Results of Experiment # 3.

<p>The voxel space performances of the three methods are compared with their PC counterparts. We can see that, in general, relatively little or no gains were achieved by the three methods in the PC space. While LRC and the SVM showed slight improvements in performance, RLR showed slight decreases in performance.</p

Results of Experiment # 2.

<p>The structure of singular values across selected sample sizes (40, 60, 180 and 210) and iterations is shown for two dimensions (50 K and 750 K). The median values of the singular values for the 100 iterations are plotted in logarithmic scale. The additional information brought by the increase of sample size was reflected by patterns of greater singular values when sample sizes were large, with the exception of the singular values located towards the “tails”, which caused poorer kernel matrices conditioning especially for the smaller dimension (50 K).</p

Results of Experiment # 2.

<p>For fixed sample size, improvements of the linear kernels matrices conditioning were observed as the dimension increased. The effect is more apparent for large sample sizes, when the difference in kernel's conditioning across dimensions was greatest. The worse conditioned kernel matrices were observed for larger sample sizes and the smallest dimension (50 K).</p

Demographic data of the ADNI participants used in this study.

<p>Demographic data of the ADNI participants used in this study.</p

Results of Experiment # 2.

<p>This figure highlights that for a fixed dimension increases in sample sizes led to poorer conditioning of the kernels matrices (especially when the dimension was small) while at the same time as we observed in previous figures, it led to increases in classification accuracy.</p

Results of Experiment # 1.

<p>An alternative view of the results in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0044877#pone-0044877-g002" target="_blank">Figure 2</a> is presented by depicting in each panel the performance of the three methods across dimensions for a fixed sample size. It is clear that not only all methods were relatively robust to the increase of dimension, but also that their performance often improved. This was especially the case for the non-regularized LRC.</p

Results of Experiment # 1.

<p>More detailed information is provided for four sample sizes (40, 60, 180 and 210) using box plots. Each panel shows the behavior of the three methods across the selected sample sizes for a fixed dimension. For all three methods classification accuracy increased as the sample size increased. The performance of the two regularized methods (RLR and SVM) was in general very similar across all situations. Surprisingly, LRC was often very competitive, although it clearly performed worse for larger samples and lower dimensions.</p

Energetically Autonomous, Wearable, and Multifunctional Sensor

Self-powered tactile sensing is the upcoming technological orientation for developing compact, robust, and energy-saving devices in human-machine interfacing and electronic skin. Here, we report an intriguing type of sensing device composed of a Pt crack-based sensor in series with a polymer solar cell as a building block for energetically autonomous, wearable, and tactile sensor. This coplanar device enables human activity and physiological monitoring under indoor light illumination (2 mW/cm²) with acceptable and readible output signals. Additionally, the device can also function as a photodetector and a thermometer owing to the rapid response of the solar cell made from polymers. Consequently, the proposed device is multifuntional, mechanically robust, flexible, stretchable, and eco-friendly, which makes it suitable for long-term medical healthcare and wearable technology as well as environmental indication. Our designed green energy powered device therefore opens up a new route of developing renewable energy based portable and wearable systems

sj-docx-1-cpj-10.1177_00099228231200405 – Supplemental material for Association Between Child Sugary Drink Consumption and Serum Lipid Levels in Electronic Health Records

Supplemental material, sj-docx-1-cpj-10.1177_00099228231200405 for Association Between Child Sugary Drink Consumption and Serum Lipid Levels in Electronic Health Records by Ankitha Iyer, Fang-Chi Hsu, Alex Bonnecaze, Joseph A. Skelton, Deepak Palakshappa and Kristina H. Lewis in Clinical Pediatrics</p

Genome scan results of LODPAL for SBP on chromosomes 5, 7, and 13, where the true quantitative trait loci located (black, Replicate 1; pink, Replicate 2; blue, Replicate 3)

<p><b>Copyright information:</b></p><p>Taken from "Comparison of significance level at the true location using two linkage approaches: LODPAL and GENEFINDER"</p><p>http://www.biomedcentral.com/1471-2156/4/s1/S46</p><p>BMC Genetics 2003;4(Suppl 1):S46-S46.</p><p>Published online 31 Dec 2003</p><p>PMCID:PMC1866482.</p><p></p