date, there have been commercial efforts to manufacture lithium thin film batteries (<1 mm in thickness) being flexible and suitable for use in card-type and wearable devices. [3] However, these thin and flexible lithium-ion batteries have typically exhibited far less volumetric energy densities (<200 Wh L −1 ) than those of conventional lithium-ion batteries (<650 Wh L −1 ). [4] This performance roll-off is largely due to the fact that high barrier encapsulation of air and moisture sensitive lithium battery materials can severely impact the effective volumetric efficiency as the batteries are miniaturized. Therefore, high volumetric energy density lithium batteries with thickness <1 mm will be critical to enabling flexible electronics. [5] To this end, notable advancements have been made in the design of flexible lithium-sulfur and lithium-air batteries-those cathode chemistries enable high theoretical energy densities of 2800 and 6940 Wh L −1 , respectively-yet there remains substantial room for improvement. [6][7][8] The potential to yield high volumetric capacities by using less environmentally sensitive materials in the batteries sees zinc as an attractive anode alternative to lithium. In addition to the stability, zinc is likely to experience less cost pressure on raw material availability compared to lithium. In all cases, zinc secondary batteries represent a highly promising area of technology for Flexible Zn-based batteries are regarded as promising alternatives to flexible lithium-ion batteries for wearable electronics owing to the natural advantages of zinc, such as environmental friendliness and low cost. In the past few years, flexible Zn-based batteries have been studied intensively and exciting achievements have been obtained in this field. However, the development of flexible Zn-based batteries is still at an early stage. The challenges of developing flexible lithium-ion batteries are presented here. Then, a brief overview of recent progress in flexible zinc secondary batteries from the perspective of advanced materials and some issues that remain to be addressed are discussed.

Under development for next-generation wearable electronics are flexible, knittable, and wearable energy-storage devices with high energy density that can be integrated into textiles. Herein, knittable fiber-shaped zinc-air batteries with high volumetric energy density (36.1 mWh cm ) are fabricated via a facile and continuous method with low-cost materials. Furthermore, a high-yield method is developed to prepare the key component of the fiber-shaped zinc-air battery, i.e., a bifunctional catalyst composed of atomically thin layer-by-layer mesoporous Co O /nitrogen-doped reduced graphene oxide (N-rGO) nanosheets. Benefiting from the high surface area, mesoporous structure, and strong synergetic effect between the Co O and N-rGO nanosheets, the bifunctional catalyst exhibits high activity and superior durability for oxygen reduction and evolution reactions. Compared to a fiber-shaped zinc-air battery using state-of-the-art Pt/C + RuO catalysts, the battery based on these Co O /N-rGO nanosheets demonstrates enhanced and stable electrochemical performance, even under severe deformation. Such batteries, for the first time, can be successfully knitted into clothes without short circuits under external forces and can power various electronic devices and even charge a cellphone.

In recent years, a number of natural products isolated from Chinese herbs have been found to inhibit proliferation, induce apoptosis, suppress angiogenesis, retard metastasis and enhance chemotherapy, exhibiting anti-cancer potential both in vitro and in vivo. This article summarizes recent advances in in vitro and in vivo research on the anti-cancer effects and related mechanisms of some promising natural products. These natural products are also reviewed for their therapeutic potentials, including flavonoids (gambogic acid, curcumin, wogonin and silibinin), alkaloids (berberine), terpenes (artemisinin, β-elemene, oridonin, triptolide, and ursolic acid), quinones (shikonin and emodin) and saponins (ginsenoside Rg3), which are isolated from Chinese medicinal herbs. In particular, the discovery of the new use of artemisinin derivatives as excellent anti-cancer drugs is also reviewed.

