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Institution

Indian Institute of Technology Indore

EducationIndore, Madhya Pradesh, India
About: Indian Institute of Technology Indore is a education organization based out in Indore, Madhya Pradesh, India. It is known for research contribution in the topics: Fading & Support vector machine. The organization has 1606 authors who have published 4803 publications receiving 66500 citations.


Papers
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Journal ArticleDOI
TL;DR: A combination of factors that includes ionic surface characteristics of the nanoparticles for strong electrostatic binding on the bacterial surface followed by possible photoinduced electron transfer from the nanoaggregates to the bacterial membrane and enhanced oxidative stress in the membrane resulting from reactive oxygen species (ROS) generation were found accountable for the high antimicrobial activity of the TGP nanoparticles.
Abstract: The increase in the use of bactericides is a matter of grave concern and a serious threat to human health. The present situation demands rapid and efficient detection and elimination of antibiotic-resistant microbes. Herein, we report the synthesis of a simple C3-symmetric molecular system (TGP) with an intrinsic positive charge through a single-step Schiff base condensation. In a water-dimethyl sulfoxide (DMSO) solvent mixture (80:20 v/v), TGP molecules self-aggregate to form spherical nanoparticles with a positively charged surface that displays efficient fluorescence owing to the aggregation-induced emission (AIE) phenomenon. Both Gram-positive and Gram-negative bacteria could be effectively detected through "turn-off" fluorescence spectroscopy as the electrostatic interaction of the resultant nanoaggregates with the negatively charged bacterial surface induced quenching of fluorescence of the nanoparticles. The fluorescence analysis and steady-state lifetime studies of TGP nanoparticles suggest that a nonradiative decay through photoinduced electron transfer from the nanoparticles to the bacterial surface leads to effective fluorescence quenching. Further, the TGP nanoaggregates demonstrate potent antimicrobial activity against microbes such as multidrug-resistant bacteria and fungi at a concentration as low as 74 μg/mL. A combination of factors including ionic surface characteristics of the nanoparticles for strong electrostatic binding on the bacterial surface followed by possible photoinduced electron transfer from the nanoaggregates to the bacterial membrane and enhanced oxidative stress in the membrane resulting from reactive oxygen species (ROS) generation is found accountable for the high antimicrobial activity of the TGP nanoparticles. The effective disruption of membrane integrity in both Gram-positive and Gram-negative bacteria upon interaction with the nanoaggregates can be observed from field emission scanning electron microscopy (FESEM) studies. The development of simple pathways for the molecular design of multifunctional broad-spectrum antimicrobial systems for rapid and real-time detection, wash-free imaging, and eradication of drug-resistant microbes might be crucial to combat pathogenic agents.

36 citations

Journal ArticleDOI
TL;DR: This enhanced GONN algorithm produces better results than popular classification algorithms like Genetic Algorithm, Support Vector Machine and Neural Network which makes it a good alternative to the well-known machine learning methods for solving multi-class classification problems.
Abstract: An enhanced Genetically Optimized Neural Network (GONN) is proposed for Multi-class data classification.Multi-tree GONN classifier is used to classify multi-class data.Enhanced GONN produces the highest classification accuracy among other classifiers in less time. Multi-class classification is one of the major challenges in real world application. Classification algorithms are generally binary in nature and must be extended for multi-class problems. Therefore, in this paper, we proposed an enhanced Genetically Optimized Neural Network (GONN) algorithm, for solving multi-class classification problems. We used a multi-tree GONN representation which integrates multiple GONN trees; each individual is a single GONN classifier. Thus enhanced classifier is an integrated version of individual GONN classifiers for all classes. The integrated version of classifiers is evolved genetically to optimize its architecture for multi-class classification. To demonstrate our results, we had taken seven datasets from UCI Machine Learning repository and compared the classification accuracy and training time of enhanced GONN with classical Koza's model and classical Back propagation model. Our algorithm gives better classification accuracy of almost 5% and 8% than Koza's model and Back propagation model respectively even for complex and real multi-class data in lesser amount of time. This enhanced GONN algorithm produces better results than popular classification algorithms like Genetic Algorithm, Support Vector Machine and Neural Network which makes it a good alternative to the well-known machine learning methods for solving multi-class classification problems. Even for datasets containing noise and complex features, the results produced by enhanced GONN is much better than other machine learning algorithms. The proposed enhanced GONN can be applied to expert and intelligent systems for effectively classifying large, complex and noisy real time multi-class data.

36 citations

Journal ArticleDOI
Shreyasi Acharya1, Dagmar Adamová2, Souvik Priyam Adhya1, Alexander Adler3  +1051 moreInstitutions (102)
TL;DR: In this paper, the J/ψ meson was reconstructed via the dimuon decay channel at forward rapidity (2.5 < y < 4) down to zero transverse momentum.
Abstract: The inclusive J/ψ production in Pb–Pb collisions at the center-of-mass energy per nucleon pair $ \sqrt{s_{\mathrm{NN}}} $ = 5.02 TeV, measured with the ALICE detector at the CERN LHC, is reported. The J/ψ meson is reconstructed via the dimuon decay channel at forward rapidity (2.5 < y < 4) down to zero transverse momentum. The suppression of the J/ψ yield in Pb–Pb collisions with respect to binary-scaled pp collisions is quantified by the nuclear modification factor (R$_{AA}$). The R$_{AA}$ at $ \sqrt{s_{\mathrm{NN}}} $ = 5.02 TeV is presented and compared with previous measurements at $ \sqrt{s_{\mathrm{NN}}} $ = 2.76 TeV as a function of the centrality of the collision, and of the J/ψ transverse momentum and rapidity. The inclusive J/ψ RAA shows a suppression increasing toward higher transverse momentum, with a steeper dependence for central collisions. The modification of the J/ψ average transverse momentum and average squared transverse momentum is also studied. Comparisons with the results of models based on a transport equation and on statistical hadronization are carried out.[graphic not available: see fulltext]

