5篇
您的检索式：作者名="Arjun Jain"
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | B4显示文摘 | Sushant Jain Alok Kumar Subhasree Mandal Joon Ong Leon Poutievski Arjun Singh Subbaiah Venkata Jim Wanderer Junlan Zhou Min Zhu Jon Zolla Urs H?lzle Stephen Stuart Amin Vahdat | 2013 | ACM SIGCOMM Computer Communication Review2013，，： | 1 |
| 2 | Endothelin-1: a key pathological factor in pre-eclampsia?显示文摘 | Arjun Jain | 2012 | Reproductive BioMedicine Online2012，，5： | 1 |
| 3 | Exploring Shape Variations by 3D‐Model Decomposition and Part‐based Recombination显示文摘 | Arjun Jain Thorsten Thorm?hlen Tobias Ritschel Hans‐Peter Seidel | 2012 | Computer Graphics Forum (pt)2012，，2： | 1 |
| 4 | Investigating the specificity of endothelin-traps as a potential therapeutic tool for endothelin-1 related disorders显示文摘BACKGROUND Endothelin(ET)-traps are Fc-fusion proteins with a design based on the physiological receptors of ET-1.Previous work has shown that use of the selected ET-traps potently and significantly reduces different markers of diabetes pathology back to normal,non-disease levels.AIM To demonstrate the selected ET-traps potently and significantly bind to ET-1.METHODS We performed phage display experiments to test different constructs of ET-traps,and conducted bio-layer interferometry binding assays to verify that the selected ET-traps bind specifically to ET-1 and display binding affinity in the double-digit picomolar range(an average of 73.8 rM,n=6).RESULTS These experiments have confirmed our choice of the final ET-traps and provided proof-of-concept for the potential use of constructs as effective biologics for diseases associated with pathologically elevated ET-1.CONCLUSION There is increased need for such therapeutics as they could help save millions of lives around the world. | Arjun Jain Kristof Bozovicar Vidhi Mehrotra Tomaz Bratkovic Martin H Johnson Ira Jha | 2022 | World Journal of Diabetes2022，13，6： | 0 |
| 5 | Artificial neural network based modeling of liquid membranes for separation of dysprosium显示文摘In recent years,the liquid membrane process has been widely investigated to remove rare earth metals.However,transport modeling of this process requires the accurate values of several parameters,which are difficult to measure.Thus,the accurate simulation of this process is a challenging task.In this study,the artificial neural network(ANN)based approach is used to model the liquid membrane process for removing dysprosium.Experimental results from a previous study were used to train the ANN.Initially,the number of neurons in the hidden layer was optimized.The minimum mean squared error between experimental results and model predictions is found with ten neurons.Model predictions were successfully validated with experimental results with correlation factor(R)of 0.9987,which confirms the authenticity of the trained network.Trained ANN was then used to study the effects of different operating parameters on transport rate.The higher volume ratio of membrane solution to feed solution(3-4)with 50-60 min of operation,higher feed pH(5),HCl concentration in stripping solution of 2 mol/L,and moderate concentration of carrier species(0.5 mol/L)with 0.5×10^(-4) mol/L dysprosium initial concentration are found to be optimum values of operating conditions for maximizing the transport rate. | Jawad Iqbal Arjun Tyagi Manish Jain | 2023 | Journal of Rare Earths2023，41，3： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员