bioRxiv Science⌕ Search

Biology subjects

Sadhu, A.

Publications and source records attributed to Sadhu, A..

2 recordsLinked to original sources

Single neuron analysis of aging associated changes in learning reveals progressive impairmentsin transcriptional plasticity

Molecular mechanisms underlying aging associated impairments in learning and long-term memory storage are poorly understood. Here we leveraged the single identified motor neuron L7 in Aplysia, which mediates a form of non-associative learning, sensitization of the siphon-withdraw reflex, to assess the transcriptomic correlates of aging associated changes in learning. RNAseq analysis of the single L7 motor neuron isolated following short-term or long-term sensitization training of 8,10 and 12 months old Aplysia, corresponding to mature, late mature and senescent stages, has revealed progressive impairments in transcriptional plasticity during aging. Specifically, we observed modulation of the expression of multiple lncRNAs and mRNAs encoding transcription factors, regulators of translation, RNA methylation, and cytoskeletal rearrangements during learning and their deficits during aging. Our comparative gene expression analysis also revealed the recruitment of specific transcriptional changes in two other neurons, the motor neuron L11 and the giant cholinergic neuron R2 whose roles in long-term sensitization were previously not known. Taken together, our analyses establish cell type specific progressive impairments in the expression of learning- and memory-related components of the transcriptome during aging.

neuroscience↗

Mammogram Segmentation using Multi-atlas Deformable Registration

Accurate breast region segmentation is an important step in various automated algorithms involving detection of lesions like masses and microcalcifications, and efficient telemammography. Existing segmentation algorithms underperform due to variations in image quality and shape of the breast region. In this paper, we propose to segment breast region by combining data-driven clustering with deformable image registration. In the first phase of the approach, we identify atlas images from a dataset of mammograms using data-driven clustering. Then, we segment these atlas images and use in the next phase of the algorithm. The second phase is atlas-based registration. For a candidate image, we find the most similar atlas image from the set of atlases identified in phase one. We deform the selected atlas image to match the given test image using the Demon's registration algorithm. Then, the segmentation mask of the deformed atlas is transferred to the mammogram in consideration. Finally, we refine the segmentation mask with some morphological operations in order to obtain accurate breast region boundary. We evaluated the performance of our method using ground-truth segmentation masks verified by an expert radiologist. We compared the proposed method with three existing state-of-the-art algorithms for breast region segmentation and the proposed approach outperformed all three in most of the cases.

bioinformatics↗