Sentence examples for summarised visually from inspiring English sources

Results for the primary outcome measures at all time-points are also summarised visually in Figs.  3, 4 and 5.

A diagram to summarise visually the various inputs to the aetiology of dental anomalies is given in Fig. 4. Competing interests: None declared.

Results on demographic transitions, as detected by chloroplast SSRs, are shown in table 4 and visually summarised in Figure 2 e), and show global trends that tend to exclude bottlenecks: no species or population displayed signatures of population contraction.

The architecture combines a data query, processing technique and visualisation methodology to rapidly integrate and visually summarise properties of a dataset.

Figure 1 summarises the results visually for the other three scaling assumption tests.

*X chromosome not modelled in simulated populations In order to visually summarise the data presented in table 4 we plotted the derived allele frequency against genomic position over the entire genome, we also contrasted this with Fst at the same positions.

Wall motion abnormalities were visually analysed and summarised in a wall motion score [ 18].

In order to provide an overview, the procedures followed are summarised below: The speech signal was examined visually and the presence of vowels was established by whether a periodic waveform corresponded to formant structure and vertical voicing striations in the spectrogram, together with an F0 trace.

The proportions of participants with different categories of VA impairment according to WHO definitions 14 and the driving standard in the UK 15, are presented in table 3. The causes of impairment in those classified as blind or visually impaired by WHO criteria are summarised in table 4.

The vessel forms and visually identified colours of the sampled glasses are summarised in Table 1.

Model discrimination was summarised by the area under the receiver operator characteristic (ROC) curve (AUC) and was visually displayed using simple histograms of density functions, which show the distribution and overlap in predicted probabilities generated from the logistic regression models [ 52].