Terahertz Spectral Domain Computational Analysis of Hydration Shell of Proteins with Increasingly Complex Tertiary Structure

Nanoantenna enhanced terahertz interaction of biomolecules

Terahertz time-domain spectroscopy (THz-TDS) is a non-invasive, non-contact and label-free technique for biological and chemical sensing as THz-spectra are less energetic and lie in the characteristic vibration frequency regime of proteins and DNA molecules. However, THz-TDS is less sensitive for the detection of micro-organisms of size equal to or less than λ/100 (where, λ is the wavelength of the incident THz wave), and molecules in extremely low concentration solutions (like, a few femtomolar). After successful high-throughput fabrication of nanostructures, nanoantennas were found to be indispensable in enhancing the sensitivity of conventional THz-TDS. These nanostructures lead to strong THz field enhancement when in resonance with the absorption spectrum of absorptive molecules, causing significant changes in the magnitude of the transmission spectrum, therefore, enhancing the sensitivity and allowing the detection of molecules and biomaterials in extremely low concentration solutions. Herein, we review the recent developments in ultra-sensitive and selective nanogap biosensors. We have also provided an in-depth review of various high-throughput nanofabrication techniques. We also discussed the physics behind the field enhancements in the sub-skin depth as well as sub-nanometer sized nanogaps. We introduce finite-difference time-domain (FDTD) and molecular dynamics (MD) simulation tools to study THz biomolecular interactions. Finally, we provide a comprehensive account of nanoantenna enhanced sensing of viruses (like, H1N1) and biomolecules such as artificial sweeteners which are addictive and carcinogenic.

Calculated Terahertz Spectra of Glycine Oligopeptide Solutions Confined in Carbon Nanotubes

To reduce the intense terahertz (THz) wave absorption of water and increase the signal-to-noise ratio, the THz spectroscopy detection of biomolecules usually operates using the nanofluidic channel technologies in practice. The effects of confinement due to the existence of nanofluidic channels on the conformation and dynamics of biomolecules are well known. However, studies of confinement effects on the THz spectra of biomolecules are still not clear. In this work, extensive all-atom molecular dynamics simulations are performed to investigate the THz spectra of the glycine oligopeptide solutions in free and confined environments. THz spectra of the oligopeptide solutions confined in carbon nanotubes (CNTs) with different radii are calculated and compared. Results indicate that with the increase of the degree of confinement (the reverse of the radius of CNT), the THz absorption coefficient decreases monotonically. By analyzing the diffusion coefficient and dielectric relaxation dynamics, the hydrogen bond life, and the vibration density of the state of the water molecules in free solution and in CNTs, we conclude that the confinement effects on the THz spectra of biomolecule solutions are mainly to slow down the dynamics of water molecules and hence to reduce the THz absorption of the whole solution in confined environments.

Water Dynamics in the Hydration Shells of Biomolecules

The structure and function of biomolecules are strongly influenced by their hydration shells. Structural fluctuations and molecular excitations of hydrating water molecules cover a broad range in space and time, from individual water molecules to larger pools and from femtosecond to microsecond time scales. Recent progress in theory and molecular dynamics simulations as well as in ultrafast vibrational spectroscopy has led to new and detailed insight into fluctuations of water structure, elementary water motions, electric fields at hydrated biointerfaces, and processes of vibrational relaxation and energy dissipation. Here, we review recent advances in both theory and experiment, focusing on hydrated DNA, proteins, and phospholipids, and compare dynamics in the hydration shells to bulk water.

Exploring the complementarity of THz pulse imaging and DCE-MRIs: Toward a unified multi-channel classification and a deep learning framework

We provide a comprehensive account of recent advances in biomedical image analysis and classification from two complementary imaging modalities: terahertz (THz) pulse imaging and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The work aims to highlight underlining commonalities in both data structures so that a common multi-channel data fusion framework can be developed. Signal pre-processing in both datasets is discussed briefly taking into consideration advances in multi-resolution analysis and model based fractional order calculus system identification. Developments in statistical signal processing using principal component and independent component analysis are also considered. These algorithms have been developed independently by the THz-pulse imaging and DCE-MRI communities, and there is scope to place them in a common multi-channel framework to provide better software standardization at the pre-processing de-noising stage. A comprehensive discussion of feature selection strategies is also provided and the importance of preserving textural information is highlighted. Feature extraction and classification methods taking into consideration recent advances in support vector machine (SVM) and extreme learning machine (ELM) classifiers and their complex extensions are presented. An outlook on Clifford algebra classifiers and deep learning techniques suitable to both types of datasets is also provided. The work points toward the direction of developing a new unified multi-channel signal processing framework for biomedical image analysis that will explore synergies from both sensing modalities for inferring disease proliferation.

Pattern identification of biomedical images with time series: Contrasting THz pulse imaging with DCE-MRIs

OBJECTIVE

We provide a survey of recent advances in biomedical image analysis and classification from emergent imaging modalities such as terahertz (THz) pulse imaging (TPI) and dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) and identification of their underlining commonalities.

METHODS

Both time and frequency domain signal pre-processing techniques are considered: noise removal, spectral analysis, principal component analysis (PCA) and wavelet transforms. Feature extraction and classification methods based on feature vectors using the above processing techniques are reviewed. A tensorial signal processing de-noising framework suitable for spatiotemporal association between features in MRI is also discussed.

VALIDATION

Examples where the proposed methodologies have been successful in classifying TPIs and DCE-MRIs are discussed.

RESULTS

Identifying commonalities in the structure of such heterogeneous datasets potentially leads to a unified multi-channel signal processing framework for biomedical image analysis.

CONCLUSION

The proposed complex valued classification methodology enables fusion of entire datasets from a sequence of spatial images taken at different time stamps; this is of interest from the viewpoint of inferring disease proliferation. The approach is also of interest for other emergent multi-channel biomedical imaging modalities and of relevance across the biomedical signal processing community.