Here, rankZ is the rank of Z, which means t corrupted errors can make 2t dimension change at most even though the network is not known at all according to the space metric.
Bars indicate standard error of the mean (t -test,* P < 0.01, difference from Col-0).
The p-values of the test H0: mean metric) non-commuting equals mean metric) commuting--where metric can be timeliness, t error, x error, or y error.
The subjects who were homozygous for the G allele (GG genotype) in the GRM7 rs37952452 polymorphism had higher mean T-scores for omission errors on the CPT than did those with the GA or AA genotype (t = 3.38, p = 0.001).
In the ADHD probands, the subjects with GG genotype in the GRM7 rs37952452 polymorphism had higher mean T-scores for omission errors on the CPT than did those with the GA or AA genotype (t = 3.38, p = 0.001).
For the total sample having no missing data (n = 336; 185 ADHD probands and 151 controls), the subjects with the GG genotype in the GRM7 rs37952452 polymorphism also showed higher mean T-scores for omission errors on the CPT than did those with the GA or AA genotype (t = 2.17, p = 0.034).
Error bars denote standard error of the mean (t-test *p ≤ 0.05, **p ≤ 0.01, treatment vs vehicle).
Aβ: β-amyloid peptide; Aβ42: AD1-42; Alzheimerimer's disease; APH-1: anterior pharynx-defective 1; BACE1: beta-site APP cleaving enzyme 1; CTF: C-terminal fragment; CSF: Cerebrospinal fluid; NDC: Non-demented control; NTF: N-terminAPPfragment; PEN-2: Presenilin enhancleavingS1: Prenzymein-1; PS2: Presenilin-2; P-tau: Hyperphosphorylated tau; SEM: Standard error of mean; T-tau: Total tau.
mse = frac{1}{N}mathop sum limits_{a = 1}^{N} [T_{a} - P_{a} ]^{2} (3)where mse is the mean square error, T a is the ath target (experimental) value, P a is the ath predicted (network calculated) value and n is the number of observations.
The Bayesian optimal estimate for the state vector y t in terms of minimizing the mean square error, given t vectors sequentially sampled from the observed process—(mathbf {x}_{1 t}triangleq {mathbf {x}_{1},ldots,mathbf {x}_{t}}), is obtained by1 hat{mathbf{y}}_{t} = Eleft[mathbf{y}_{t}|mathbf{x}_{1 t}right] = int { pleft(mathbf{y}_{t}|mathbf{x}_{1 t}right)mathbf{y}_{t}dmathbf{y}_{t}}.
