21,724 research outputs found
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 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 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 varies with the distance to the
quantum critical point QCP as, where the shift
exponent . In the paramagnetic side of the phase diagram, the
spin gap behaves as for consistent with
the value found for the dynamical critical exponent. We also find in this
region a power law temperature dependence in the specific heat for
and along the non-Fermi liquid trajectory. For , 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
- …