Coupling Glycerol Conversion with Hydrogen Production Using Alloyed Electrocatalysts.

Herein, uniform precious alloys including PtAg, PdAg, and PtPdAg nanoparticles were synthesized as electrocatalysts for glycerol oxidation reaction (GOR). The structures of the samples were characterized by transmission electron microscopy, X-ray diffraction, and X-ray photoelectron spectrometry. The catalytic performance of the samples was evaluated in both alkaline and acidic electrolytes. Among the samples, PtPdAg exhibited superior activity with the largest current density of 3.77 mA cm-2 in alkaline solutions, which is 4.1 and 7.7 times those of Pd/C and Pt/C, respectively. In acidic solutions, the PtPdAg catalyst shows the highest current density of 0.58 mA cm-2, which is 1.8 times that of the Pt/C catalyst. The products of GOR were analyzed by high-performance liquid chromatography. Eight products including oxalic acid, tartronic acid, glyoxylic acid, glyceric acid, glyceraldehyde (GLAD), glycolic acid, lactic acid, and dihydroxyacetone were detected. Notably, in acidic solutions, PtAg and PtPdAg yielded the largest GLAD selectivity of 92.2% at 0.6 and 0.8 V, respectively. Using the alloyed catalysts, electrolysis processes coupling the GOR with the hydrogen evolution reaction were conducted. The conversion of glycerol and production of hydrogen were determined. To highlight the energy efficiency, a solar-panel-powered electrolysis process was conducted for the simultaneous production of hydrogen and high-valued products.

Spatial analysis with SPIAT and spaSim to characterize and simulate tissue microenvironments.

Spatial proteomics technologies have revealed an underappreciated link between the location of cells in tissue microenvironments and the underlying biology and clinical features, but there is significant lag in the development of downstream analysis methods and benchmarking tools. Here we present SPIAT (spatial image analysis of tissues), a spatial-platform agnostic toolkit with a suite of spatial analysis algorithms, and spaSim (spatial simulator), a simulator of tissue spatial data. SPIAT includes multiple colocalization, neighborhood and spatial heterogeneity metrics to characterize the spatial patterns of cells. Ten spatial metrics of SPIAT are benchmarked using simulated data generated with spaSim. We show how SPIAT can uncover cancer immune subtypes correlated with prognosis in cancer and characterize cell dysfunction in diabetes. Our results suggest SPIAT and spaSim as useful tools for quantifying spatial patterns, identifying and validating correlates of clinical outcomes and supporting method development.

Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain.

Deep reinforcement learning (DRL) and deep multiagent reinforcement learning (MARL) have achieved significant success across a wide range of domains, including game artificial intelligence (AI), autonomous vehicles, and robotics. However, DRL and deep MARL agents are widely known to be sample inefficient that millions of interactions are usually needed even for relatively simple problem settings, thus preventing the wide application and deployment in real-industry scenarios. One bottleneck challenge behind is the well-known exploration problem, i.e., how efficiently exploring the environment and collecting informative experiences that could benefit policy learning toward the optimal ones. This problem becomes more challenging in complex environments with sparse rewards, noisy distractions, long horizons, and nonstationary co-learners. In this article, we conduct a comprehensive survey on existing exploration methods for both single-agent RL and multiagent RL. We start the survey by identifying several key challenges to efficient exploration. Then, we provide a systematic survey of existing approaches by classifying them into two major categories: uncertainty-oriented exploration and intrinsic motivation-oriented exploration. Beyond the above two main branches, we also include other notable exploration methods with different ideas and techniques. In addition to algorithmic analysis, we provide a comprehensive and unified empirical comparison of different exploration methods for DRL on a set of commonly used benchmarks. According to our algorithmic and empirical investigation, we finally summarize the open problems of exploration in DRL and deep MARL and point out a few future directions.

Towards quantitative and intuitive percutaneous tumor puncture via augmented virtual reality.

In recent years, the radiofrequency ablation (RFA) therapy has become a widely accepted minimal invasive treatment for liver tumor patients. However, it is challenging for doctors to precisely and efficiently perform the percutaneous tumor punctures under free-breathing conditions. This is because the traditional RFA is based on the 2D CT Image information, the missing spatial and dynamic information is dependent on surgeons' experience. This paper presents a novel quantitative and intuitive surgical navigation modality for percutaneous respiratory tumor puncture via augmented virtual reality, which is to achieve the augmented visualization of the pre-operative virtual planning information precisely being overlaid on intra-operative surgical scenario. In the pre-operation stage, we first combine the signed distance field of feasible structures (like liver and tumor) where the puncture path can go through and unfeasible structures (like large vessels and ribs) where the needle is not allowed to go through to quantitatively generate the 3D feasible region for percutaneous puncture. Then we design three constraints according to the RFA specialists consensus to automatically determine the optimal puncture trajectory. In the intra-operative stage, we first propose a virtual-real alignment method to precisely superimpose the virtual information on surgical scenario. Then, a user-friendly collaborative holographic interface is designed for real-time 3D respiratory tumor puncture navigation, which can effectively assist surgeons fast and accurately locating the target step-by step. The validation of our system is performed on static abdominal phantom and in vivo beagle dogs with artificial lesion. Experimental results demonstrate that the accuracy of the proposed planning strategy is better than the manual planning sketched by experienced doctors. Besides, the proposed holographic navigation modality can effectively reduce the needle adjustment for precise puncture as well. Our system shows its clinical feasibility to provide the quantitative planning of optimal needle path and intuitive in situ holographic navigation for percutaneous tumor ablation without surgeons' experience-dependence and reduce the times of needle adjustment. The proposed augmented virtual reality navigation system can effectively improve the precision and reliability in percutaneous tumor ablation and has the potential to be used for other surgical navigation tasks.