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Sathishkumar, Natarajan,Sathiyamoorthy, Subramaniyam,Ramya, Mathiyalagan,Yang, Dong-Uk,Lee, Hee Nyeong,Yang, Deok-Chun Informa Healthcare 2012 Journal of enzyme inhibition and medicinal chemist Vol.27 No.5
<P>Anti-apoptotic proteins such as BCL-2, BCL-XL and MCL-1 bind with pro-apoptotic proteins to induce apoptosis mechanism. BCL-2 family proteins are key regulators of apoptosis process. Over expression of these anti-apoptotic proteins lead to several cancers by preventing apoptosis. A number of studies revealed that ginseng derivatives reduce tumor growth. Ginseng, the most valuable medicinal herb found in eastern Asia belongs to Araliaceae family. In this study, docking simulations were performed for anti-apoptotic proteins with several ginsenosides from Panax ginseng. Our finding shows ginsenosides Rf, Rg1, Rg3 and Rh2 have more binding affinity with BCL-2, BCL-XL and MCL-1 and other ginsenosides also interact with each anti-apoptotic proteins. Therefore, ginseng derivatives represent a novel class of potent inhibitors and could be used for cancer chemotherapy.</P>
Sathishkumar, Yesupatham,Krishnaraj, Chandran,Rajagopal, Kalyanaraman,Sen, Dwaipayan,Lee, Yang Soo Springer-Verlag 2016 BIOPROCESS AND BIOSYSTEMS ENGINEERING Vol.39 No.2
<P>In this study, the transcriptional alterations in Penicillium chrysogenum under simulated microgravity conditions were analyzed for the first time using an RNA-Seq method. The increasing plethora of eukaryotic microbial flora inside the spaceship demands the basic understanding of fungal biology in the absence of gravity vector. Penicillium species are second most dominant fungal contaminant in International Space Station. Penicillium chrysogenum an industrially important organism also has the potential to emerge as an opportunistic pathogen for the astronauts during the long-term space missions. But till date, the cellular mechanisms underlying the survival and adaptation of Penicillium chrysogenum to microgravity conditions are not clearly elucidated. A reference genome for Penicillium chrysogenum is not yet available in the NCBI database. Hence, we performed comparative de novo transcriptome analysis of Penicillium chrysogenum grown under microgravity versus normal gravity. In addition, the changes due to microgravity are documented at the molecular level. Increased response to the environmental stimulus, changes in the cell wall component ABC transporter/MFS transporters are noteworthy. Interestingly, sustained increase in the expression of Acyl-coenzyme A: isopenicillin N acyltransferase (Acyltransferase) under microgravity revealed the significance of gravity in the penicillin production which could be exploited industrially.</P>
Sathishkumar Chinnasamy,Sivasubramanian Ramanathan 한양대학교 세라믹연구소 2020 Journal of Ceramic Processing Research Vol.21 No.1
In this work, ruthenium(II)bis(2,2'-bipyridyl4,4'-dicarboxylicacid)(pyrazine)bis(tetrafluroborate) [Ru(II)(dcbpy)2(pyz)]n(BF4)2n]based metal organic polymer (RuMOP-Pyz-1), was synthesized at room temperature under inert atmosphere and theirperformance in dye-sensitized solar cells (DSSC) were studied. The metal organic polymer was prepared by coupling pyrazineas linker units with the Ru(II) dicarboxylated based mono metallic complex. The UV-visible absorption profiles covered abroad range of absorption and the formation of polymer leads to shift in the absorption wavelength. The metal mediated π-conjugation units in the polymer complex exhibit a strong emission at λem = 552 nm with an excitation wavelength of 395 nm. The synthesized metallo-polymer was employed as a sensitizer in DSSC and the maximum power conversion efficiency (PCE)value of ɳ = 1.507 % with short circuit current (Jsc) of 3.22 mA/cm2; open circuit voltage (Voc) 0.684 V and Fill Factor (FF)of 68.45 % under Air Mass (AM) 1.5 G simulated sunlight at a light intensity of 100 mW/cm2 was obtained. The efficiencyof the device and its photovoltaic performances were found to be satisfactory. The enhanced performance of the device isattributed to the presence of extended conjugation of the metallo polymer which helps in a facile electron transfer from theHOMO to LUMO of TiO2.
