We first demonstrate the use of few layer graphene films directly grown on SiO₂ substrates obtained by ambient pressure chemical vapor deposition (APCVD) as counter electrodes in dye-sensitized solar cells (DSSCs). The layer number and crystal size of graphene films can be tuned by changing growth temperature, growth time and gas flow ratio (CH₄ : H₂). The continuous graphene films exhibit extremely excellent electrical transport properties with a sheet resistance of down to 63.0 Ω sq⁻¹ and extremely high mobility of up to 201.4 cm² v⁻¹ s⁻¹. The highly conductive graphene films as counter electrodes of DSSCs achieve a photovoltaic efficiency of 4.25%, which is comparable to the DSSC efficiency (4.32%) based on FTO counter electrodes. Our work indicates the great potential of CVD graphene films directly grown on dielectric substrates for photovoltaic and electronic applications.

In recent years, the sparse signal processing technique has shown significant potential in synthetic aperture radar (SAR) imaging, such as image performance improvement and downsampled data-based image recovery. However, due to the huge computational complexity needed, the existing sparse SAR imaging methods, such as conventional observation matrix-based and azimuth-range decouple-based algorithms, are not able to achieve real-time processing, especially for the large-scale scenes, which seriously restricts its application in some fields, e.g., real-time monitoring and early warning. To solve this problem, this article presents a novel real-time sparse SAR imaging method, which can get a similar image performance to that obtained by the existing sparse imaging methods, to reduce the computational complexity to the same order as that required by matched filtering (MF)-based algorithms. This means that with the proposed method, real-time data processing for practical large-scale scene sparse reconstruction becomes possible. Experimental results based on simulated and real data along with a performance analysis are presented to validate the proposed real-time sparse imaging method.

Synthetic aperture radar (SAR) is a widely used active high-resolution microwave imaging technique that has alltime and all-weather reconnaissance ability. Compared with traditionally matched filtering (MF)-based methods, L _q (0 ≤ q ≤ 1) regularization technique can efficiently improve SAR imaging performance e.g., suppressing sidelobes and clutter. However, conventional L _q -regularization-based SAR imaging approach requires transferring the 2-D echo data into a vector and reconstructing the scene via 2-D matrix operations. This leads to significantly more computational complexity compared with MF, and makes it very difficult to apply in high-resolution and wide-swath imaging. Typical L _q regularization recovery algorithms, e.g., iterative thresholding algorithm, can improve imaging performance of bright targets, but not preserve the image background distribution well. Thus, image background statistical-property-based applications, such as constant false alarm rate (CFAR) detection, cannot be applied to regularization recovered SAR images. On the other hand, complex approximated message passing (CAMP), an iterative recovery algorithm for L ₁ regularization reconstruction, can achieve not only the sparse estimation of the original signal as typical regularization recovery algorithms but also a nonsparse solution simultaneously. In this paper, two novel CAMP-based SAR imaging algorithms are proposed for raw data and complex radar image data, respectively, along with CFAR detection via the CAMP recovered nonsparse result. The proposed method for raw data can not only improve SAR image performance as conventional L ₁ regularization technique but also reduce the computational cost efficiently. While only when we have MF recovered SAR complex image rather than raw data, the proposed method for complex image data can achieve a similar reconstructed image quality as the regularization-based SAR imaging approach using the full raw data. The most important contribution of this paper is that the proposed CAMP-based methods make CFAR detection based on the regularization reconstruction SAR image possible using their nonsparse scene estimations, which has a similar background statistical distribution as the MF recovered images. The experimental results validated the effectiveness of the proposed methods and the feasibility of the recovered nonsparse images being used for CFAR detection.

