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The electrochemical CO2 reduction reaction to produce CO is one of the most promising pathways to eliminate CO2 emissions and store intermittent energy sources. However, the most critical usage of CO is via mixing with H2 to form syngas, which is a crucial feedstock for many value‐added chemicals. Therefore, producing syngas with a suitable CO/H2 ratio in a one‐step reaction is desirable in the CO2RR process. To achieve this end, dual single‐atom sites supported by the nitrogen‐doped carbon show great potential. Herein, we show that a dual single‐atom catalyst with Bi−N4 and Zn−N4 units is efficient for generating syngas with tunable CO/H2 ratios (0.20 to 2.92), which is of great significance to downstream industrial production. Moreover, this work highlights the potential to control the CO/H2 ratios for efficient syngas production using the coexisting single‐atom sites. [ABSTRACT FROM AUTHOR]

Traditional image recognition approaches can achieve high performance only when the images have high resolution and superior quality. A new vehicle logo recognition (VLR) method is proposed to treat low-resolution and poor-quality images captured from urban crossings in intelligent transport system, and the proposed approach is based on statistical random sparse distribution (SRSD) feature and multiscale scanning. The SRSD feature is a novel feature representation strategy that uses the correlation between random sparsely sampled pixel pairs as an image feature and describes the distribution of a grayscale image statistically. Multiscale scanning is a creative classification algorithm that locates and classifies a logo integrally, which alleviates the effect of propagation errors in traditional methods by processing the location and classification separately. Experiments show an overall recognition rate of 97.21% for a set of 3370 vehicle images, which showed that the proposed algorithm outperforms classical VLR methods for low-resolution and inferior quality images and is very suitable for on-site supervision in ITSs. [ABSTRACT FROM PUBLISHER]

Objective In theory, color-to-gray transformation is a process of dimensionality reduction, thus making information loss inevitable. Therefore, the goal of decolorization is to use the limited gray level range to preserve as much information of the original color image as possible. Researchers have proposed many related algorithms. However, the algorithms fail in the simultaneous preservation of the local and global contrast, as well as the contrast, color consistency, and grayscale pixel, of the original image. To solve these problems, we propose a new approach that can maximally maintain the features of the original color image. Method To preserve the structure and local information, we use a bimodal Gaussian distribution, followed by the difference between the pixel and its neighbors, to construct the error energy function. For global color consistency, we use locally linear embedding to build the energy function, which causes the same color pixels to exhibit the same gray levels in the result. For grayscale feature preservation, we distinguish grayscale pixels and specify that the gray values of grayscale pixels are known quantities and are initially unchanged during conversion. We then construct the energy function between grayscale pixels and other pixels. Thereafter, we build the objective function with the linear combination of the three energy functions and the gray image is obtained by solving the objective function using the iterative method. Result Experimental results show that our algorithm can preserve salient structure and fine detail, as well ensure that the same color can be transformed to the same grayscale and that the gray value of the grayscale pixel would be unchanged after conversion. Conclusion Our algorithm conforms to the degree of perception about contrast change in image and can preserve detailed information and global structure. The algorithm can be applied to digital printing, pattern recognition, and so on, and thus has great application value. [ABSTRACT FROM AUTHOR]

Objective: Sirt7, as one of the seven Sirtuin family members, which plays distinct roles in cancer progression, is bringing emerging attention due to its oncogenic characteristic. The expression of Sirt7 in breast cancer remained unclear, and the aim of this study was to elucidate its role in breast cancer., Methods: A total of 188 cases included in this study were immunohistochemically evaluated for Sirt7, and western blot assay was used to assess its expression in breast cell lines as well as 36 breast cancer tissues and 36 paired non-cancerous tissues., Results: Upregulation of Sirt7 was found in breast cancer cell lines and breast cancer tissues (P < 0.001) by western blot analysis. Sirt7 was highly expressed in breast cancer tissue samples (67.8%) compared to adjacent normal breast tissues (31.8%) by immunohistochemical assay. It was also observed that the high expression level of Sirt7 was significantly correlated with high histological grade (P = 0.039) and negatively related to overall survival (P = 0.006). Sirt7 proved to be an independent prognostic factor (P = 0.007) in breast cancer., Conclusions: Sirt7 expression was implicated with high histological grade and independently predicted poor clinical outcome in patients with breast cancer, suggesting that Sirt7 might play a role in the malignant progression of breast cancer.

Triple negative breast cancer (TNBC) is one type of breast cancer (BC), which is defined as negative for estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor-2 (Her2). Its origins and development seem to be elusive. And for now, drugs like tamoxifen or trastuzumab which specifically apply to ER, PR or Her2 positive BC seem unforeseeable in TNBC clinical treatment. Due to its extreme malignancy, high recurrence rate and poor prognosis, a lot of work on the research of TNBC is needed. This review aims to summarize the latest findings in TNBC in risk factors, possible therapeutic targets and possible prognostic makers.