Sentence examples for be valuable in selecting from inspiring English sources
Detailed structural and property studies suggest that the chemical structure motif of reactant (HNCCSH) determines the optical properties of CA-derived C-dots, which would be valuable in selecting efficient reactants for preparing highly fluorescent CA-derived C-dots.
Besides adapting the empirically initiated antimicrobial therapy, culture results provide insights into local epidemiological patterns, which can be valuable in selecting the proper agents in the empirical phase.
In cN0 head and neck squamous cell carcinoma (HNSCC), use of the sentinel lymph node (SLN) technique has proved to be valuable in selecting pN+ patients for therapeutic neck dissection while sparing unneeded extended neck surgery for pN0 patients.
As discussed above, biomarkers can be valuable in selecting subjects for clinical trials and for therapeutic interventions, for reducing unexplained variance and thus improving statistical power, and for establishing proof of concept in early phase drug development.
Knowledge about factors associated with poor prognosis could be valuable in selecting patients for more intensive monitoring and treatment, in order to further improve outcome.
Bronchography can be valuable in selected cases as a supplement to bronchoscopy and MDCT to clarify anatomy and function.
Therefore, understanding the performance in hydrothermal pretreatment and enzymatic hydrolysis of select species harvested from a mixed prairie is valuable in selecting these components for such applications.
Readers would never know, for example, how often it is valuable in selecting among psychiatric diagnoses.
Bone scintigraphy using technetium-99m methylene diphosphonate (99mTc-MDP) is valuable in selecting patients for radium-223 treatment because both radiotracers, radium-223 and 99mTc-MDP, have a similar calcium-mimicking affinity for the bone mineral hydroxyapatite associated with bone metastasis.
9 This latex agglutination assay could be valuable in correctly identifying select agents and excluding closely related Burkholderia species that cause similar disease in humans.
Our results show that machine learning approach may be valuable in diagnosing subtypes of narcolepsy and selecting new clinical features of narcolepsy on machine scale.
