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This work has been able to recall all data stored in the ProcAuxInfo correctly, though has in two instances suffered from slight data loss due to issues of the underlying current InChI implementation.
In the current paper, data from this study was extracted from one of the original papers reporting Rotterdam I study [ 28] rather than from a previous meta-analysis [ 19], thereby leading to a slight data variation.
The study presented here concentrated on a number of normalization techniques and shows that different normalization methods yield different results, thus the normalization method should be selected in accordance to the data set under investigation with the general guideline 'enough' normalization with only slight data modification.
In one extreme, convergence to very different solutions under slight data perturbation that make no biological sense would indicate a problem in the modeling approach or with the ability to overcome local minima, while, on the other hand, consistent convergence to a single solution would suggest a distinct global optimum under the modeling assumptions.
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Although the trend was slight, our data suggest that students with lower math scores might receive additional benefits from viewing class demonstrations, especially those that address quantitative physics concepts.
When more orthogonal components were allowed into the OPLS-R model, the dark roast again shifted to a higher caffeine concentration, beautifully indicating the presence of slight overfitting (data not shown).
Importantly, both the rescue and the worsening phenotypes were sensitive to the dose of EDARADD mRNA injected, with 50 and 100 pg of EDARADD mRNA producing significant and equivalent effects whereas 25 pg EDARADD mRNA leading only to slight effects (data not shown).
Although there were no statistically significant differences between the followed up sample and the participants lost according to age, sex, level of education and smoking status, the final sample skewed as slighter older (data not shown).
If minimal number of replicates is not used, outlier removal has a slight impact on data quality (data not shown).
In the New York area, however, that increase was slight, and the data does not include information on condos, co-ops or new developments, which make up most of the Manhattan market.
Lossless compression techniques compress without loss of any information but have low compression rate, and lossy compression techniques can compress at high compression ratio but with a slight loss of data.
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