Biology subjects

Iyer, L. R.

Publications and source records attributed to Iyer, L. R..

2 recordsLinked to original sources

bioRxiv2025

Full-length next-generation sequencing of 11 HLA loci of more than 1000 individuals from clinical cohorts in East and West Africa

Human Leukocyte Antigen (HLA) loci have been implicated in several diseases from different world populations, including HIV-1. It is necessary to characterize HLA allele variation at the population level prior to investigating associations linked to human diseases. In global databases, limited high-resolution HLA allele types generated by next-generation sequencing (NGS) have been described for populations from African countries. We sought to expand our HLA NGS database to include a total of 1023 participants from multiple HIV clinical studies using full-length HLA genotyping by NGS. Collectively we describe HLA genotypes of individuals from Kenya (n=375), Uganda (n=338), Nigeria (n=139), Tanzania (n=89), and Mozambique (n=82). Overall, we identified 371 unique HLA alleles across 11 loci with the most frequent at each locus being A*02:01:01, B*53:01:01, C*04:01:01, C*06:02:01, DPA1*01:03:01, DPB1*01:01:01, DQA1*01:02:01, DQB1*06:02:01, DRB1*15:03:01, DRB3*02:02:01, DRB4*01:03:01, and DRB5*01:01:01. A total of 25 novel alleles were identified, including 4 with non-synonymous changes affecting the peptide binding groove of HLA molecules. This expansion of NGS based HLA data at the African population level will improve our understanding of human genetic variation and provide insights for vaccine development and targeted personalized therapies.

genetics↗

bioRxiv2020

A Spiking Neural Network Model for Category Learning

The creation of useful categories from data is an important cognitive ability, and from the extensive research on categorization, it is now known that the brain has distinct systems for category learning. In this paper, we present the first spiking neural network (SNN) model of human category learning. Here categories are combinations of features - such categories are observed in the prefrontal cortex (PFC). The system follows an architecture commonly used to model the cortex - features are arranged in a topological 2D grid with short range excitatory connectivity and long range inhibitory connectivity - however, here, this architecture is used differently from other models to model higher level cognition. Earlier we presented an artificial neural network (ANN) model of category learning; however, here, a simpler model was adequate, as desired functionality emerges from the SNN dynamics. We identified the objectives that had to be fulfilled for the model to achieve the desired functionality, and performed a design space exploration (DSE) to identify the parameter range in which each of the objectives was fulfilled, and the parameter range for which the system exhibits good performance. Finally, we compared triphasic STDP (a variant of spike time dependant plasticity (STDP)) with standard STDP and observed that triphasic STDP exhibited quicker convergence.

bioengineering↗