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KechrisLab/ComBat_dogBrainMRI: ComBat for Dog-Brain MRI Data Harmonization
No full textThis version is what we submitted our manuscript for publication in the "Case Study" category at the F1000 Research journal
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Description S2
No full textDescription of results using only three image variables in the RF classification model (Case 1).Using only three image variables in the RF classification model (Case 1).Pre-harmonization: Total accuracy: Using only the image variables in the RF model, the lower bound total accuracy (pre-ComBat) does not differ significantly from that using only three clinical covariates (Case 0): medians 60.5% vs. 57.9%; p-value = 0.270. However, the upper bound total accuracy is significantly higher than that in Case 0: medians 65.8% vs. 57.9%; p-value = 4.06 E-07 (Figure S5-A). Sensitivity: Using only the image variables in the RF model, the lower bound sensitivity (pre-ComBat) is significantly higher than that using only three clinical covariates (Case 0): medians 47.4% vs. 42.1%; p-value = 9.68 E-04. Similarly, the upper bound sensitivity is also significantly higher than that in Case 0: medians 52.6% vs. 47.4%; p-value = 6.58 E-04 (Figure S5-B).Specificity: Using only the image variables in the RF model, interestingly, the lower bound specificity (pre-ComBat) is significantly lower than that using only three clinical covariates (Case 0): medians 68.4% vs. 73.7%; p-value = 3.31 E-03. However, the upper bound specificity is significantly higher than that in Case 0: medians 78.9% vs. 73.7%; p-value = 5.67 E-05 (Figure S5-C).Post-harmonization: Total accuracy: Post-ComBat harmonization (scenarios a, b), the total accuracy lower bounds are significantly higher compared to their pre-ComBat and Case 0 counterparts. For example, post-ComBat with only three image variables (scenario a): (1) vs. pre-ComBat: medians 65.8% vs. 60.5%; p-value = 2.64 E-08 (Tables 4 and S4, Figures 2A and S5-A) and (2) vs. using only the clinical covariates (Case 0): medians 65.8% vs. 57.9%; p-value = 4.98 E-08 (Figure S5-A). Sensitivity: Post-ComBat harmonization (scenarios a, b), the sensitivity lower bounds are significantly higher compared to their pre-ComBat and Case 0 counterparts. For example, post-ComBat with only three image variables (scenario a): (1) vs. pre-ComBat: medians 57.9% vs. 47.4%; p-value = 4.33 E-08 (Table S4 and Figure S5-B) and (2) vs. using only the clinical covariates (Case 0): medians 57.9% vs. 42.1%; p-value = 7.88 E-11 (Figure S5-B).Specificity: Post-ComBat harmonization (scenarios a, b), the specificity lower bounds are significantly higher compared to their pre-ComBat counterparts. For example, post-ComBat with only three image variables (scenario a) vs. pre-ComBat: medians 73.7% vs. 68.4%; p-value = 1.16 E-03 (Table S4 and Figure S5-C). Interestingly though, these post-ComBat lower bounds are not significantly higher than that using only the clinical covariates (Case 0): all three medians 73.7%; p-values (scenarios a, b vs. Case 0) = 0.347 and 0.359 (Figure S5-C).</div
Figure S1-B
No full textBoxplots of means (across up to three slices) of normalized standard deviation of signal intensities measured on 244 subjects distributed across four subpopulation
Figure S1-A
No full textBoxplots of means (across up to three slices) of normalized mean of signal intensities measured on 244 subjects distributed across four subpopulation
Description S1
No full textDescription of results using only three clinical covariates in the RF classification model (no ComBat harmonization involved).Using only the clinical covariates of the subjects in the RF model (Case 0), the lower bound total accuracies are not significantly lower than those for upper bounds: both medians = 57.9%; p-value = 0.332 (Figure S4). The lower bounds of the sensitivity and the specificity measures are also not significantly lower than those for the upper bounds: p-values 0.133 and 0.884 respectively. Thus, the distributions of the age / sex / breed-type between meningioma / glioma subjects do not vary significantly across sites. E.g., exact p-values corresponding to the Pearson’s chi-squared tests (with Yates’ continuity correction) on the two 2x2 contingency tables for sex and breedtype distributions across CSU and Outside sites are respectively 0.762 and 0.604. Also, among all scenarios, RF achieves the lowest medians of total accuracy and sensitivity in this case, which indicates an overall poor predictive strength of using only clinical covariates in the RF model.</div
Figure S2-A
No full textBoxplots of means (across up to three slices) of normalized mean of signal intensities measured by two processors (“XY” and “DN”) on 244 subjects distributed across four subpopulations: GC = “Glio-CSU”, MC = “Menin-CSU”, GO = “Glio-Out”, MO = “Menin-Out
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