7) Make clear in the text that specificity comparisons with APY24 and the various commercial TKIs are based on "stock" results from invitrogen, and not an independent comparison conducted/commissioned by the authors (if this is indeed the case?) We made the appropriate changes to the text in the Methods section.
7) Make clear in the text that specificity comparisons with APY24 and the various commercial TKIs are based on "stock" results from invitrogen, and not an independent comparison conducted/commissioned by the authors (if this is indeed the case?) 8) Does the GSK compound bind an active or inactive PERK kinase conformation?
Demonstrating little responsiveness to text or emotional specificity — Fauré's "Lune Blanche" did have an aptly gauzy sheen — they passed in a blur.
These annotations are classified in specialized subsections, some written in free text ("Function", "Tissue specificity", etc).
In the case of EBOV we immediately found antiviral medicinal chemistry basic recall searches (i.e. not authentic text mining) had specificity challenges, even just associated with synonyms for EBOV, related isolates and phylogenetic neighbors (e.g. Marburg virus).
Minors: Some sentences are confused and needed to be more concise or clarified: In the case of EBOV we immediately found antiviral medicinal chemistry basic recall searches (i.e. not authentic text mining) had specificity challenges, even just associated with synonyms for EBOV, related isolates and phylogenetic neighbors (e.g. Marburg virus).
Due to the lack of ideal but practical negative control (as discussed in the text), sensitivities and specificities calculated in this study are based on the practical negative controls in group B and C. TP, true positive; FN, false negative; TN, true negative; FP, false positive, LP, lamina propria, SE, surface epithelium.
We found that high abstract similarity can be used to predict high full text similarity with a specificity of 20.1% (95% CI [17.3%, 23.1%]) and sensitivity of 99.999%.
Also based on these numbers, using high abstract similarity to predict high full text similarity yields a specificity of 20.1% (95% CI [17.3%, 23.1%]), sensitivity of 99.999%, and false negative rate of 1.2E-5 (95% CI [1.1E-5, 1.3E-5]).
The distribution curves of the two groups intercept in the (0.55, 0.6) range, suggesting that a value in this range may serve as an abstract similarity threshold by which one can predict high full text similarity with good specificity and sensitivity.
For most searches, the input method is a bit of a blunt instrument, lacking the specificity of a text or voice-based search.
