Bridge centrality indexes (standardized <i>z</i>-scores) calculated in each partial correlation network.

MDD = major depressive disorder, PD = panic disorder, SAD = social anxiety disorder, OCD = obsessive-compulsive disorder. MDD1 = cognitive/affective symptoms, MDD2 = somatic symptoms, PD1 = physical concerns, PD2 = cognitive concerns, PD3 = social concerns, OCD1 = hoarding, OCD2 = checking, OCD3 = ordering, OCD4 = neutralizing, OCD5 = washing, OCD6 = obsessing.</p

Centrality indexes (standardized <i>z</i>-scores) calculated in each partial correlation network.

MDD = major depressive disorder, PD = panic disorder, SAD = social anxiety disorder, OCD = obsessive-compulsive disorder. MDD1 = cognitive/affective symptoms, MDD2 = somatic symptoms, PD1 = physical concerns, PD2 = cognitive concerns, PD3 = social concerns, OCD1 = hoarding, OCD2 = checking, OCD3 = ordering, OCD4 = neutralizing, OCD5 = washing, OCD6 = obsessing.</p

The symptom-profile of patient clusters identified by GGMM (left panel) and the covariance symptom network estimated for each cluster (right panel).

The thickness of edges in the symptom networks reflects the values of covariances. MDD = major depressive disorder, PD = panic disorder, SAD = social anxiety disorder, OCD = obsessive-compulsive disorder. MDD1 = cognitive/affective symptoms, MDD2 = somatic symptoms, PD1 = physical concerns, PD2 = cognitive concerns, PD3 = social concerns, OCD1 = hoarding, OCD2 = checking, OCD3 = ordering, OCD4 = neutralizing, OCD5 = washing, OCD6 = obsessing.</p

Correlations between factor scores of depressive and anxiety symptoms.

MDD = major depressive disorder, PD = panic disorder, SAD = social anxiety disorder, OCD = obsessive-compulsive disorder. MDD1 = cognitive/affective symptoms, MDD2 = somatic symptoms, PD1 = physical concerns, PD2 = cognitive concerns, PD3 = social concerns, OCD1 = hoarding, OCD2 = checking, OCD3 = ordering, OCD4 = neutralizing, OCD5 = washing, OCD6 = obsessing.</p

Participants’ allocations to the Gaussian graphical mixture model (GGMM)-based clusters (<i>N</i> = 1,521).

Participants’ allocations to the Gaussian graphical mixture model (GGMM)-based clusters (N = 1,521).</p

Partial correlation network estimated for each patient cluster.

Nodes represent symptoms, and edges represent partial correlations between them. The thickness of an edge reflects the absolute value of the regularized partial correlation. Blue and red edges represent positive and negative regularized partial correlations, respectively.</p

Light-Driven Locomotion of Bubbles

Remotely controlling the movement of small objects is a challenging research topic, which can realize the transportation of materials. In this study, remote locomotion control of particle-stabilized bubbles on a planar water surface by near-infrared laser or sunlight irradiation is demonstrated. A light-induced Marangoni flow was utilized to induce the locomotion of the bubbles on water surface, and the timing and direction of the locomotion can be controlled by irradiation timing and direction on demand. The velocity, acceleration, and force of the bubbles were analyzed. It was also confirmed that the bubbles can work as light-driven towing engines to pull other objects. Furthermore, it was demonstrated that the bubbles can work as an adhesive to bond two solid substrates by application of compressive stress under water. Such remote transport of the materials, pulling of the objects by light, and controlling the release of gas on demand should open up a wide field of conceivable applications

Light-Driven Locomotion of Bubbles

Remotely controlling the movement of small objects is a challenging research topic, which can realize the transportation of materials. In this study, remote locomotion control of particle-stabilized bubbles on a planar water surface by near-infrared laser or sunlight irradiation is demonstrated. A light-induced Marangoni flow was utilized to induce the locomotion of the bubbles on water surface, and the timing and direction of the locomotion can be controlled by irradiation timing and direction on demand. The velocity, acceleration, and force of the bubbles were analyzed. It was also confirmed that the bubbles can work as light-driven towing engines to pull other objects. Furthermore, it was demonstrated that the bubbles can work as an adhesive to bond two solid substrates by application of compressive stress under water. Such remote transport of the materials, pulling of the objects by light, and controlling the release of gas on demand should open up a wide field of conceivable applications

Light-Driven Locomotion of Bubbles

Remotely controlling the movement of small objects is a challenging research topic, which can realize the transportation of materials. In this study, remote locomotion control of particle-stabilized bubbles on a planar water surface by near-infrared laser or sunlight irradiation is demonstrated. A light-induced Marangoni flow was utilized to induce the locomotion of the bubbles on water surface, and the timing and direction of the locomotion can be controlled by irradiation timing and direction on demand. The velocity, acceleration, and force of the bubbles were analyzed. It was also confirmed that the bubbles can work as light-driven towing engines to pull other objects. Furthermore, it was demonstrated that the bubbles can work as an adhesive to bond two solid substrates by application of compressive stress under water. Such remote transport of the materials, pulling of the objects by light, and controlling the release of gas on demand should open up a wide field of conceivable applications