Simple transformations capture auditory input to cortex.

Sounds are processed by the ear and central auditory pathway. These processing steps are biologically complex, and many aspects of the transformation from sound waveforms to cortical response remain unclear. To understand this transformation, we combined models of the auditory periphery with various encoding models to predict auditory cortical responses to natural sounds. The cochlear models ranged from detailed biophysical simulations of the cochlea and auditory nerve to simple spectrogram-like approximations of the information processing in these structures. For three different stimulus sets, we tested the capacity of these models to predict the time course of single-unit neural responses recorded in ferret primary auditory cortex. We found that simple models based on a log-spaced spectrogram with approximately logarithmic compression perform similarly to the best-performing biophysically detailed models of the auditory periphery, and more consistently well over diverse natural and synthetic sounds. Furthermore, we demonstrated that including approximations of the three categories of auditory nerve fiber in these simple models can substantially improve prediction, particularly when combined with a network encoding model. Our findings imply that the properties of the auditory periphery and central pathway may together result in a simpler than expected functional transformation from ear to cortex. Thus, much of the detailed biological complexity seen in the auditory periphery does not appear to be important for understanding the cortical representation of sound.

A dynamic network model of temporal receptive fields in primary auditory cortex.

Auditory neurons encode stimulus history, which is often modelled using a span of time-delays in a spectro-temporal receptive field (STRF). We propose an alternative model for the encoding of stimulus history, which we apply to extracellular recordings of neurons in the primary auditory cortex of anaesthetized ferrets. For a linear-non-linear STRF model (LN model) to achieve a high level of performance in predicting single unit neural responses to natural sounds in the primary auditory cortex, we found that it is necessary to include time delays going back at least 200 ms in the past. This is an unrealistic time span for biological delay lines. We therefore asked how much of this dependence on stimulus history can instead be explained by dynamical aspects of neurons. We constructed a neural-network model whose output is the weighted sum of units whose responses are determined by a dynamic firing-rate equation. The dynamic aspect performs low-pass filtering on each unit's response, providing an exponentially decaying memory whose time constant is individual to each unit. We find that this dynamic network (DNet) model, when fitted to the neural data using STRFs of only 25 ms duration, can achieve prediction performance on a held-out dataset comparable to the best performing LN model with STRFs of 200 ms duration. These findings suggest that integration due to the membrane time constants or other exponentially-decaying memory processes may underlie linear temporal receptive fields of neurons beyond 25 ms.

Two highly similar LAEDDTNAQKT and LTDKIGTEI epitopes in G glycoprotein may be useful for effective epitope based vaccine design against pathogenic Henipavirus.

Nipah virus and Hendra virus, two members of the genus Henipavirus, are newly emerging zoonotic pathogens which cause acute respiratory illness and severe encephalitis in human. Lack of the effective antiviral therapy endorses the urgency for the development of vaccine against these deadly viruses. In this study, we employed various computational approaches to identify epitopes which has the potential for vaccine development. By analyzing the immune parameters of the conserved sequences of G glycoprotein using various databases and bioinformatics tools, we identified two potential epitopes which may be used as peptide vaccines. Using different B cell epitope prediction servers, four highly similar B cell epitopes were identified. Immunoinformatics analyses revealed that LAEDDTNAQKT is a highly flexible and accessible B-cell epitope to antibody. Highly similar putative CTL epitopes were analyzed for their binding with the HLA-C 12*03 molecule. Docking simulation assay revealed that LTDKIGTEI has significantly lower binding energy, which bolstered its potential as epitope-based vaccine design. Finally, cytotoxicity analysis has also justified their potential as promising epitope-based vaccine candidate. In sum, our computational analysis indicates that either LAEDDTNAQKT or LTDKIGTEI epitope holds a promise for the development of universal vaccine against all kinds of pathogenic Henipavirus. Further in vivo and in vitro studies are necessary to validate the obtained findings.

Genome-wide Analysis of Mycoplasma hominis for the Identification of Putative Therapeutic Targets.

Ever increasing propensity of antibiotic resistance among pathogenic bacteria raises the demand for the development of novel therapeutic agents to control this grave problem. Advances in the field of bioinformatics, genomics, and proteomics have greatly facilitated the discovery of alternative drugs by swift identification of new drug targets. In the present study, we employed comparative genomics and metabolic pathway analysis with an aim of identifying therapeutic targets in Mycoplasma hominis. Our study has revealed 40 annotated metabolic pathways, including five unique pathways of M. hominis. Our study also identified 179 essential proteins, including 59 proteins having no similarity with human proteins. Further filtering by molecular weight, subcellular localization, functional analysis, and protein network interaction, we identified 57 putative candidates for which new drugs can be developed. Druggability analysis for each of the identified targets has prioritized 16 proteins as suitable for potential drug development.