00857nas a2200241   4500008004100000022001400041245008500055210006900140260009400209300000900303490000800312100001700320700002200337700001400359700001600373700002100389700001600410700001800426700002300444700001300467700002000480856011500500       2012                        eng d  a0022-202X00aNovel mechanism involved in temporal regulation of skin pigmentation homeostasis0 aNovel mechanism involved in temporal regulation of skin pigmenta  a75 VARICK ST, 9TH FLR, NEW YORK, NY 10013-1917 USAbEuropean Soc Dermatol Res (ESDR)cSEP  aS1280 v1321 aGanju, Parul1 aNatarajan, V., T.1 aSingh, A.1 aVijayan, V.1 aDani, Prachi, P.1 aKar, H., K.1 aNatarajan, K.1 aGadgil, Chetan, J.1 aRani, R.1 aGokhale, R., S.  uhttp://library.ncl.res.in/content/novel-mechanism-involved-temporal-regulation-skin-pigmentation-homeostasis-002926nas a2200265   4500008004100000022001400041245007400055210006900129260008000198300000700278490000600285520204200291653003102333653001102364653001002375653001902385653001002404100002402414700002302438700002902461700002202490700002302512700001802535856010702553       2014                        eng d  a1662-510200aNon-coding RNA interact to regulate neuronal development and function0 aNoncoding RNA interact to regulate neuronal development and func  aPO BOX 110, LAUSANNE, 1015, SWITZERLANDbFRONTIERS RESEARCH FOUNDATIONcFEB  a470 v83 a<p>The human brain is one of the most complex biological systems, and the cognitive abilities have greatly expanded compared to invertebrates without much expansion in the number of protein coding genes. This suggests that gene regulation plays a very important role in the development and function of nervous system, by acting at multiple levels such as transcription and translation. In this article we discuss the regulatory roles of three classes of non-protein coding RNAs (ncRNAs)-microRNAs (miRNAs), piwi-interacting RNA (piRNAs) and long-non-coding RNA (lncRNA), in the process of neurogenesis and nervous function including control of synaptic plasticity and potential roles in neurodegenerative diseases. miRNAs are involved in diverse processes including neurogenesis where they channelize the cellular physiology toward neuronal differentiation. miRNAs can also indirectly influence neurogenesis by regulating the proliferation and self renewal of neural stem cells and are dysregulated in several neurodegenerative diseases. miRNAs are also known to regulate synaptic plasticity and are usually found to be co-expressed with their targets. The dynamics of gene regulation is thus dependent on the local architecture of the gene regulatory network (GRN) around the miRNA and its targets. piRNAs had been classically known to regulate transposons in the germ cells. However, piRNAs have been, recently, found to be expressed in the brain and possibly function by imparting epigenetic changes by DNA methylation. piRNAs are known to be maternally inherited and we assume that they may play a role in early development. We also explore the possible function of piRNAs in regulating the expansion of transposons in the brain. Brain is known to express several lncRNA but functional roles in brain development are attributed to a few lncRNA while functions of most of the them remain unknown. We review the roles of some known lncRNA and explore the other possible functions of lncRNAs including their interaction with miRNAs.</p>10agene expression regulation10alncRNA10amiRNA10anetwork-motifs10apiRNA1 aIyengar, Bharat, R.1 aChoudhary, Ashwani1 aSarangdhar, Mayuresh, A.1 aVenkatesh, K., V.1 aGadgil, Chetan, J.1 aPillai, Beena  uhttp://library.ncl.res.in/content/non-coding-rna-interact-regulate-neuronal-development-and-function-002093nas a2200169   4500008004100000022001400041245008200055210006900137260000800206300001200214490000700226520151500233100002001748700002101768700002301789856011101812       2024                        eng d  a1072-050200aNetwork motifs exhibiting a differential response to spaced and massed inputs0 aNetwork motifs exhibiting a differential response to spaced and   cJUL  aa0540120 v313 a<p>
	One characteristic of long-term memory is the existence of an inverted U-shaped response to increasing intervals between training sessions, and consequently, an optimal spacing that maximizes memory formation. Current models of this spacing effect focus on specific molecular components and their interactions. Here, we computationally study the underlying network architecture, in particular, the potential of motif dynamics in qualitatively capturing the spacing effect in a manner that is independent of the animal model, biomolecular components, and the timescales involved. We define a common training and test protocol, and computationally identify network topologies that can qualitatively replicate the experimentally observed characteristics of the spacing effect. For 41 motifs derived from fundamental network architectures such as autoregulation, feedback, and feedforward motifs, we tested their capacity to manifest the spacing effect in terms of an inverted U-shaped response curve, using different combinations of stimulation protocols, response metrics, and kinetic parameters. Our findings indicate that positive feedback motifs where the stimulus enhances conversion reaction in the loop replicate the spacing effect across all response metrics, while feedforward motifs exhibit a metric-specific spacing effect. For some parameter combinations, linear cascades of activation and conversion reactions were found sufficient to qualitatively exhibit spacing effect characteristics.</p>
1 aSreejan, Ashley1 aSaxena, Priyanka1 aGadgil, Chetan, J.  uhttp://library.ncl.res.in/content/network-motifs-exhibiting-differential-response-spaced-and-massed-inputs