Using sparse signal processing to replace matched filtering (MF) in synthetic aperture radar (SAR) imaging has shown significant potential to improve image quality. Due to the huge computational cost needed, it is difficult to apply conventional observation-matrix-based sparse SAR imaging method for large-scene reconstruction. The azimuth-range decouple method is able to minimize the computational complexity and achieve image performance similar to that obtained by the observation-matrix-based algorithm. However, there still exist two difficult problems in sparse SAR imaging, i.e., real-time processing and lack of raw data. To solve these problems, this paper presents a novel complex-image-based sparse SAR imaging method. It is found that if the input MF-recovered SAR complex image is obtained via fully sampled raw data, the proposed method can achieve an identical high-resolution image to that obtained by the azimuth-range decouple algorithm. The computational complexity is also decreased to the same order as that of MF, which makes the real-time sparse SAR imaging become possible. In addition, it should be noted that even though without raw data, the proposed method can still obtain impressive sparse recovery performance by using only the available complex image. Performance analysis and experimental results on real data validate the proposed method.

Background: High-risk human papilloma virus (hrHPV) is the main causal factor of cervical precancer and cancer when persistent infection is left untreated. Previous studies have confirmed the vaginal microbiota is associated with HPV infection and the development of cervical lesions. The microbiota at different parts of the female genital tract is closely related but different from each other. To analyze the distinction between the vaginal and cervical microbiota of hrHPV(+) women in China, one hundred subjects were recruited, including 10 patients with HPV16/18(+) and cervical carcinoma, 38 patients with HPV16/18(+) but no cervical carcinoma, 32 patients with other hrHPV(+) and 20 healthy controls with HPV(−). Vaginal and cervical microbiota were separately tested through next-generation sequencing (NGS) targeting the variable region (V3-V4) of the bacterial ribosome 16S rRNA gene. Results: HrHPV(+) subjects had higher percentages of vaginal douching history (P = 0.001), showed more frequent usage of sanitary pads (P = 0.007), had more sex partners (P = 0.047), were more sexually active (P = 0.025) and more diversed in ways of contraception (P = 0.001). The alpha diversity of the cervical microbiota was higher than that of the vagina. The cervical microbiota consisted of a lower percentage of Firmicutes and a higher percentage of Proteobacteria than the vagina at the phylum level. Sphingomonas, belonging to α-Proteobacteria, was almost below the detection limit in the vagina but accounted for five to 10 % of the bacteria in the hrHPV(−) cervix (P<0.001) and was inversely associated with hrHPV infection (P<0.05). Pseudomonas, belonging to γ-Proteobacteria, could hardly be seen in the normal vagina and shared a small percentage in the normal cervix but was significantly higher in the HPV16/18(+) (P<0.001) and cancerous cervix (P<0.05). No significant difference was shown in the percentage of BV associated anaerobes, like Gardnerella, Prevotella, Atopobium and Sneathia, between the cevix and vigina. Conclusions: The proportion of Proteobacteria was significantly higher in the cervical microbiota than that of vagina. The hrHPV infection and cervical cancer was positively associated with Pseudomonas and negatively associated with Sphingomonas. It is of great improtance to deeply explore the cervical microbiota and its function in cervical cacinogenesis.

This paper proposes a novel azimuth-range decouple-based L ₁ regularization imaging approach for the focusing in terrain observation by progressive scans (TOPS) synthetic aperture radar (SAR). Since conventional L ₁ regularization technique requires transferring the (2-D) echo data into a vector and reconstructing the scene via 2-D matrix operations leading to significantly more computational complexity, it is very difficult to apply in high-resolution and wide-swath SAR imaging, e.g., TOPS. The proposed method can achieve azimuth-range decouple by constructing an approximated observation operator to simulate the raw data, the inverse of matching filtering (MF) procedure, which makes large-scale sparse reconstruction, or called compressive sensing reconstruction of surveillance region with full- or downsampled raw data in TOPS SAR possible. Compared with MF algorithm, e.g., extended chirp scaling-baseband azimuth scaling, it shows huge potential in image performance improvement; while compared with conventional L ₁ regularization technique, it significantly reduces the computational cost, and provides similar image features. Furthermore, this novel approach can also obtain a nonsparse estimation of considered scene retaining a similar background statistical distribution as the MF-based image, which can be used to the further application of SAR images with precondition being preserving image statistical properties, e.g., constant false alarm rate detection. Experimental results along with a performance analysis validate the proposed method.