02488nas a2200265   4500008004100000022001400041245020700055210006900262260007300331300001200404490000800416520142000424653002101844653003601865653002101901653001701922653002401939653002601963653002901989653001402018653001202032100002702044700001902071856013202090       2009                        eng d  a1387-181100aDependence of Si-29 NMR chemical shielding properties of precursor silicate species, Q(0) on its local structure at the pre-nucleation stages of zeolite synthesis - a DFT based computational correlation0 aDependence of Si29 NMR chemical shielding properties of precurso  aPO BOX 211, 1000 AE AMSTERDAM, NETHERLANDSbELSEVIER SCIENCE BVcJUN  a105-1130 v1223 a<p>The exploration for new zeolite structures with tailored framework architectures for enhanced catalytic applications requires the knowledge about their nucleation and crystallization at molecular level. Nuclear magnetic resonance (NMR) is one of the most widely tried techniques to understand this. However, by NMR, it is difficult to accurately assign the molecular level precursor silicate structures at the pre-nucleation stages of zeolite synthesis. Hence, understanding the chemical shielding of such precursor molecules using quantum mechanical (QM) computations is extremely useful. Alkali is a fundamental component in the alkali based hydrothermal zeolite synthesis and its nature plays a major role. In the present report, we attempt to understand the differences in the local structure of the primary building block such as Si(OH)(4) (Q(0) silicate species) due to the associated alkali and their influence on NMR chemical shielding properties. Present work reports the calculation of Si-29 NMIR isotropic chemical shifts of T species with different cations such as Na, K and Ca using density functional theory (DFT). Results of natural bonding orbital (NBO) analysis, Perturbation theory energy analysis and electron density iso-surfaces were employed to obtain a deeper insight about their influence on the chemical shielding and on zeolite synthesis. (C) 2009 Elsevier Inc. All rights reserved.</p>10aAb initio method10aDensity Functional Theory (DFT)10aElectron density10aNBO analysis10aPerturbation theory10aQ(0) silicate species10aSi-29 NMR chemical shift10asynthesis10azeolite1 aSelvaraj, Kaliaperumal1 aKurian, Reshmi  uhttps://library.ncl.res.in/content/dependence-si-29-nmr-chemical-shielding-properties-precursor-silicate-species-q0-its-local-002125nas a2200265   4500008004100000022001400041245009000055210006900145260000800214300000800222490000700230520122000237653002401457653002201481653001901503653003301522653004301555653002501598653002401623653002501647100002201672700003001694700002301724856011201747       2018                        eng d  a1751-884900aMethod for identification of sensitive nodes in boolean models of biological networks0 aMethod for identification of sensitive nodes in boolean models o  cFEB  a1-60 v123 a<p>Biological systems are often represented as Boolean networks and analysed to identify sensitive nodes which on perturbation disproportionately change a predefined output. There exist different kinds of perturbation methods: perturbation of function, perturbation of state and perturbation in update scheme. Nodes may have defects in interpretation of the inputs from other nodes and calculation of the node output. To simulate these defects and systematically assess their effect on the system output, two new function perturbations, referred to as not of function' and function of not', are introduced. In the former, the inputs are assumed to be correctly interpreted but the output of the update rule is perturbed; and in the latter, each input is perturbed but the correct update rule is applied. These and previously used perturbation methods were applied to two existing Boolean models, namely the human melanogenesis signalling network and the fly segment polarity network. Through mathematical simulations, it was found that these methods successfully identified nodes earlier found to be sensitive using other methods, and were also able to identify sensitive nodes which were previously unreported.</p>10abiological networks10aBoolean functions10aBoolean models10afly segment polarity network10ahuman melanogenesis signalling network10aperturbation methods10aPerturbation theory10aphysiological models1 aDnyane, Pooja, A.1 aPuntambekar, Shraddha, S.1 aGadgil, Chetan, J.  uhttps://library.ncl.res.in/content/method-identification-sensitive-nodes-boolean-models-biological-networks