University of Hyderabad
  • Hyderabad, Telangana, India
Recent publications
Alzheimer’s disease is a neurological disorder that affects an individual’s memory, motor functions, behaviour, and thought process. It has been observed that the hippocampus is the first region that gets affected by Alzheimer’s. Hence, a study of the hippocampus region can identify genes responsible for the occurrence of the early stage of the disease. Most often, t-test and correlation are used to identify significant genes at the initial level. As the genes are differentially expressed, their classification power is generally high. These genes might appear significant, but their degree of specificity towards the disease might be low, leading to misleading interpretations. Similarly, there may be many false correlations between the genes that can affect the identification of relevant genes. This paper introduces a new framework to reduce the false correlations and find the potential biomarkers for the disease. The framework concerned uses the t-test, correlation, Gene Ontology (GO) categories, and machine learning techniques to find potential genes. The proposed framework detects Alzheimer-related genes and achieves more than 95% classification accuracy in every dataset considered. Some of the identified genes which are directly involved in Alzheimer are APP, GRIN2B, and APLP2. The proposed framework also identifies genes like ZNF621, RTF1, DCH1, and ERBB4, which may play an important role in Alzheimer’s. Gene set enrichment analysis (GSEA) is also carried out to determine the major GO categories: down-regulated and up-regulated.
The present study investigates the impact of macroeconomic factors on food price inflation in India utilizing the monthly time series during January 2006–March 2019. The long-run relationship is confirmed among the variables using the ARDL bounds testing approach to cointegration. The coefficients of long-run estimates show that per capita income, money supply, global food prices, and agricultural wages are positively and significantly impacted food price inflation in both the short and long-run. While food grain availability has a negative and significant impact on food price inflation in both the short-run and long run. Further, the short-run estimates revealed that real exchange rate positively impacts food price inflation. However, the coefficient is insignificant in the short-run. The Granger causality estimates show that a short-run bidirectional causality is confirmed among per capita income, the exchange rate, per capita net availability of food grain and food price inflation. Further, there is evidence of unidirectional causality running from global food prices to food price inflation. However, there is no causal relationship running from money supply and agricultural wages to food price inflation in the short-run.
In this work, the Eu2O3 doped B2O3 glass specimen embedded with gold nanoparticles (NPs) was designed by the process of melt-quenching and subjected for thermal treatment to understanding the efficacy of thermal dependency on the optical and nonlinear optical (NLO) characteristics. Linear optical studies have revealed a decrease trend in the energy band gap as the function of annealing duration, indicating the increase of non-bridging oxygen concentration in the glass host. The NLO properties were assessed in near-infrared region utilizing 150 fs laser pulses delivered at the rate of 80 MHz. The aperture free Z-scan measurements exhibited an increased nonlinear absorption coefficient with annealing duration. Further, the closed aperture Z-scan data depicted an increase trend in the positive nonlinear refraction with annealing duration and eventually reduced at the greater duration of annealing. The optical limiting studies demonstrated similar behaviour to the nonlinear absorption characteristics of studied glass samples. These results are related to the local field stimulated by surface plasmons of gold NPs near the Eu³⁺ ions during the exposure of glass to high fluence laser beam. Our results indicate that B2O3 glasses embedded with Au NPs heat-treated for 30 h at 450 °C are good candidates for nonlinear photonic, optoelectronic, and optical power limiting applications in photonics.
In light of global energy insecurity and other pressing concerns like global warming, stringent emissions, and rapid industrialization, researchers are looking at the potential for alternative fuel research as a fossil fuel substitute. The present research work is carried out to study the influence of injection strategies (i.e., FIP and FIT) by varying the engine load at a constant velocity of 2000 rpm. The effect of several parameters such as engine load, FIT, and FIP on an RCCI engine fuelled with B20/1-pentanol fuel is examined using an L20 orthogonal array for an RCCI engine fuelled with B20/1-pentanol fuel. Various models were constructed and validated with the aid of experimental results. The ideal engine parameters found were 62.06% of engine load, FIP of 438.5 bar, and 27 0bTDC, and under this configuration, the output obtained is as follows: 206.9 ppm, 25.09 ppm, 33.04 J/deg, 29.39% and 2.39 kW for NOx, HC, HRR, BTE and BP emission respectively. Also, the regression model is developed using ANOVA, a statistically reliable test, and the test results suggest that the regression model is acceptable for the following. The following are the R² values obtained: NOx − 99.78%, HC– 99.14%, NHRR- 98.5%, BTE-99.93%, and BP-99.96% respectively.