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In logistic regression, separation occurs when a linear combination of the predictors can perfectly classify part or all of the observations in the sample, and as a result, finite maximum likelihood estimates of the regression coefficients do not exist. Gelman et al. (2008) recommended independent Cauchy distributions as default priors for the regression coefficients in logistic regression, even in the case of separation, and reported posterior modes in their analyses. As the mean does not exist for the Cauchy prior, a natural question is whether the posterior means of the regression coefficients exist under separation. We prove theorems that provide necessary and sufficient conditions for the existence of posterior means under independent Cauchy priors for the logit link and a general family of link functions, including the probit link. We also study the existence of posterior means under multivariate Cauchy priors. For full Bayesian inference, we develop a Gibbs sampler based on Pólya-Gamma data augmentation to sample from the posterior distribution under independent Student-t priors including Cauchy priors, and provide a companion R package in the supplement. We demonstrate empirically that even when the posterior means of the regression coefficients exist under separation, the magnitude of the posterior samples for Cauchy priors may be unusually large, and the corresponding Gibbs sampler shows extremely slow mixing. While alternative algorithms such as the No-U-Turn Sampler in Stan can greatly improve mixing, in order to resolve the issue of extremely heavy tailed posteriors for Cauchy priors under separation, one would need to consider lighter tailed priors such as normal priors or Student-t priors with degrees of freedom larger than one.

1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1617-1625.

Developing unique single atoms as active sites is vitally important to boosting the efficiency of photocatalytic CO 2 reduction, but directly atomizing metal particles and simultaneously adjusting the configuration of individual atoms remain challenging. Herein, we demonstrate a facile strategy at a relatively low temperature (500 °C) to access the in situ metal atomization and coordination adjustment via the thermo-driven gaseous acid. Using this strategy, the pyrolytic gaseous acid (HCl) from NH 4 Cl could downsize the large metal particles into corresponding ions, which subsequently anchored onto the surface defects of a nitrogen-rich carbon (NC) matrix. Additionally, the low-temperature treatment-induced CO motifs within the interlayer of NC could bond with the discrete Fe sites in a perpendicular direction and finally create stabilized Fe−N 4 O species with high valence status (Fe 3+ ) on the shallow surface of the NC matrix. It was found that the Fe−N 4 O species can achieve a highly efficient CO 2 conversion when accepting energetic electrons from both homogeneous and heterogeneous photocatalysts. The optimized sample achieves a maximum turnover number (TON) of 1494 within 1 h in CO generation with a high selectivity of 86.7% as well as excellent stability. Experimental and theoretical results unravel that high valence Fe sites in Fe−N 4 O species can promote the adsorption of CO 2 and lower the formation barrier of key intermediate COOH* compared with the traditional Fe−N 4 moiety with lower chemical valence. Our discovery provides new points of view in the construction of more efficient single-atom cocatalysts by considering the optimization of the atomic configuration for high-performance CO 2 photoreduction.

Mixtures of Zellner's g-priors have been studied extensively in linear models and have been shown to have numerous desirable properties for Bayesian variable selection and model averaging. Several extensions of g-priors to Generalized Linear Models (GLMs) have been proposed in the literature; however, the choice of prior distribution of g and resulting properties for inference have received considerably less attention. In this paper, we unify mixtures of g-priors in GLMs by assigning the truncated Compound Confluent Hypergeometric (tCCH) distribution to 1/(1 + g), which encompasses as special cases several mixtures of g-priors in the literature, such as the hyper-g, Beta-prime, truncated Gamma, incomplete inverse-Gamma, benchmark, robust, hyper-g/n, and intrinsic priors. Through an integrated Laplace approximation, the posterior distribution of 1/(1 + g) is in turn a tCCH distribution, and approximate marginal likelihoods are thus available analytically, leading to "Compound Hypergeometric Information Criteria" for model selection. We discuss the local geometric properties of the g-prior in GLMs and show how the desiderata for model selection proposed by Bayarri et al, such as asymptotic model selection consistency, intrinsic consistency, and measurement invariance may be used to justify the prior and specific choices of the hyper parameters.We illustrate inference using these priors and contrast them to other approaches via simulation and real data examples. The methodology is implemented in the R package BAS and freely available on CRAN.