Soft robots, compared to regular rigid robots, as their multiple segments with soft materials bring flexibility and compliance, have the advantages of safe interaction and dexterous operation in the environment. However, due to its characteristics of high dimensional, nonlinearity, time-varying nature, and infinite degree of freedom, it has been challenges in achieving precise and dynamic control such as trajectory tracking and position reaching. To address these challenges, we propose a framework of Deep Koopman-based Model Predictive Control (DK-MPC) for handling multi-segment soft robots. We first employ a deep learning approach with sampling data to approximate the Koopman operator, which therefore linearizes the high-dimensional nonlinear dynamics of the soft robots into a finite-dimensional linear representation. Secondly, this linearized model is utilized within a model predictive control framework to compute optimal control inputs that minimize the tracking error between the desired and actual state trajectories. The real-world experiments on the soft robot “Chordata” demonstrate that DK-MPC could achieve highprecision control, showing the potential of DK-MPC for future applications to soft robots. More visualization results can be found at https://pinkmoon-io.github.io/DKMPC/.

MOTIVATION: Aberrant DNA methylation is strongly associated with heterogeneity in tumors. This study investigated the prognostic value of CpG island methylator phenotype in hepatocellular carcinoma (HCC). RESULTS: A total of 319 HCC samples with 21 121 CpG sites were included in this study and 215 disease-free survival (DFS) and overall survival (OS)-related CpG sites were identified. These CpG sites were divided into seven clusters by using consensus clustering method. Cluster 4, which constructed the prognostic prediction model as the seed cluster to evaluate survival risk for DFS and OS of HCC patients, had the lowest methylation level with the worse prognosis. The low-risk group patients had a significantly prolonged DFS and OS than the patients in the high-risk group (P = 0.008 and P < 0.001, respectively). A receiver operating characteristic curve results for predicting DFS and OS were 0.691 and 0.695, respectively. These results suggested that the CpG site methylation appears to be an informative prognostic biomarker in HCC. The CpG site methylation-related prognostic model may be an innovative insight to evaluate clinical outcomes for HCC patients. AVAILABILITY AND IMPLEMENTATION: The code of the analysis is available at https://www.bioconductor.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Conventional single photon emission computed tomography (SPECT) relies on mechanical collimation whose resolution and sensitivity are interdependent, the best performance a SPECT system can attain is only a compromise of these two equally desired properties. To simultaneously achieve high resolution and sensitivity, we propose to use sensitive detectors constructed in a multi-layer in ter spaced mosaicdetectors (MATRICES) architecture to accomplish part of the collimation needed. We name this new approach self-collimation. We evaluate three self-collimating SPECT systems and report their imaging performance: 1) A simulated human brain SPECT achieves 3.88% sensitivity, it clearly resolves 0.5-mm and 1.0-mm hot-rod patterns at noise-free and realistic count-levels, respectively; 2) a simulated mouse SPECT achieves 1.25% sensitivity, it clearly resolves 50- [Formula: see text] and 100- [Formula: see text] hot-rod patterns at noise-free and realistic count-levels, respectively; 3) a SPECT prototype achieves 0.14% sensitivity and clearly separates 0.3-mm-diameter point sources of which the center-to-center neighbor distance is also 0.3 mm. Simulated contrast phantom studies show excellent resolution and signal-to-noise performance. The unprecedented system performance demonstrated by these 3 SPECT scanners is a clear manifestation of the superiority of the self-collimating approach over conventional mechanical collimation. It represents a potential paradigm shift in SPECT technology development.