The most important strategies of sustainable manufacturing or green manufacturing are encouraging non polluting energy sources and minimizing energy wastage. This can be achieved by utilizing the waste heat that has already been generated by other activities in the industries making double use of a single power source. The power conversion efficiency of industrial equipment is enhanced by integrating with Thermoelectric Generator (TEG) by converting a part of heat energy into the electrical energy. This paper presents a design of hybrid intelligent controller to extract maximum electrical power from the TEG system based on fuzzy logic and synergetic control theory. The designed hybrid controller assures the rapid convergence towards the maximum power operating point with reduced fluctuations under variable temperature and load conditions. The simulation results show effectiveness of proposed method in comparison with P&O and synergetic control methods under different conditions such as variable temperature and load.
TikTok is a popular platform allowing users to view and make short videos. The platform's embeddedness among youth cultures is key to TikTok’s commercial success, to attract and sustain a diverse array of international users. The discourse around children and social media especially TikTok is laced with technopanics, as is the case in India. Although sparse, literature shows children in India want to explore social media but parental mediation usually follows a restrictive style. Using a qualitative approach and multimodal methods, we unpack how children (10–18 years) in a large Indian metropolitan city use TikTok for self-presentation and creative expression while navigating restrictive parental mediation. This article helps gain in-depth understanding of children’s TikTok cultures in India by foregrounding their voices and contributes to larger scholarship on youth digital cultures by focusing on their: (a) vernacular cultures on TikTok, (b) deliberations on safety and wellbeing, and (c) negotiations regarding agency and resistance.
Signs of turbulence have been observed at the relativistic heavy ion collision at high collision energies. We study the signatures of turbulence in this system and find that there are significant departures from isotropic turbulence in the initial and the pre-equilibrium stages of the collision. As the anisotropic fluctuations are subleading to the isotropic fluctuations, the Kolmogorov spectrum can usually be obtained even for the initial stages. However, the energy spectrum and the temperature fluctuations indicate deviations from isotropic turbulence. Since a strong momentum anisotropy exists between the transverse and the longitudinal plane, we study the energy density spectrum in these two planes by slicing the sphere into different planes. The geometrical anisotropy is reflected in the anisotropic turbulence generated in the rotating plasma and we find that the scaling exponent is different in the two planes. We also obtain the temperature spectrum in the pre-equilibrium stages. The spectrum deviates from the Gaussian spectra expected for an isotropic turbulence. All these seem to indicate that the large scale momentum anisotropy persists in the smaller length scales for the relativistic heavy ion collisions.
Despite the obligatory role of ethylene in climacteric fruit ripening and the identification of 77 ethylene response factors (ERFs) in the tomato (Solanum lycopersicum) genome, the role of few ERFs has been validated in the ripening process. Here, using a comprehensive morpho-physiological, molecular and biochemical approach, we demonstrate the regulatory role of Ethylene Response Factor D7 (SlERF.D7) in tomato fruit ripening. SlERF.D7 expression positively responded to exogenous ethylene and auxin treatments, most likely in a Ripening Inhibitor (RIN)-independent manner. SlERF.D7 overexpression promoted ripening, and its silencing had the opposite effect. Alterations in its expression modulated ethylene production, pigment accumulation, and fruit firmness. Consistently, genes involved in ethylene biosynthesis and signalling, lycopene biosynthesis, and cell wall loosening were upregulated in the overexpression lines and downregulated in RNAi lines. These transgenic lines also accumulated altered levels of IAA at late-breaker stages. A positive association between Auxin Response Factor 2 (ARF2) paralog’s transcripts and SlERF.D7 mRNA levels and that SlARF2A and SlARF2B are direct targets of SlERF.D7 underpinned the perturbed auxin-ethylene crosstalk for the altered ripening program observed in the transgenic fruits. Overall, this study uncovers that SlERF.D7 positively regulates SlARF2A/B abundance to amalgamate auxin and ethylene signalling pathways for controlling tomato fruit ripening.
