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Unfortunately, those methods gave poor performance on tweets, as pointed out in [31].
These three methods gave poor q values for THR and GST, with the DSM-MTS method giving the worst q values for GST. Figure 2 shows the database enrichment curves obtained by the MSM-MTS method, the ML-DSI method, the consensus set, and the sum set.
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However, testing this method gave poor results, with severe noise and object fragmentation.
The MTS and DSM-MTS methods gave good q values for COX2, while the CS method gave poor q values for COX2.
In general, the singular-field approach based on 2D elasticity and the finite element method gives poor predictions of fracture toughness.
Although this method gives poor accuracy in the energy and the frequency compared with the ab initio method, the results of PM6 method can be considered only as a qualitative estimate of how EDA binds to CB[7].
The expected sensitivity of SPEDRE to input noise has not yet been confirmed in the tests shown (and in other tests, such as Supplementary Table S12); rather we found that all methods gave unacceptably poor answers with noisy data.
In fact, for SIMFMZ these methods gave the poorest results (see Fig. 7a, green and light blue symbols) and frequently motion correction failed completely.
Despite being very fast, image-independent methods usually give poor results since they do not take into account the image contents.
Other classical methods of calibration such as those using absolute values of absorbance, initial rates, etc. gave poor results in the cases considered.
However, FA fixation gave poor results in preserving neuronal tissues.
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