Map of the bootstrap ratios for the 161 genes, analyzed with DiCA, grouped per family and per class of neuronal function.

<p>In blue are represented genes with significant positive bootstrap ratios (<i>BT</i>>4.00) associated with the VSA ring and in red genes with significant negative bootstrap ratios (<i>BT</i><–4.00) associated with the PTF ring. For each family, extreme genes are identified. These genes are the most preferentially expressed in either VSA or PTF.</p

The 161 genes coding for proteins forming ionic channels or involved in neurotransmitter release.

<p>In the analysis of the genes the most differentially expressed on the cortex we found genes belonging to ionic channels and neurotransmitter release neuronal function classes. In particular these genes belong to six families of genes (KCN, SCN, CACN, SYT, CPLX, and VAMP). The members of these six families (when we take all the members for each family) sum up to a total of 161 genes. The table gives the number of genes per family and the number of genes per class.</p><p>The 161 genes coding for proteins forming ionic channels or involved in neurotransmitter release.</p

Summary of the temporal properties of proteins that most differentiate between the two rings and their correspondence with the preferred information processing modes of the rings.

<p>Summary of the temporal properties of proteins that most differentiate between the two rings and their correspondence with the preferred information processing modes of the rings.</p

DiCA analysis: regions factor scores histogram.

<p>We plot the histogram of the factor score values–obtained for the 394 regions by the DiCA analysis–as a function of the number of regions <i>a priori</i> assigned to the VSA (blue) or the PTF (red) ring.</p

Scatter plot of the gene factor scores of the discriminant dimension extracted by the DiCA analyses performed on the 161 genes measured on Specimens H0352001 (horizontal) and H0352002 of ABA.

<p>Each dot represents one of the 161 genes. The coefficient of correlation is equal to .90 (<i>r</i>² = .81, <i>p</i><.001).</p

The two intertwined rings corresponding to different type of temporal processing.

<p>The VSA ring (in blue) corresponds to high fidelity evoked processing and the PTF ring (in red) corresponds to more spontaneous processing more independent of input action potentials timing (from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0115913#pone.0115913-Mesmoudi1" target="_blank">[3]</a>). Cortical regions sampled by the Allen Institute for Brain Sciences (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0115913#pone.0115913-Hawrylycz1" target="_blank">[7]</a>) are represented by spheres colored like their respective rings. Points in sulci are not visible.</p

Heatmap representing the correlations between the 938 genes used in DiCA.

<p>The correlation coefficients between genes were computed using all 394 regions. The genes are ordered according to their positions on the dimension extracted by DiCA. Red to magenta colors denote strong positive correlations whereas green denotes a strong negative correlation. The genes are clearly organized into two blocks that are related to the gene differential expression for the two rings.</p

Differential distribution of gene expression: CA analysis.

<p><b>a)</b> Dimensions 1 and 2 as extracted by a CA performed on the 394 regions and the 938 genes. The dots represent the factor scores for the regions; each dot is colored in red or blue depending on the represented region localization within (respectively) PTF or VSA. The eigenvalue of Dimension 1 (λ₁ = 4.59^–03) represents 29% of the total variance, The eigenvalue of Dimension 2 (λ₂ = 1.51^–03) represents 9% of the total variance. <b>b)</b> The localization of the cortical regions on the brain with PTF and VSA rings colored in, respectively, dark red and dark blue. Light red dots represent regions with significant negative factor scores (<i>BT</i><–2.00) whereas light blue dots represent regions with significant positive factor scores (<i>BT</i>>2.00).</p

Predicting outcomes following lower extremity open revascularization using machine learning

Lower extremity open revascularization is a treatment option for peripheral artery disease that carries significant peri-operative risks; however, outcome prediction tools remain limited. Using machine learning (ML), we developed automated algorithms that predict 30-day outcomes following lower extremity open revascularization. The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity open revascularization for chronic atherosclerotic disease between 2011 and 2021. Input features included 37 pre-operative demographic/clinical variables. The primary outcome was 30-day major adverse limb event (MALE; composite of untreated loss of patency, major reintervention, or major amputation) or death. Our data were split into training (70%) and test (30%) sets. Using tenfold cross-validation, we trained 6 ML models. Overall, 24,309 patients were included. The primary outcome of 30-day MALE or death occurred in 2349 (9.3%) patients. Our best performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve (95% CI) of 0.93 (0.92-0.94). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.08. Our ML algorithm has potential for important utility in guiding risk mitigation strategies for patients being considered for lower extremity open revascularization to improve outcomes. </p

Predicting outcomes following lower extremity open revascularization using machine learning

Lower extremity open revascularization is a treatment option for peripheral artery disease that carries significant peri-operative risks; however, outcome prediction tools remain limited. Using machine learning (ML), we developed automated algorithms that predict 30-day outcomes following lower extremity open revascularization. The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity open revascularization for chronic atherosclerotic disease between 2011 and 2021. Input features included 37 pre-operative demographic/clinical variables. The primary outcome was 30-day major adverse limb event (MALE; composite of untreated loss of patency, major reintervention, or major amputation) or death. Our data were split into training (70%) and test (30%) sets. Using tenfold cross-validation, we trained 6 ML models. Overall, 24,309 patients were included. The primary outcome of 30-day MALE or death occurred in 2349 (9.3%) patients. Our best performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve (95% CI) of 0.93 (0.92-0.94). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.08. Our ML algorithm has potential for important utility in guiding risk mitigation strategies for patients being considered for lower extremity open revascularization to improve outcomes. </p