The problem of choosing a team for a given project/task with minimum communication cost is known as team formation problem for which many algorithms have been proposed in the literature. The skill-centric algorithms in the literature start by searching for suitable experts for each skill. These algorithms are very slow as the search requires shortest path calculations. We propose that by considering the topology of the underlying social network, the algorithms can be made more efficient. We contribute two algorithms in this paper for team formation, namely, TPLRandom and TPLClosest which exploit the power law of the degree distribution of the social network to form a team. The proposed algorithm is based on the idea that is generally adopted while a team is being formed for the real world challenges. A team leader is identified first who then sets about choosing the team members possessing the necessary skills required for the task. The algorithms choose high degree nodes from the heavy tail of the degree distribution to act as leaders. The leaders form teams from their own neighbourhoods and the one with the lowest communication cost is chosen as the best team. This is an entirely novel approach to team formation problem. We show that these high degree experts and their neighbours cover a large number of skills required for the task, reducing the expensive computations and thus yielding a fast and scalable algorithm. The experimentation is carried out on the well-known DBLP data set. We build a much larger benchmark data set from DBLP for experimentation. Our algorithms TPLClosest and TPLRandom provide teams with significantly lower communication costs. They also surpass the other conventional algorithms such as MinLD and MinSD in terms of the execution time.
Multi-state and multi-mode vibronic dynamics in the seven energetically low-lying (X~2A', A~2A″, B~2A', C~2A', D~2A″, E~2A', and F~2A') electronic states of the acetaldehyde radical cation is theoretically studied in this article. Adiabatic energies of these electronic states are calculated by ab initio quantum chemistry methods. A vibronic coupling model of seven electronic states is constructed in a diabatic electronic basis to carry out the first-principles nuclear dynamics study. The vibronic spectrum is calculated and compared with the experimental findings reported in the literature. The progressions of vibrational modes found in the spectrum are assigned. The findings reveal that the X~2A' and F~2A' electronic states are energetically well-separated from the other electronic states and the remaining states (A~2A″ to E~2A') are energetically very close or even quasi-degenerate at the equilibrium geometry of the reference electronic ground state of acetaldehyde. The energetic proximity of A~2A″ to E~2A' electronic states results in multiple multi-state conical intersections. The impact of electronic nonadiabatic interactions due to conical intersections on the vibronic structure of the photoionization band and nonradiative internal conversion dynamics is discussed.
The 3.52-Mbp whole-genome sequence of a Glutamicibacter sp. strain isolated from soil sediment of the floating islands of Loktak Lake is reported. The genomic information here gives insight into the presence of genes linked to oxidative stress, osmo-protection, and cold shock proteins which helps in the survival of the organism under extreme environmental conditions.
In recent times, with more thermal efficiency, low NOx and particulate matter, Homogeneous Charge Compression Engines (HCCI Engines) emerged for renewed interest. However, knocking and misfiring were considered as classic problems in HCCI engines. To compensate the impact of depletion of petroleum reserves also to lessen the deleterious effects of most regularly used fossil fuels, alternate fuels emerged into the scenario. Researchers studied that Compression ignition engines faced difficulties due to the inherent nature of high viscosity, when vegetable oils were consumed. In the present research, the impact of Kusum oil biodiesel blends on performance and emission characteristics were examined using experimental investigation. Conventional diesel engine has been converted into HCCI engine to operate in homogeneous mode. The blends B20, B50 and pure biodiesel were taken, by varying the input air temperature and port fuel injection pressure, the variation of performance and emission characteristics were analyzed. To implement HCCI mode operation, Homogenous mixture of air and fuel is made by port fuel injection technique which is the basic requirement. Using an external device, the mixture formation and fuel vaporization was done. Varying the temperature of the blends at different port fuel injection pressures, the performance parameter brakes thermal efficiency, NOx, CO, CO2, HC emissions and smoke density were analyzed in the HCCI mode. Also optimal parameters were found by using the experimental investigations.KeywordsKusum oil biodieselAlternative fuelHCCI engineEngine emissionPort fuel injection pressureAir inlet temperature
The discovery of the roles of RNA other than just as a messenger, such as a ribozyme, and regulatory RNAs such as microRNA and long noncoding RNAs are fascinating. RNA is now recognized as an important regulator involved in practically every biological process. Research in the field of non-coding RNAs, specifically microRNAs (miRNAs) and long non-coding RNAs (LncRNAs) are developed immensely over the years. Recent studies identified diverse RNAs including non-coding RNAs such as LncRNA and their various modes of action in the cells. These RNAs are anticipated to be key targets for the treatment of various diseases since they control a broad array of biological pathways. LncRNA-targeted drug platform delivers the pharmaceutical industry a myriad of opportunities and has the potential to modulate diseases at the genetic level while also overcoming the limitations of inconsistent proteins. This article focuses on the recent advancement as well as the major challenges in the field and describes the various RNA-based therapeutics which alter the quality of healthcare for many diseases and bring personalized medicines to fruition. The article also summarizes RNA-based therapeutics that are undergoing testing in clinical trials or have granted FDA approval.
