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Engineered Nanotopography to Modulate Growth and Signaling of Neuronal Networks

Advanced Functional Materials, EarlyView.
This study examines the interaction between neurons and nanoscale topographies on engineered substrates. Neurons cultured on nanowires, nanopillars and nanoneedles exhibit enhanced neurite outgrowth, form more organized neuronal circuits, and display improved synaptic connectivity, compared to flat substrates.
Hao Nguyen Tran, Kalyan Shobhana, Zlatan Trifunovic, Dan Smith, Michael Stuiber, Chennupati Jagadish, Mirella Dottori, Andrea J. O'Connor, Vini Gautam   +8 more
wiley   +1 more source

Modeling and Optimization of α‑Amylase Immobilization on Chitosan-Based Supports: A Comparison of Response Surface Methodology and Machine Learning Approaches. [PDF]

ACS Omega
Birdal B, Bahçıvan B, Akbulut K, Tarlak F, Binay B.   +4 more
europepmc   +1 more source

Engineering Optimized Nanostructured Lipid Carriers for Astaxanthin: A Response Surface Methodology Approach. [PDF]

Int J Nanomedicine
Abdol Wahab NR, Meor Mohd Affandi MMR, Fakurazi S, Alias E, Hassan H.   +4 more
europepmc   +1 more source

Optimization of enzyme-assisted extraction of polyphyllins from paris polyphylla var. yunnanensis rhizomes using response surface methodology. [PDF]

Sci Rep
Dong L, Long P, Jin Y, Chen L, He X, Sun R.   +5 more
europepmc   +1 more source

Optimization of antioxidant extraction from Citrullus colocynthis seed using response surface methodology. [PDF]

Sci Rep
Hoffola AA, Robi AG, Tefera ZT, Abera WG.   +3 more
europepmc   +1 more source

Optimization of Wild Blackthorn (<i>Prunus spinosa</i> L.) Purée Drying: Effect of Egg Albumen Concentration Using Response Surface Methodology. [PDF]

Foods
Măntăilă S, Balan N, Cotârleț M, Nistor OV, Andronoiu DG, Mocanu DG.   +5 more
europepmc   +1 more source

Optimizing brown rice liquefaction and saccharification using response surface methodology for grain ethanol production. [PDF]

Sci Rep
Jang SW, Yu HH, Kim JC, Jang M.
europepmc   +1 more source

Response Surface Methodology

, 2020
Response Surface Methods (RSMs) are statistical and numerical models that approximate the relationship between multiple input variables and an output variable. This chapter introduces the methodology and its importance for engineer- ing design optimisation. The basic steps to build RSMs and validate the model accuracy are explained.
Péter Zénó Korondi, Mariapia Marchi, Carlo Poloni   +2 more
core   +4 more sources

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Response Surface Methodology

Journal of Quality Technology, 1996
(1996). Response Surface Methodology. IIE Transactions: Vol. 28, Scheduling and Logistics, pp. 1031-1032.
Raymond H. Myers, Douglas C. Montgomery
openaire   +2 more sources

Response Surface Methodology

2004
Abstract : There is a problem faced by experimenters in many technical fields, where, in general, the response variable of interest is y, and there is a set of predictor variables x1, x2,...xk. For example, in Dynamic Network Analysis (DNA) Response Surface Methodology (RSM) might be useful for sensitivity analysis of various DNA measures for different
Jeff Reminga, Natalia Y. Kamneva, Kathleen M. Carley   +2 more
openaire   +1 more source