Determination of the Optimal Conditions for Bovine Serum Albumin Using Surface Enhanced Raman Scattering on Silver Colloids and Nanoparticle Films

Bovine serum albumin (BSA) was analyzed using surface enhanced Raman scattering (SERS) to find the optimal conditions to observe BSA with SERS on colloids and nanoparticles films. The optimal conditions were determined by varying the concentrations of colloidal silver, Na2SO4, and BSA. The most favorable conditions using SERS for BSA were 500ug/mL, pH 4, and 0.1 M Na2SO4. Under these conditions, peaks due to an alpha helical secondary structure by the amide 3 vibrations at 1297 cm-1 were most distinct. The peaks appearing in the spectrum are the parts of the BSA molecule which are interacting with the silver colloids. Colloidal metal films were made and soaked in varying pH of 500 µg/mL BSA solutions, the spectrum at pH 4 showed tyrosine peaks at 1566 and 1339 cm-1 and tryptophan peaks at 1158 and 1038 cm-1. The peaks on colloidal films only appear when the amino acid residue is perpendicular to the colloidal surface; therefore, one could deduce that the tyrosine molecules at 1566 and 1339 cm-1 and tryptophan molecules at 1158 and 1038 cm-1 are perpendicular to the film. SERS can be used for label-free detection of proteins, thus finding the best conditions to obtain spectra using this technique may be very beneficial to proteomic research

Prediction of protein-protein interactions using one-class classification methods and integrating diverse data

This research addresses the problem of prediction of protein-protein interactions (PPI) when integrating diverse kinds of biological information. This task has been commonly viewed as a binary classification problem (whether any two proteins do or do not interact) and several different machine learning techniques have been employed to solve this task. However the nature of the data creates two major problems which can affect results. These are firstly imbalanced class problems due to the number of positive examples (pairs of proteins which really interact) being much smaller than the number of negative ones. Secondly the selection of negative examples can be based on some unreliable assumptions which could introduce some bias in the classification results. Here we propose the use of one-class classification (OCC) methods to deal with the task of prediction of PPI. OCC methods utilise examples of just one class to generate a predictive model which consequently is independent of the kind of negative examples selected; additionally these approaches are known to cope with imbalanced class problems. We have designed and carried out a performance evaluation study of several OCC methods for this task, and have found that the Parzen density estimation approach outperforms the rest. We also undertook a comparative performance evaluation between the Parzen OCC method and several conventional learning techniques, considering different scenarios, for example varying the number of negative examples used for training purposes. We found that the Parzen OCC method in general performs competitively with traditional approaches and in many situations outperforms them. Finally we evaluated the ability of the Parzen OCC approach to predict new potential PPI targets, and validated these results by searching for biological evidence in the literature

Determination of the Optimal Conditions for Bovine Serum Albumin Surface Enhanced Raman Scattering on Silver Colloids

Bovine serum albumin (BSA) was analyzed using surface enhanced Raman scattering (SERS) to find the optimal conditions to observe BSA with SERS. Colloidal silver, Na2SO4, and BSA were mixed together at varying pHs and concentrations to obtain multiple spectra.The most favorable conditions using SERS for BSA were 500ug/mL and pH 4.The spectrum under those conditions showed the most intense and discernible peaks and the alpha helical secondary structure was very distinct at 1297 cm-1. SERS can be used for label free detection of proteins, thus finding the best conditions to obtain spectra using this technique may be very beneficial to proteomic research

Thermodynamic quantum critical behavior of the Kondo necklace model

We obtain the phase diagram and thermodynamic behavior of the Kondo necklace model for arbitrary dimensions $d$ using a representation for the localized and conduction electrons in terms of local Kondo singlet and triplet operators. A decoupling scheme on the double time Green's functions yields the dispersion relation for the excitations of the system. We show that in $d\geq 3$ there is an antiferromagnetically ordered state at finite temperatures terminating at a quantum critical point (QCP). In 2-d, long range magnetic order occurs only at T=0. The line of Neel transitions for $d>2$ varies with the distance to the quantum critical point QCP $|g|$ as, $T_N \propto |g|^{\psi}$ where the shift exponent $\psi=1/(d-1)$. In the paramagnetic side of the phase diagram, the spin gap behaves as $\Delta\approx \sqrt{|g|}$ for $d \ge 3$ consistent with the value $z=1$ found for the dynamical critical exponent. We also find in this region a power law temperature dependence in the specific heat for $k_BT\gg\Delta$ and along the non-Fermi liquid trajectory. For $k_BT \ll\Delta$, in the so-called Kondo spin liquid phase, the thermodynamic behavior is dominated by an exponential temperature dependence.Comment: Submitted to PR

Synchronization of Excitatory Neurons with Strongly Heterogeneous Phase Responses

In many real-world oscillator systems, the phase response curves are highly heterogeneous. However, dynamics of heterogeneous oscillator networks has not been seriously addressed. We propose a theoretical framework to analyze such a system by dealing explicitly with the heterogeneous phase response curves. We develop a novel method to solve the self-consistent equations for order parameters by using formal complex-valued phase variables, and apply our theory to networks of in vitro cortical neurons. We find a novel state transition that is not observed in previous oscillator network models.Comment: 4 pages, 3 figure