Malaria continues to severely impact the global public health not only due to the mortality and morbidity associated with it, but also because of the huge burden on the world economy it imparts. Despite the intensive vaccine-research and drug-development programs, there is not a single effective vaccine suitable for all age groups, and there is no drug on the market against which resistance is not developed.
Transglutaminase 2 (TG2) is a calcium-dependent enzyme that catalyzes the N-ϵ-(γ-glutamyl) lysine bonds between side chains of glutamine and lysine residues resulting in proteolytically resistant cross-links. Increased TG2 activity and levels are involved in the pathophysiology of various diseases, including liver injury, cystic fibrosis, celiac sprue, metastatic cancers, and several neurodegenerative conditions. Inhibiting TG2 activity is considered a potential strategy to combat these diseases. Although guanine nucleotide (GTP) could inhibit TG2, its inhibitory activity decreased with increased calcium concentration. Search for GTP analogs that could strongly bind and inhibit TG2 activity is intense. This study screened the PubChem database for about two thousand GTP-like compounds for TG2. Using docking and molecular dynamics simulations we identified three compounds (C4959, C4215, and C9560) that could selectively interact with TG2. These three compounds have less affinity for several other intracellular and extracellular GTP-binding proteins suggesting selectivity for TG2. Interestingly, C9560 showed stronger interactions and better binding energy with TG2 than C4959 and C4215, suggesting that C9560 can form a more stable complex with TG2. Our study indicates that C9560, a GTP analog, could be exploited as a promising candidate to inhibit TG2-mediated fibrotic conditions.
Climate change adversely affects the yield and productivity of cereal crops, which consequently impacts food security. Therefore, studying stress acclimation, particularly transcriptional patterns and morpho-physiological responses of cereal crops to different stresses, will provide insights into the molecular determinants underlying climate resilience. The availability of advanced tools and approaches has enabled the characterization of plants at morphological, physiological, biochemical, and molecular levels, which will lead to the identification of genomic regions regulating the stress responses at these levels. This will further facilitate using transgenic, breeding, or genome editing approaches to manipulate the identified regions (genes, alleles, or QTLs) to enhance stress resilience. Next-generation sequencing approaches have advanced the identification of causal genes and markers in the genomes through forward or reverse genetics. In this context, the review enumerates the progress of dissecting the molecular mechanisms underlying transcriptional and physiological responses of major cereals to climate-induced stresses. The review systematically discusses different tools and approaches available to study the response of plants to various stresses and identify the molecular determinants regulating stress-resilience. Further, the application of genomics-assisted breeding, transgene-, and targeted editing-based approaches for modulating the genetic determinants for enhanced climate resilience has been elaborated.
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3,942 members
Bindu Bambah
  • School of Physics
Sadaf Kalam
  • Department of Plant Sciences
James Raju
  • School of Physics
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Gachibowli, 500046, Hyderabad, Telangana, India
Head of institution
Prof B.J. Rao
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www.uohyd.ac.in
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040 66792000