Temporal Network Prediction and Interpretation

Temporal networks refer to networks like physical contact networks whose topology changes over time. Predicting future temporal network is crucial e.g., to forecast the epidemics. Existing prediction methods are either relatively accurate but black-box, or white-box but less accurate. The lack of interpretable and accurate prediction methods motivates us to explore what intrinsic properties/mechanisms facilitate the prediction of temporal networks. We use interpretable learning algorithms, Lasso Regression and Random Forest, to predict, based on the current activities (i.e., connected or not) of all links, the activity of each link at the next time step. From the coefficients learned from each algorithm, we construct the prediction backbone network that presents the influence of all links in determining each links future activity. Analysis of the backbone, its relation to the link activity time series and to the time aggregated network reflects which properties of temporal networks are captured by the learning algorithms. Via six real-world contact networks, we find that the next step activity of a particular link is mainly influenced by (a) its current activity and (b) links strongly correlated in the time series to that particular link and close in distance (in hops) in the aggregated network.Multimedia ComputingIntelligent System

A multi-axis robot-based bioprinting system supporting natural cell function preservation and cardiac tissue fabrication

Despite the recent advances in artificial tissue and organ engineering, how to generate large size viable and functional complex organs still remains as a grand challenge for regenerative medicine. Three-dimensional bioprinting has demonstrated its advantages as one of the major methods in fabricating simple tissues, yet it still faces difficulties to generate vasculatures and preserve cell functions in complex organ production. Here, we overcome the limitations of conventional bioprinting systems by converting a six degree-of-freedom robotic arm into a bioprinter, therefore enables cell printing on 3D complex-shaped vascular scaffolds from all directions. We also developed an oil bath-based cell printing method to better preserve cell natural functions after printing. Together with a self-designed bioreactor and a repeated print-and-culture strategy, our bioprinting system is capable to generate vascularized, contractible, and long-term survived cardiac tissues. Such bioprinting strategy mimics the in vivo organ development process and presents a promising solution for in vitro fabrication of complex organs.Materials and ManufacturingIndustrial Design Engineerin

Effects of Apolipoprotein E Genotype on the Off-Line Memory Consolidation

Spontaneous brain activity or off-line activity after memory encoding is associated with memory consolidation. A few recent resting-state functional magnetic resonance imaging (RS-fMRI) studies indicate that the RS-fMRI could map off-line memory consolidation effects. However, the gene effects on memory consolidation process remain largely unknown. Here we collected two RS-fMRI sessions, one before and another after an episodic memory encoding task, from two groups of healthy young adults, one with apolipoprotein E (APOE) epsilon 2/epsilon 3 and the other with APOE epsilon 3/epsilon 4. The ratio of regional homogeneity (ReHo), a measure of local synchronization of spontaneous RS-fMRI signal, of the two sessions was used as an index of memory-consolidation. APOE epsilon 3/epsilon 4 group showed greater ReHo ratio within the medial temporal lobe (MTL). The ReHo ratio in MTL was significantly correlated with the recognition memory performance in the APOE epsilon 3/epsilon 4 group but not in epsilon 2/epsilon 3 group. Additionally, APOE epsilon 3/epsilon 4 group showed lower ReHo ratio in the occipital and parietal picture-encoding areas. Our results indicate that APOE epsilon 3/epsilon 4 group may have a different off-line memory consolidation process compared to epsilon 2/epsilon 3 group. These results may help generate future hypotheses that the off-line memory consolidation might be impaired in Alzheimer's disease