Semi-empirical Analysis of Complex ITC Data from Protein-Surfactant Interactions.

Isothermal titration calorimetry (ITC) is a widely used method to determine binding affinities and thermodynamics in ligand-receptor interactions, but it also has the capability of providing detailed information on much more complex events. However, the lack of available methods to analyze ITC data is limiting the use of the technique in such multifaceted cases. Here, we present the software ANISPROU. Through a semi-empirical approach that allows for extraction of quantitative information from complex ITC data, ANISPROU solves an inverse problem where three parameters describing a set of predefined functions must be found. In analogy to strategies adopted in other scientific fields, such as geophysics, imaging, and many others, it employs an optimization algorithm which minimizes the difference between calculated and experimental data. In contrast to the existing methods, ANISPROU provides automated and objective analysis of ITC data on sodium dodecyl sulfate (SDS)-induced protein unfolding, and in addition, more information can be extracted from the data. Here, data series on SDS-mediated protein unfolding is analyzed, and binding isotherms and thermodynamic information on the unfolding events are extracted. The obtained binding isotherms as well as the enthalpy of different events are similar to those obtained using the existing manual methods, but our methodology ensures a more robust result, as the entire data set is used instead of single data points. We foresee that ANISPROU will be useful in other cases with complex enthalpograms, for example, in cases with coupled interactions in biomolecular, polymeric, and amphiphilic systems including cases where both structural changes and interactions occur simultaneously.

Compressive beamforming.

Sound source localization with sensor arrays involves the estimation of the direction-of-arrival (DOA) from a limited number of observations. Compressive sensing (CS) solves such underdetermined problems achieving sparsity, thus improved resolution, and can be solved efficiently with convex optimization. The DOA estimation problem is formulated in the CS framework and it is shown that CS has superior performance compared to traditional DOA estimation methods especially under challenging scenarios such as coherent arrivals and single-snapshot data. An offset and resolution analysis is performed to indicate the limitations of CS. It is shown that the limitations are related to the beampattern, thus can be predicted. The high-resolution capabilities and the robustness of CS are demonstrated on experimental array data from ocean acoustic measurements for source tracking with single-snapshot data.

Modeling and detection of oil in sea water.

The challenge of a deep-water oil leak is that a significant quantity of oil remains in the water column and possibly changes properties. There is a need to quantify the oil settled within the water column and determine its physical properties to assist in the oil recovery. There are currently no methods to map acoustically submerged oil in the sea. In this paper, high-frequency acoustic methods are proposed to localize the oil polluted area and characterize the parameters of its spatial covariance, i.e., variance and correlation. A model is implemented to study the underlying mechanisms of backscattering due to spatial heterogeneity of the medium and predict backscattering returns. An algorithm for synthetically generating stationary, Gaussian random fields is introduced which provides great flexibility in implementing the physical model of an inhomogeneous field with spatial covariance. A method for inference of spatial covariance parameters is proposed to describe the scattering field in terms of its second-order statistics from the backscattered returns. The results indicate that high-frequency acoustic methods not only are suitable for large-scale detection of oil contamination in the water column but also allow inference of the spatial covariance parameters resulting in a statistical description of the oil field.

Heat flux anomalies in Antarctica revealed by satellite magnetic data.

The geothermal heat flux is an important factor in the dynamics of ice sheets; it affects the occurrence of subglacial lakes, the onset of ice streams, and mass losses from the ice sheet base. Because direct heat flux measurements in ice-covered regions are difficult to obtain, we developed a method that uses satellite magnetic data to estimate the heat flux underneath the Antarctic ice sheet. We found that the heat flux underneath the ice sheet varies from 40 to 185 megawatts per square meter and that areas of high heat flux coincide with known current volcanism and some areas known to have ice streams.