Jin Gao1,2, Weiguo Li2,3, Richard Magin3, and Danilo Erricolo1,3
1Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL, United States, 2Research Resources Center, University of Illinois at Chicago, Chicago, IL, United States, 3Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States
1Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL, United States, 2Research Resources Center, University of Illinois at Chicago, Chicago, IL, United States, 3Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States
Multicomponent Diffusion Analysis using L1-norm Regularized NNLS for an Accurate and Robust Detection of Micro-environment Alternations in Spinal Cord of SOD1G93A Mouse Model
Fig. 4. maps of summed-weights: (A) in the region ‘a.s’ (Sw,a.s) ; (B) in the region ‘a.l’ (Sw,a.l)
Fig. 3. animal results: (A) averaged weights with standard deviations from the L1-norm method in WT group (blue) and SOD group (red). Three regions are labeled as ‘a’, ‘b’ and ‘c’, and the region ‘a’ is divided into sub-region ‘a.s’ and ‘a.l’. The ‘*’ denotes a significant difference (P<0.05) is found in this region. (B) a representative fitting results from WT group (blue) and SOD group (red)