Co3O4@CoS core-shell nanosheets on carbon cloth for high performance supercapacitor electrodes

In this work, a two-step electrodeposition strategy is developed for the synthesis of core-shell Co3O4@CoS nanosheet arrays on carbon cloth (CC) for supercapacitor applications. Porous Co3O4 nanosheet arrays are first directly grown on CC by electrodeposition, followed by the coating of a thin layer of CoS on the surface of Co3O4 nanosheets via the secondary electrodeposition. The morphology control of the ternary composites can be easily achieved by altering the number of cyclic voltammetry (CV) cycles of CoS deposition. Electrochemical performance of the composite electrodes was evaluated by cyclic voltammetry, galvanostatic charge-discharge and electrochemical impedance spectroscopy techniques. The results demonstrate that the Co3O4@CoS/CC with 4 CV cycles of CoS deposition possesses the largest specific capacitance 887.5 F·g-1 at a scan rate of 10 mV·s-1 (764.2 F·g-1 at a current density of 1.0 A·g-1), and excellent cycling stability (78.1% capacitance retention) at high current density of 5.0 A·g-1 after 5000 cycles. The porous nanostructures on CC not only provide large accessible surface area for fast ions diffusion, electron transport and efficient utilization of active CoS and Co3O4, but also reduce the internal resistance of electrodes, which leads to superior electrochemical performance of Co3O4@CoS/CC composite at 4 cycles of CoS deposition. © 2017 by the authors.National Natural Science Foundation of China [21371057]; International Science and Technology Cooperation Program of China [2016YFE0131200, 2015DFA51220]; International Cooperation Project of Shanghai Municipal Science and Technology Committee [15520721100

Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models

Hand, foot, and mouth disease (HFMD) is a worldwide infectious disease, prominent in China. China’s HFMD data are sparse with a large number of observed zeros across locations and over time. However, no previous studies have considered such a zero-inflated problem on HFMD’s spatiotemporal risk analysis and mapping, not to mention for the entire Mainland China at county level. Monthly county-level HFMD cases data combined with related climate and socioeconomic variables were collected. We developed four models, including spatiotemporal Poisson, negative binomial, zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) models under the Bayesian hierarchical modeling framework to explore disease spatiotemporal patterns. The results showed that the spatiotemporal ZINB model performed best. Both climate and socioeconomic variables were identified as significant risk factors for increasing HFMD incidence. The relative risk (RR) of HFMD at the local scale showed nonlinear temporal trends and was considerably spatially clustered in Mainland China. The first complete county-level spatiotemporal relative risk maps of HFMD were generated by this study. The new findings provide great potential for national county-level HFMD prevention and control, and the improved spatiotemporal zero-inflated model offers new insights for epidemic data with the zero-inflated problem in environmental epidemiology and public health

Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple Hospitals

Nasopharyngeal carcinoma (NPC) is a prevalent and clinically significant malignancy that predominantly impacts the head and neck area. Precise delineation of the Gross Tumor Volume (GTV) plays a pivotal role in ensuring effective radiotherapy for NPC. Despite recent methods that have achieved promising results on GTV segmentation, they are still limited by lacking carefully-annotated data and hard-to-access data from multiple hospitals in clinical practice. Although some unsupervised domain adaptation (UDA) has been proposed to alleviate this problem, unconditionally mapping the distribution distorts the underlying structural information, leading to inferior performance. To address this challenge, we devise a novel Sourece-Free Active Domain Adaptation (SFADA) framework to facilitate domain adaptation for the GTV segmentation task. Specifically, we design a dual reference strategy to select domain-invariant and domain-specific representative samples from a specific target domain for annotation and model fine-tuning without relying on source-domain data. Our approach not only ensures data privacy but also reduces the workload for oncologists as it just requires annotating a few representative samples from the target domain and does not need to access the source data. We collect a large-scale clinical dataset comprising 1057 NPC patients from five hospitals to validate our approach. Experimental results show that our method outperforms the UDA methods and achieves comparable results to the fully supervised upper bound, even with few annotations, highlighting the significant medical utility of our approach. In addition, there is no public dataset about multi-center NPC segmentation, we will release code and dataset for future research