The following equation describes the AMPA synaptic conductance (g glu) of both AMPA and NMDA receptors used in this model: (6) g g l u (t - t p r e ) = ḡ (e - (t - t p r e ) / τ d e c a y - e - (t - t p o e ) / τ r i s e ) The synaptic current for each excitatory synaptic release was then calculated as, I glu = g glu(V − V glu).
It exploits the fact that although a genetic model G(g) with epistasis depends nonlinearly on g, it only depends linearly on the interaction parameters X which specify the interaction coefficients (i.e., z and Z in Eq. (7)).
where represents an estimate of the model order d, and g(G)(P) and a(G)(P) are the geometric and arithmetic means of the P smallest global eigenvalues, respectively.
The maximal estimation errors were set as Δmax = 5 units, Δmax = 1 m, Δmax = 2 m and Δmax = 5 m for small object models G a - G c, the indoor models G d - G h, the large indoor models G i and the outdoor models G j - G k, respectively.
The shape-normalized images are then vectorized, resulting in the texture vectors g 1, …, g N. Afterwards, the very same PCA-based procedure as for the shape model is employed, which results in the linear texture model g = g ¯ + P g b g, (2).
Assume also that model g m is replaced by another model, g n, then according to (17) the mismatch of the pdf model pair, g m and g n, denoted by dmis(m,n), can be given as d mis ( m, n ) = ∫ S m ρ m g m ( x ) ln g m ( x ) g n ( x ) dx = ρ m I ( g m | | g n ) (26).
The I equations of a system model g = 0 are g 1 (F n, T n, K, p ) = p T n 1 − F n 1 − ∑ j = 1 J W 1 j ∏ i ′ = 1 I F n i ′ W i ′ j K j = 0 g i (F n, T n, K ) = T n i − F n i − ∑ j = 1 J W i j ∏ i ′ = 1 I F n i ′ W i ′ j K j = 0 (1 < i ≤ I ) where pT n 1 is the total concentration of undamaged R and F n 1 is the concentration of free R that is undamaged and thus capable of forming complexes.
The HKY + G (G = 0.36) model was selected as the best corrected Akaike Information Criterion model (AICc) using jModelTest 0.1.1 [ 54].
For other traits, no difference in accuracy was found between G BASE and G SM. Models G RR and G BL also performed similarly, and consistently better than other genomic models only for FP and PP.
Models G BASE and G SM performed similarly across all traits, with a sizable advantage of G SM only for FP.
To implement the models, G IBS-STD, G IBS-NSTD, G LA and G ROH were inverted and were then used in ASReml [ 17] to predict GEBV of both phenotyped and non-phenotyped individuals.
