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The electrocatalyst of oxygen reduction reactions is one of the basic components of a fuel cell. In addition to costly Pt/C benchmark catalysts, cost-effective carbon-based catalysts have received the most attention. Enormous efforts have been dedicated to trade off the catalyst performance against the economic benefit. Optimizing composition and/or structure is a universal strategy for improving performance, but it is typically limited by tedious synthesis steps. Herein, we have found that directly introducing CNT into MOF-derived carbonaceous nanopolyhedra, i.e., introduced carbon nanotubes (CNTs) penetrated porous nitrogen-doped carbon polyhedra (NCP) dotted with cobalt nanoparticles (denoted as CNTs-Co@NCP), can optimize the catalytic activity, stability, and methanol tolerance. The hierarchical architecture combines the 0D/1D/3D Co/CNT/NCP interfaces and 1D/3D CNT/NCP junctions with the frameworks with a greatly exposed active surface, strengthened mass transport kinetics, stereoscopic electrical conductivity networks and structural robustness. The sterical self-consistency of MOF-self-assembly triggered by introduced CNTs demonstrates tactful ORR electrocatalytic activity regulation. Eventually, the CNTs-Co@NCP showed a half-wave potential (E1/2) of 0.86 V and a diffusion-limited current density (JL) of 5.94 mA/cm2 in alkaline electrolyte. The CNTs-Co@NCP was integrated into the cathode of a direct methanol fuel cell (DMFC) with an anion-exchange membrane, and an open-circuit voltage (OCV) of 0.93 V and a high power density of 46.6 mW cm−2 were achieved. This work successfully developed a catalyst with competitive ORR performance through plain parameter fine-tuning without complex material design. [ABSTRACT FROM AUTHOR]

To systematically evaluate the predictive value of preoperative computed tomography perfusion imaging (CTPI) parameters in early response to transcatheter arterial chemoembolization (TACE) of hepatocellular carcinoma (HCC). The relationship between CT perfusion imaging and TACE prognosis was searched in China knowledge Network, Wanfang, VIP, PubMed, Medline, Web of Science and Embase databases by computer. Meta analysis was carried out by using RevMan 5.4 software. The mean difference (MD) was used, and each effect was expressed by 95% confidence interval (CD. A total of 7 articles were included, including 433 patients with a total of 475 tumor lesions. Meta analysis showed that there was significant difference in arterial liver perfusion (ALP) between the reactive group and the non-responsive group after TACE (MD=16.84, 95% CI 2.06~31.62, P<0.05). The ALP of reactive group was significantly higher than that of non-responsive group, and the best cut-off value range was 9.885~22.175 mL/ (100 mL • min). There was no significant difference in portal venous liver perfusion (PVP), hepatic perfusion index (HPI), blood flow (BF), blood volume (BV), mean transit time (MTT) and time to peak (TTP) between the reactive group and the non-responsive group after TACE. High preoperative ALP can predict the early effectiveness of liver cancer after TACE, but the more exact cut-off value of ALP needs further study. [ABSTRACT FROM AUTHOR]

As a precursor of aromatic compounds, fatty acids play important roles in apple fruit quality; however, the genetic and molecular basis underlying fatty acid synthesis and metabolism is largely unknown. In this study, we conducted a genome-wide association study (GWAS) of seven fatty acids using genomic data of 149 Malus accessions and identified 232 significant signals (–log10 P >5) associated with 99 genes from GWAS of four fatty acids across 2 years. Among these, a significant GWAS signal associated with linoleic acid was identified in the transcriptional regulator SUPERMAN-like (SUP) MD13G1209600 at chromosome 13 of M. × domestica. Transient overexpression of MdSUP increased the contents of linoleic and linolenic acids and of three aromatic components in the fruit. Our study provides genetic and molecular information for improving the flavor and nutritional value of apple. [ABSTRACT FROM AUTHOR]

There is a steadily growing range of applications that can benefit from facial reconstruction techniques, leading to an increasing demand for reconstruction of high-quality 3D face models. While it is an important expressive part of the human face, the nose has received less attention than other expressive regions in the face reconstruction literature. When applying existing reconstruction methods to facial images, the reconstructed nose models are often inconsistent with the desired shape and expression. In this paper, we propose a coarse-to-fine 3D nose reconstruction and correction pipeline to build a nose model from a single image, where 3D and 2D nose curve correspondences are adaptively updated and refined. We first correct the reconstruction result coarsely using constraints of 3D-2D sparse landmark correspondences, and then heuristically update a dense 3D-2D curve correspondence based on the coarsely corrected result. A final refinement step is performed to correct the shape based on the updated 3D-2D dense curve constraints. Experimental results show the advantages of our method for 3D nose reconstruction over existing methods. [ABSTRACT FROM AUTHOR]

