Accumulating evidence demonstrates that long non-coding RNAs (lncRNAs) play important roles in the development and progression of complex human diseases, and predicting novel human lncRNA-disease associations is a challenging and urgently needed task, especially at a time when increasing amounts of lncRNA-related biological data are available. In this study, we proposed a global network-based computational framework, RWRlncD, to infer potential human lncRNA-disease associations by implementing the random walk with restart method on a lncRNA functional similarity network. The performance of RWRlncD was evaluated by experimentally verified lncRNA-disease associations, based on leave-one-out cross-validation. We achieved an area under the ROC curve of 0.822, demonstrating the excellent performance of RWRlncD. Significantly, the performance of RWRlncD is robust to different parameter selections. Predictively highly-ranked lncRNA-disease associations in case studies of prostate cancer and Alzheimer's disease were manually confirmed by literature mining, providing evidence of the good performance and potential value of the RWRlncD method in predicting lncRNA-disease associations.

Powered exoskeletons can empower paraplegics to stand and walk. Actively controlled hip ab/adduction (HAA) is needed for weight shift and for lateral foot placement to support dynamic balance control and to counteract disturbances in the frontal plane. Here, we describe the design, control, and preliminary evaluation of a novel exoskeleton, MINDWALKER. Besides powered hip flexion/extension and knee flexion/extension, it also has powered HAA. Each of the powered joints has a series elastic actuator, which can deliver 100 Nm torque and 1 kW power. A finite-state machine based controller provides gait assistance in both the sagittal and frontal planes. State transitions, such as stepping, can be triggered by the displacement of the Center of Mass (CoM). A novel step-width adaptation algorithm was proposed to stabilize lateral balance. We tested this exoskeleton on both healthy subjects and paraplegics. Experimental results showed that all users could successfully trigger steps by CoM displacement. The step-width adaptation algorithm could actively counteract disturbances, such as pushes. With the current implementations, stable walking without crutches has been achieved for healthy subjects but not yet for SCI paraplegics. More research and development is needed to improve the gait stability.

Neuroprosthetic technology and robotic exoskeletons are being developed to facilitate stepping, reduce muscle efforts, and promote motor recovery. Nevertheless, the guidance forces of an exoskeleton may influence the sensory inputs, sensorimotor interactions and resulting muscle activity patterns during stepping. The aim of this study was to report the muscle activation patterns in a sample of intact and injured subjects while walking with a robotic exoskeleton and, in particular, to quantify the level of muscle activity during assisted gait. We recorded electromyographic (EMG) activity of different leg and arm muscles during overground walking in an exoskeleton in six healthy individuals and four spinal cord injury (SCI) participants. In SCI patients, EMG activity of the upper limb muscles was augmented while activation of leg muscles was typically small. Contrary to our expectations, however, in neurologically intact subjects, EMG activity of leg muscles was similar or even larger during exoskeleton-assisted walking compared to normal overground walking. In addition, significant variations in the EMG waveforms were found across different walking conditions. The most variable pattern was observed in the hamstring muscles. Overall, the results are consistent with a non-linear reorganization of the locomotor output when using the robotic stepping devices. The findings may contribute to our understanding of human-machine interactions and adaptation of locomotor activity patterns.

The unique correspondence between mathematical operators and photonic elements in wave optics enables quantitative analysis of light manipulation with individual optical devices. Phase-transition materials are able to provide real-time reconfigurability of these devices, which would create new optical functionalities via (re)compilation of photonic operators, as those achieved in other fields such as field-programmable gate arrays (FPGA). Here, by exploiting the hysteretic phase transition of vanadium dioxide, an all-solid, rewritable metacanvas on which nearly arbitrary photonic devices can be rapidly and repeatedly written and erased is presented. The writing is performed with a low-power laser and the entire process stays below 90 °C. Using the metacanvas, dynamic manipulation of optical waves is demonstrated for light propagation, polarization, and reconstruction. The metacanvas supports physical (re)compilation of photonic operators akin to that of FPGA, opening up possibilities where photonic elements can be field programmed to deliver complex, system-level functionalities.