36 citations

Journal ArticleDOI
TL;DR: In this article, the authors presented the first results of a project called SAGAN dedicated solely to the studies of relatively rare megaparsec-scale radio galaxies in the Universe, called giant radio galaxies (GRGs).
Abstract: We present the first results of a project called SAGAN, which is dedicated solely to the studies of relatively rare megaparsec-scale radio galaxies in the Universe, called giant radio galaxies (GRGs). We have identified 162 new GRGs primarily from the NRAO VLA Sky Survey with sizes ranging from ∼0.71 Mpc to ∼2.82 Mpc in the redshift range of ∼0.03−0.95, of which 23 are hosted by quasars (giant radio quasars). As part of the project SAGAN, we have created a database of all known GRGs, the GRG catalogue, from the literature (including our new sample); it includes 820 sources. For the first time, we present the multi-wavelength properties of the largest sample of GRGs. This provides new insights into their nature. Our results establish that the distributions of the radio spectral index and the black hole mass of GRGs do not differ from the corresponding distributions of normal-sized radio galaxies (RGs). However, GRGs have a lower Eddington ratio than RGs. Using the mid-infrared data, we classified GRGs in terms of their accretion mode: either a high-power radiatively efficient high-excitation state, or a radiatively inefficient low-excitation state. This enabled us to compare key physical properties of their active galactic nuclei, such as the black hole mass, spin, Eddington ratio, jet kinetic power, total radio power, magnetic field, and size. We find that GRGs in high-excitation state statistically have larger sizes, stronger radio power, jet kinetic power, and higher Eddington ratio than those in low-excitation state. Our analysis reveals a strong correlation between the black hole Eddington ratio and the scaled jet kinetic power, which suggests a disc-jet coupling. Our environmental study reveals that ∼10% of all GRGs may reside at the centres of galaxy clusters, in a denser galactic environment, while the majority appears to reside in a sparse environment. The probability of finding the brightest cluster galaxy as a GRG is quite low and even lower for high-mass clusters. We present new results for GRGs that range from black hole mass to large-scale environment properties. We discuss their formation and growth scenarios, highlighting the key physical factors that cause them to reach their gigantic size.

36 citations

Journal ArticleDOI
TL;DR: Mycobacterium tuberculosis LprE is an important virulence factor that plays a crucial role in mycobacterial pathogenesis in the context of innate immunity and the inhibition of phago-lysosome fusion by LprEMtb was found to be due to downregulation of IL-12 and IL-22 cytokines.
Abstract: Despite representing a very important class of virulence proteins, the role of lipoproteins in the pathogenesis of Mycobacterium tuberculosis remains elusive. In this study, we investigated the role of putative lipoprotein LprE in the subversion of host immune responses using the M. tuberculosis CDC1551 LprE (LprE Mtb ) mutant (Mtb∆LprE). We show that deletion of LprE Mtb results in reduction of M. tuberculosis virulence in human and mouse macrophages due to upregulation of vitamin D3-responsive cathelicidin expression through the TLR2-dependent p38-MAPK-CYP27B1-VDR signaling pathway. Conversely, episomal expression of LprE Mtb in Mycobacterium smegmatis improved bacterial survival. Infection in siTLR2-treated or tlr2-/- macrophages reduced the survival of LprE Mtb expressing M. tuberculosis and M. smegmatis because of a surge in the expression of cathelicidin. Infection with the LprE Mtb mutant also led to accumulation of autophagy-related proteins (LC3, Atg-5, and Beclin-1) and augmented recruitment of phagosomal (EEA1 and Rab7) and lysosomal (LAMP1) proteins, thereby resulting in the reduction of the bacterial count in macrophages. The inhibition of phago-lysosome fusion by LprE Mtb was found to be due to downregulation of IL-12 and IL-22 cytokines. Altogether, our data indicate that LprE Mtb is an important virulence factor that plays a crucial role in mycobacterial pathogenesis in the context of innate immunity.

36 citations


Authors

Showing all 1738 results

NameH-indexPapersCitations
Raghunath Sahoo10655637588
Biswajeet Pradhan9873532900
A. Kumar9650533973
Franco Meddi8447624084
Manish Sharma82140733361
Anindya Roy5930114306
Krishna R. Reddy5840011076
Sudipan De549910774
Sudip Chakraborty513439319
Shaikh M. Mobin5151511467
Ashok Kumar5040510001
Ankhi Roy492598634
Aditya Nath Mishra491397607
Ram Bilas Pachori481828140
Pragati Sahoo471336535
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202365
2022253
2021914
2020801
2019677
2018614