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1                                                             Variables collected over V1-V4 were measured as; day of (DO)
2                      The random forest procedure selected 6 variables (not growth phase) for inclusion in the logistic mo
3 rd stepwise selection conducted on the model containing all variables that were significant at the 0.2 level in the univa
4  were used to examine the relationship between diabetes and variables associated with CRC risk and ADR.
5                     When adjusted for prespecified baseline variables, the odds ratio for 90-day mortality was 0.82 (95%
6 cruitment, (c) separately analyzing physical and biological variables fails to identify the significant association for w
7 le difference in the average measures for all the biometric variables between devices.
8 od to two empirical datasets containing largely categorical variables: an anthropological survey of rice farmers in Bali
9 e factors-climate heterogeneity, collinearity among climate variables, and spatial scale-interact to shape the spatial st
10                            On the basis of various clinical variables, patients with corresponding (68)Ga-PSMA-11 PET/CT
11 of disease severity assessing the correlation with clinical variables, cross-sectional brain imaging and neurophysiologic
12  of methylation probes which are correlated with confounder variables reduces the error of inference by 30-35%, and that
13                The vmPFC signals a multiplicity of decision variables, the strength and polarity of which vary with behav
14 on priorities, food production potential and socio-economic variables likely to influence the success of land sparing.
15 ent and, as we show, cannot accommodate general environment variables, modest sample sizes, heterogeneous noise, and bina
16                                           Some experimental variables such as pH, DES solvent type and volume, aprotic so
17 ce of a high degree of collinearity between the explanatory variables.
18 uture studies should use standard, clearly defined exposure variables to strengthen understanding of the relationship bet
19                                  In total, 22 new geriatric variables were imported from the ACS NSQIP geriatric pilot st
20            No associations were found for any immunological variables after 1 year of ART.
21 ization (MR) is the use of genetic variants as instrumental variables to infer the causal effect of a specific risk facto
22 a method able to handle both the high number of interacting variables and the noise in the available heterogeneous experi
23 g how the brain represents and computes with dynamic latent variables.
24 However, the interpretation of test results depends on many variables and factors, including sensitivity, specificity, po
25 effect to adjust for within-school clustering, minimisation variables, baseline cluster-level score of the outcome, and s
26 l common currency of information theoretic and motivational variables are discussed.SIGNIFICANCE STATEMENT Agency is a ce
27 ch a trained model relies on conceptually related groups of variables, such as frequency bands or regions of interest in
28 were collected and descriptive statistics were performed on variables of interest.
29 estionnaires for collection of data on medication and other variables.
30 metry was modeled from experimentally derived pharmacologic variables.
31 associations of MC1R genetic risk categories and phenotypic variables and genetic ancestry.
32 7) and then examine how a set of environmental and physical variables affect the stability of these communities.
33 ownscaling to calibrate measurements and projected physical variables, (b) physical drivers are statistically significant
34                           Using all pre- and posttransplant variables until 3 and 12 months (n = 65), the obtained models
35                                                   Predictor variables included gender, calendar year, geography, years si
36 ing, the distribution of baseline clinical and radiological variables was similar across the two patient groups.
37 ariate linear regressions were conducted with sleep-related variables as explanatory and subsequent changes in BW, BF%, a
38 t related to any of the psychological life history-relevant variables measured (including short- vs. long-term sexual str
39 ume loops, invasive pressures, diuretic output, respiratory variables, and blood analysis.
40 quent changes in BW, BF%, and metabolic markers as response variables.
41 he RAS-B system, while flow affected certain coral response variables in the FTS tanks; there were few effects of light o
42 her work defining what number and which combination of risk variables works best for predicting risk of dementia in LMICs
43 ion method to impute missing data and then fed the selected variables to multiple machine learning models.
44  variation of this clade, secondly, relate stress and shape variables, and finally, to classify fossil individuals into b
45                                                 Significant variables associated with the occurrence of periodontitis in
46          In this study, we analyzed highly resolved spatial variables in cities, together with case count data, to invest
47  practices) and individual-level (perceptions and symptoms) variables.
48 on of adulterants was possible through a PLS model with the variables selected by iPLS.
49                                                       These variables, in turn, systematically influence which of opposit
50  growth phases of 4469 Scottish Blackface sheep and weather variables during the same period to derive novel resilience p