Shiv Nadar University

About: Shiv Nadar University is a education organization based out in Dadri, Uttar Pradesh, India. It is known for research contribution in the topics: Population & Graphene. The organization has 1015 authors who have published 1924 publications receiving 18420 citations.

Crossing the Nernst Limit (59 mV/pH) of Sensitivity Through Tunneling Transistor-Based Biosensor

Abstract: In this work, an underlap structure of tunneling field-effect transistor (TFET) containing electrolyte/watery solution is examined to enhance the Nernst limit (59 mV/pH) of sensitivity. After incorporating the electrolyte medium in TFET, effect of pH variation on device characteristics such as drain current vs front gate voltage, voltage sensitivity, and current sensitivity are investigated. The interface charge density at the oxide-silicon interface of TFET is obtained as a function of electrolyte pH from physics-based modelling. Voltage sensitivity value ~180 mV/pH that is greater than three times of Nernst limit of 59 mV/pH and current sensitivity value that is more than one decade per pH are observed for TFET based sensor. In order to validate the results, models used in TFET are well-calibrated with experimental data and the result of TFET are compared with inversion mode (IM) device. Results show that TFET gives superior performance than IM device; hence an underlap TFET can be a promising alternative for the next generation biosensor.

The catalytic cycle of cytochrome P450: a fascinating choreography

Abstract: We review molecular dynamic (MD) simulations of catalytic cycles for two typical cytochrome P450 enzymes that oxidize organic molecules (substrates). The simulations show that P450 is an open nanomachine that exchanges molecules with the outside and functions automatically once a substrate enters the enzyme’s cavity. The rest is done by reducing partners, a few residues, and water molecules that provide timed cues for these nanomachines. Fascinating outcomes are achieved, like opening/closing of the enzyme to uptake substrates, closings/openings of water gates to shuttle protons, and gating of channels through which the oxidized products exit and provide a ‘restart’ cue. The more we analyzed the MD trajectories, the more apparent it became: the amazing features of these enzymes are rooted in the chemistry of weak interactions in tight spaces. These cues are sensed by the protein and elicit the seemingly automatic cycle that governs the regio- and enantioselectivity of the enzymes.

EmTaggeR: A Word Embedding Based Novel Method for Hashtag Recommendation on Twitter

Abstract: The hashtag recommendation problem addresses recommending (suggesting) one or more hashtags to explicitly tag a post made on a given social network platform, based upon the content and context of the post. In this work, we propose a novel methodology for hashtag recommendation for microblog posts, specifically Twitter. The methodology, EmTaggeR, is built upon a training-testing framework that builds on the top of the concept of word embedding. The training phase comprises of learning word vectors associated with each hashtag, and deriving a word embedding for each hashtag. We provide two training procedures, one in which each hashtag is trained with a separate word embedding model applicable in the context of that hashtag, and another in which each hashtag obtains its embedding from a global context. The testing phase constitutes computing the average word embedding of the test post, and finding the similarity of this embedding with the known embeddings of the hashtags. The tweets that contain the most-similar hashtag are extracted, and all the hashtags that appear in these tweets are ranked in terms of embedding similarity scores. The top-K hashtags that appear in this ranked list, are recommended for the given test post. Our system produces F1 score of 50.83%, improving over the LDA baseline by around 6.53 times, outperforming the best-performing system known in the literature that provides a lift of 6.42 times. EmTaggeR is a fast, scalable and lightweight system, which makes it practical to deploy in real-life applications.

Post-translational regulation of a Porphyromonas gingivalis regulator

Abstract: Background: Bacteria use two-component signal transduction systems (among others) to perceive and respond to environmental changes. Within the genus Porphyromonas, we observed degeneration of these systems, as exemplified by the loss of RprX, the sensor kinase partner of the RprY. Objective: The purpose of this study was to investigate modulation of RprY function by acetylation. Design: The transcriptional activity of the rprY-pat genes were measured by RT-PCR and 5'-RACE. The acetylation of RprY were detected by western blotting. Electromobility shift and in vitro ChIP assays were used to measure the DNA binding activity of RprY. The expression of RprY target genes was measured by qRT-PCR. Effects of acetylation on phosphorylation of RprY were measured by Phos-tag gels. Results: The rprY gene is cotranscribed with pat. RprY is acetylated in vivo, and autoacetylated in vitro in a reaction that is enhanced by Pat; the CobB sirtuin deacetylates RprY. Acetylation reduced the DNA binding of RprY. Induced oxidative stress decreased production of RprY in vivo, increased its acetylation and increased expression of nqrA. Conclusions: We propose that to compensate for the loss of RprX, P. gingivalis has evolved a novel mechanism to inactivate RprY through acetylation.

A new pentacyclic pyrylium fluorescent probe that responds to pH imbalance during apoptosis

Abstract: Efficient fluorophores with easy synthetic routes and fast responses are of great importance in clinical diagnostics. Herein, we report a new, rigid pentacyclic pyrylium fluorophore, PS-OMe, synthesised in a single step by a modified Vilsmeier–Haack reaction. Insights into the reaction mechanism facilitated a new reaction protocol for the efficient synthesis of PS-OMe which upon demethylation resulted in a “turn-on” pH sensor, PS-OH. This new fluorescent probe has been successfully used to monitor intracellular acidification at physiological pH. From the fluorescence image analysis, we were able to quantify the intracellular dynamic pH change during apoptosis. This new pH probe is a potential chemical tool for screening, drug discovery and dose determination in cancer therapy.