Reducing the seat vibration of vehicle by semi active force control technique
Sathishkumar. P.,Jancirani. J.,Dennie John 대한기계학회 2014 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.28 No.2
This article focusses on reducing the axis acceleration and minimizing the vertical displacement by using an air spring actuator and activeforce control as a main control element. In active force control loop track the developed force of an air spring actuator is fed as afeedback to the actuator. Mamdani and sugeno type fuzzy interference system are used to develop a desired force and to estimate mass ofthe system respectively. The performance of the system is analyzed for both time and frequency domains and contrasted with passivesuspension due to the irregular road disturbances. While developing the simulation model, quarter car suspension with seat as three degreeof freedom and an air spring actuator acting as a force generator are modeled as non-linear system. The simulation result shows theeffectiveness of the proposed control scheme in suppressing the undesirable effects of the suspension system.
Green fabrication of zirconia nano-chains using novel Curcuma longa tuber extract
Sathishkumar, M.,Sneha, K.,Yun, Y.S. North-Holland 2013 Materials letters Vol.98 No.-
Bioreduction using plant materials for nanoparticle synthesis is a well known technique, but application of plant materials for hydrolytic process to synthesize nanoparticles has not much attempted earlier. One such unexplored natural material consisting of a wide range of organics is Curcuma longa tuber, which was tested for its potential use in the synthesis of zirconia nanoparticles (ZrNP). The linear chains of ZrNP formed were with an average size in the range of 41-45nm. Water soluble organics that were present in the plant materials were mainly responsible for hydrolysis of ZrF<SUB>6</SUB><SUP>2-</SUP> ions to ZrNP. The production of ZrNP was better at higher pH values. The zeta potential studies showed that the surface charge of the formed nanoparticles was highly negative and increased with increasing pH.
An Energy Consumption Prediction Model for Smart Factory Using Data Mining Algorithms
Sathishkumar V E,이명배,임종현,김유빈,신창선,박장우,조용윤 한국정보처리학회 2020 정보처리학회논문지. 소프트웨어 및 데이터 공학 Vol.9 No.5
Energy Consumption Predictions for Industries has a prominent role to play in the energy management and control system as dynamic and seasonal changes are occurring in energy demand and supply. This paper introduces and explores the steel industry's predictive models of energy consumption. The data used includes lagging and leading reactive power lagging and leading current variable, emission of carbon dioxide (tCO2) and load type. Four statistical models are trained and tested in the test set: (a) Linear Regression (LR), (b) Radial Kernel Support Vector Machine (SVM RBF), (c) Gradient Boosting Machine (GBM), and (d) Random Forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used for calculating regression model predictive performance. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set.
Predicting Daily Nutrient Water Consumption by Strawberry Plants in a Greenhouse Environment
( Sathishkumar V E ),( Myeong-bae Lee ),( Jong-hyun Lim ),( Chang-sun Shin ),( Chang-woo Park ),( Yong Yun Cho ) 한국정보처리학회 2019 한국정보처리학회 학술대회논문집 Vol.26 No.2
Food consumption is growing worldwide every year owing to a growing population. Hence, the increasing population needs the production of sufficient and good quality food products. Strawberry is one of the world's most famous fruit. To obtain the highest strawberry output, we worked with three strawberry varieties supplied with three kinds of nutrient water in a greenhouse and with the outcome of the strawberry production, the highest yielding strawberry variety is detected. This Study uses the nutrient water consumed every day by the highest yielding strawberry variety. The atmospheric temperature, humidity and CO2 levels within the greenhouse are identified and used for the prediction, since the water consumption by any plant depends primarily on weather conditions. Machine learning techniques show successful outcomes in a multitude of issues including time series and regression issues. In this study, daily nutrient water consumption of strawberry plants is predicted using machine learning algorithms is proposed. Four Machine learning algorithms are used such as Linear Regression (LR), K nearest neighbour (KNN), Support Vector Machine with Radial Kernel (SVM) and Gradient Boosting Machine (GBM). Gradient Boosting System produces the best results.