Meme, usually represented by an image of exaggerated expressive face captioned with short text, are increasingly produced and used online to express people's strong or subtle emotions. Meanwhile, meme mimic apps continuously appear, such as the meme filming feature in WeChat App that allow users to imitate meme expressions. Motivated by such scenarios, we focus on transferring exaggerated or unique expressions which is rarely noticed by previous works. We present a technique—"expressive style transfer"—which allows users to faithfully imitate popular memes' unique expression styles both geometrically and textually. To conduct distortion-free transferring of exaggerated geometry, we propose a novel accurate feature curve-based face reconstruction algorithm for 3D-aware image warping. Furthermore, we propose an identity preserving blending model, based on a deep neural network, to enhance facial expressive textural details. We demonstrate the effectiveness of our method on a collection of Internet memes. [ABSTRACT FROM AUTHOR]

Abstract: Reshaping faces is an interesting thing and a common demand in today’s media industry and cosmetic industry. Especially, the reshaped face images contribute a lot to special effects industry and plastic-beauty industry. In this paper, we provide a novel parametric representation of 3D face model that describes the face shape as a linear combination of semantic and non-semantic bases. The semantic (local) bases correspond to each individual face part and they are used to semantically edit each face part, while the non-semantic bases explain other shape variations to span the subspace of the face shape. First, we build a sparse and spatially localized parametric face model from a dataset of 3D face models by sparse principal component analysis. Second, to define the semantic bases, we train a regression model to correlate semantically significant values like nose height and mouth width. Finally, the bases of the resulting parametric face model is orthogonalized to all defined semantic bases by the Gram-Schmidt algorithm for generating the novel parametric representation of 3D face model. The novel representation can be applied in reshaping face in the images. The experiment results demonstrate that our representation not only retains the accuracy for 3D face reconstruction but also provides users a user-friendly tool to edit facial parts for desired facial shapes.Graphical abstract: [ABSTRACT FROM AUTHOR]

The relationship between body size and fitness in parasitoid wasps has several effects on parasitic ability, reproductive behavior in female wasps, and progeny fitness. Female wasps with various body sizes were obtained by mass–rearing a gregarious ectoparasitoid, Sclerodermus pupariae, which is one of the excellent parasites to control the larvae and pupae of Buprestidae and Cerambycidae. We investigated the effects of body size of adult females introduced on Thyestilla gebleri (Coleoptera: Cerambycidae) larvae on their paralysis time, pre–oviposition period, oviposition period and fecundity, and the related fitness of their offspring. Results showed that small female wasps needed more time to paralyze a host and had a higher mortality rate than large female wasps. More offspring were produced by large female wasps than by small female wasps, and the percentage and body size of female offspring was not affected by maternal body size. The duration of the egg stage was not affected by foundress size, nor was that of the pupal stage, but the duration of the larval stage and generation time of small female wasps was longer than that of large females. Our findings suggest that the parasitic fitness and offspring performance are affected by maternal size, and there is need to choose reasonable body size of female wasps, to optimally utilize mass rearing and to control target pests with the lowest mortality cost. [ABSTRACT FROM AUTHOR]

Detecting salient objects in challenging images attracts increasing attention as many applications require more robust method to deal with complex images from the Internet. Prior methods produce poor saliency maps in challenging cases mainly due to the complex patterns in the background and internal color edges in the foreground. The former problem may introduce noises into saliency maps and the later forms the difficulty in determining object boundaries. Observing that depth map can supply layering information and more reliable boundary, we improve salient object detection by integrating two features: color information and depth information which are calculated from stereo images. The two features collaborate in a two-stage framework. In the object location stage, depth mainly helps to produce a noise-filtered salient patch, which indicates the location of the object. In the object boundary inference stage, boundary information is encoded in a graph using both depth and color information, and then we employ the random walk to infer more reliable boundaries and obtain the final saliency map. We also build a data set containing 100+ stereo pairs to test the effectiveness of our method. Experiments show that our depth-plus-color based method significantly improves salient object detection compared with previous color-based methods. [ABSTRACT FROM AUTHOR]