Feature—disease association.

<p>Distributions of three features are displayed by group and by visit. Elbow angle and lifting angle show no group differences as opposed to velocity.</p

Repeatability.

<p>The first column shows the scatter plots of three features for the two assessments of controls within the same day (angles in degrees and velocity in m/s). The second column shows the Bland-Altman plots of the same two assessments. Values are colored by individual IDs of the controls. The third column displays between visit assessments for SMA patients and controls. Measurements from the same subject are connected by lines and are colored by groups.</p

Game scene.

<p>In the game scene, a visual skeleton figure represents the body of the subject. A flashing indicator and information below (pink) instruct the subject where to reach with which hand. On the upper left corner a counter and a timer are shown.</p

Feasibility of Using Microsoft Kinect to Assess Upper Limb Movement in Type III Spinal Muscular Atrophy Patients

<div><p>Although functional rating scales are being used increasingly as primary outcome measures in spinal muscular atrophy (SMA), sensitive and objective assessment of early-stage disease progression and drug efficacy remains challenging. We have developed a game based on the Microsoft Kinect sensor, specifically designed to measure active upper limb movement. An explorative study was conducted to determine the feasibility of this new tool in 18 ambulant SMA type III patients and 19 age- and gender-matched healthy controls. Upper limb movement was analysed elaborately through derived features such as elbow flexion and extension angles, arm lifting angle, velocity and acceleration. No significant differences were found in the active range of motion between ambulant SMA type III patients and controls. Hand velocity was found to be different but further validation is necessary. This study presents an important step in the process of designing and handling digital biomarkers as complementary outcome measures for clinical trials.</p></div

Learning effect.

<p>Total time spent in finishing the test for all visits is plotted with lines connecting the records from the same subject. Thick lines display the linear fit per group, with 95% confidence intervals.</p

Pearson correlations for the three features from test-retest data within the same day for controls and from between visits for controls and patients.

<p>Pearson correlations for the three features from test-retest data within the same day for controls and from between visits for controls and patients.</p

Performance summary of the 5 gene MAPK network.

<p>The first column gives the log-likelihood for each model, showing that the true network is much less likely than the inferred networks. The second and third column show performance of the networks in terms of accuracy (ACC) and area under curve (AUC). The inferred <i>p</i>₀ for the NEMix models is displayed in column four. Column five indicates the corresponding sub-figure of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004078#pcbi.1004078.g003" target="_blank">Fig. 3</a>. The network ‘KEGG Graph + Z’ denotes the structure of the known KEGG network, where only the position of <i>Z, p</i>₀, and <i>θ</i> are inferred.</p><p>Performance summary of the 5 gene MAPK network.</p

Trace plot.

<p>Movement trajectories of all 9 tracked body points in x-y dimension for a patient with a tremor and a healthy control.</p

Inferred MAPK networks on HRV infection data.

<p>Best networks of the 5 top scoring siRNAs from the MAPK pathway for HRV infection for the different compared methods are displayed. (A) shows the known KEGG pathway. (B) is the inferred NEM and (C) the sc-NEM. (D) left shows the known network with the most likely attachment of the hidden variable <i>Z</i> (blue) and (E) is the inferred NEMix. For all networks their performance is summarized in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004078#pcbi.1004078.t001" target="_blank">Table 1</a>. Subfigure (F) summarizes robustness of the MAPK network inference. For the inferred MAPK signaling networks on the HRV infection data, we assessed robustness of the accuracy for edge recovery. Box-plots display the result of 50 bootstrap samples for the three compared methods, on the 5 gene (<i>n</i> = 5) and 8 gene (<i>n</i> = 8) network.</p

NEM versus NEMix.

<p>A schematic example is shown comparing the classical nested effects model (NEM; panel <b>A</b>) with the new nested effects mixture model (NEMix; panel <b>B</b>) on six features observed in 15 individual cells. Blue nodes in the graph depict the signaling genes <i>S</i>₁, <i>S</i>₂, and <i>S</i>₃ that have been silenced and whose dependency structure is sought. The observed features <i>E</i>₁, …, <i>E</i>₆ are shown in green. Each box below the graphs indicates the observed (noisy) features (e.g., image-based read-outs) for a single cell. Within each box, dark entries indicate an effect of the knock-down on the feature, light entries indicate no effect. In cells 1 and 2 (left in both <b>A</b> and <b>B</b>), the pathway has been activated via <i>S</i>₂, whereas in cells 3, 4, and 5 (right in both <b>A</b> and <b>B</b>) it has remained inactivated. In the latter case, the effects of silencing <i>S</i>₂ are masked and the resulting silencing scheme then differs from the one where the pathway is stimulated. Classic NEMs (<b>A</b>) could explain such a heterogeneous cell population only by two different signaling graphs Φ. By contrast, with the NEMix model proposed in this work (<b>B</b>), both observed patterns can be explained by the same signaling graph Φ, because the hidden pathway stimulation <i>Z</i> (shown in red) is modeled explicitly. In the NEMix model, <i>Z</i> is a hidden binary random variable indicating pathway activation (<i>Z</i> = 1), which occurs with probability <i>P</i>(<i>Z</i> = 1) = <i>p</i>₁.</p