Sensorimotor adaptation of voice fundamental frequency in Parkinson's disease

randomly generated short speech intelligibility test

Clinical prediction models for mortality and functional outcome following ischemic stroke: A systematic review and meta-analysis

systematic reviews of prediction modelling
predictor variables during multivariate modelling
discrimination of externally validated model
confidence interval overlapthe ssv 36 model

ing 66 pms estimating stroke functional outcome mortality n=27

The Camden & Islington Research Database: Using electronic mental health records for research

Adaptogenic potential of royal jelly in liver of rats exposed to chronic stress

Enhanced susceptibility to stress and seizures in GAD65 deficient mice

containing 20 equally spaced holes along its perimeter platform diameter=122 cm, hole diameter=10
0=no GFP^+ cells migrated hilus; 1=1-25 GFP^+ cells migrated; 2<25
n=20 exhibited marked behavioral impairments compared GAD65
GAD65-/- mice transplanted n=10 sham n=6; t, p<0.01
n=20 exhibited stress induce seizures during open fielding. By 8

Individual differences in compliance and agreement for sleep logs and wrist actigraphy: A longitudinal study of naturalistic sleep in healthy adults

long term study design afforded 3 complementary analysis
pittsburgh sleep diary items allowed estimation
percentage of data contained within specific reference intervals
data being contained within smaller difference intervals
measured pearson product moment correlation coefficient
empirical data contained within 3 specific reference intervals

An EGR performance evaluation and decision-making approach based on grey theory and grey entropy analysis

pone.0191626.e018">removedGRAPHIC r i=1 N ∑ k=1
pone.0191626.e029">removedGRAPHIC E + eta k=∑ j=1 m ∑ k=1 s e + (r 1 j k, v 1 k +) eta k E - eta k=∑ j=1 m ∑ k=1 s e - (r 1 j k, v 1
pone.0191626.e030">removedGRAPHIC (P ¯) { min E + eta k=∑ j=1 m ∑ k=1
pone.0191626.e031">removedGRAPHIC H ⊗ eta=-∑ i=1
pone.0191626.e032">removedGRAPHIC (P ' ¯) { max H ⊗ eta=-∑ i=1 s eta i ln eta i s. t. ∑ k=1 s eta k=1, eta k<0, k=1, 2
pone.0191626.e035">removedGRAPHIC min { μ ∑ j=1 m ∑ k=1
k + (1 -2 μ) ∑ i=1 s eta i ln eta i } s. t. ∑ k=1 s eta k=1, eta k<0, k=1, 2
k + (1 -2 μ) - ∑ i=1 s eta k ln eta k - lambda (∑ k=1 s eta k -1
pone.0191626.e038">removedGRAPHIC eta 1 k=eta k eta 0 k ∑ k=1