and ab-initio modeling was partially validated by cross-linking. The effect of the 3 amino acid replacements present among RLK allotypes, A87V, Q115K and H274N were predicted to alter domain stability and function. Therefore, the LRR domain of GmRLK18-1 might underlie both root development and disease resistance in soybean and provide an avenue to develop new variants and ligands that might promote reduced losses to SCN

High spatial resolution imaging of methane and other trace gases with the airborne Hyperspectral Thermal Emission Spectrometer (HyTES)

Currently large uncertainties exist associated with the attribution and quantification of fugitive emissions of criteria pollutants and greenhouse gases such as methane across large regions and key economic sectors. In this study, data from the airborne Hyperspectral Thermal Emission Spectrometer (HyTES) have been used to develop robust and reliable techniques for the detection and wide-area mapping of emission plumes of methane and other atmospheric trace gas species over challenging and diverse environmental conditions with high spatial resolution that permits direct attribution to sources. HyTES is a pushbroom imaging spectrometer with high spectral resolution (256 bands from 7.5 to 12 µm), wide swath (1&ndash;2 km), and high spatial resolution (∼ 2 m at 1 km altitude) that incorporates new thermal infrared (TIR) remote sensing technologies. In this study we introduce a hybrid clutter matched filter (CMF) and plume dilation algorithm applied to HyTES observations to efficiently detect and characterize the spatial structures of individual plumes of CH4, H2S, NH3, NO2, and SO2 emitters. The sensitivity and field of regard of HyTES allows rapid and frequent airborne surveys of large areas including facilities not readily accessible from the surface. The HyTES CMF algorithm produces plume intensity images of methane and other gases from strong emission sources. The combination of high spatial resolution and multi-species imaging capability provides source attribution in complex environments. The CMF-based detection of strong emission sources over large areas is a fast and powerful tool needed to focus on more computationally intensive retrieval algorithms to quantify emissions with error estimates, and is useful for expediting mitigation efforts and addressing critical science questions

Stability analysis of the GAL regulatory network in Saccharomyces cerevisiae and Kluyveromyces lactis

<p>Abstract</p> <p>Background</p> <p>In the yeast <it>Saccharomyces cerevisiae</it>, interactions between galactose, Gal3p, Gal80p, and Gal4p determine the transcriptional status of the genes required for the galactose utilization. Increase in the cellular galactose concentration causes the galactose molecules to bind onto Gal3p which, via Gal80p, activates Gal4p, which induces the GAL3 and GAL80 gene transcription. Recently, a linear time-invariant multi-input multi-output (MIMO) model of this GAL regulatory network has been proposed; the inputs being galactose and Gal4p, and the outputs being the active Gal4p and galactose utilization. Unfortunately, this model assumes the cell culture to be homogeneous, although it is not so in practice. We overcome this drawback by including more biochemical reactions, and derive a quadratic ordinary differential equation (ODE) based model.</p> <p>Results</p> <p>We show that the model, referred to above, does not exhibit bistability. We establish sufficiency conditions for the domain of attraction of an equilibrium point of our ODE model for the special case of full-state feedback controller. We observe that the GAL regulatory system of <it>Kluyveromyces lactis </it>exhibits an aberration of monotone nonlinearity and apply the Rantzer multipliers to establish a class of stabilizing controllers for this system.</p> <p>Conclusion</p> <p>Feedback in a GAL regulatory system can be used to enhance the cellular memory. We show that the system can be modeled as a quadratic nonlinear system for which the effect of feedback on the domain of attraction of the equilibrium point can be characterized using <it>linear matrix inequality </it>(LMI) conditions that are easily implementable in software. The benefit of this result is that a mathematically sound approach to the synthesis of full-state and partial-state feedback controllers to regulate the cellular memory is now possible, irrespective of the number of state-variables or parameters of interest.</p

High spatial resolution imaging of methane and other trace gases with the airborne Hyperspectral Thermal Emission Spectrometer (HyTES)

Currently large uncertainties exist associated with the attribution and quantification of fugitive emissions of criteria pollutants and greenhouse gases such as methane across large regions and key economic sectors. In this study, data from the airborne Hyperspectral Thermal Emission Spectrometer (HyTES) have been used to develop robust and reliable techniques for the detection and wide-area mapping of emission plumes of methane and other atmospheric trace gas species over challenging and diverse environmental conditions with high spatial resolution that permits direct attribution to sources. HyTES is a pushbroom imaging spectrometer with high spectral resolution (256 bands from 7.5 to 12 µm), wide swath (1–2 km), and high spatial resolution (∼ 2 m at 1 km altitude) that incorporates new thermal infrared (TIR) remote sensing technologies. In this study we introduce a hybrid clutter matched filter (CMF) and plume dilation algorithm applied to HyTES observations to efficiently detect and characterize the spatial structures of individual plumes of CH_4, H_2S, NH_3, NO_2, and SO_2 emitters. The sensitivity and field of regard of HyTES allows rapid and frequent airborne surveys of large areas including facilities not readily accessible from the surface. The HyTES CMF algorithm produces plume intensity images of methane and other gases from strong emission sources. The combination of high spatial resolution and multi-species imaging capability provides source attribution in complex environments. The CMF-based detection of strong emission sources over large areas is a fast and powerful tool needed to focus on more computationally intensive retrieval algorithms to quantify emissions with error estimates, and is useful for expediting mitigation efforts and addressing critical science questions

Hypoxia-induced long non-coding RNA Malat1 is dispensable for renal ischemia/reperfusion-injury

Renal ischemia-reperfusion (I/R) injury is a major cause of acute kidney injury (AKI). Non-coding RNAs are crucially involved in its pathophysiology. We identified hypoxia-induced long non-coding RNA Malat1 (Metastasis Associated Lung Adenocarcinoma Transcript 1) to be upregulated in renal I/R injury. We here elucidated the functional role of Malat1 in vitro and its potential contribution to kidney injury in vivo. Malat1 was upregulated in kidney biopsies and plasma of patients with AKI, in murine hypoxic kidney tissue as well as in cultured and ex vivo sorted hypoxic endothelial cells and tubular epithelial cells. Malat1 was transcriptionally activated by hypoxia-inducible factor 1-a. In vitro, Malat1 inhibition reduced proliferation and the number of endothelial cells in the S-phase of the cell cycle. In vivo, Malat1 knockout and wildtype mice showed similar degrees of outer medullary tubular epithelial injury, proliferation, capillary rarefaction, inflammation and fibrosis, survival and kidney function. Small-RNA sequencing and whole genome expression analysis revealed only minor changes between ischemic Malat1 knockout and wildtype mice. Contrary to previous studies, which suggested a prominent role of Malat1 in the induction of disease, we did not confirm an in vivo role of Malat1 concerning renal I/Rinjury