Processive reactions, such as transcription or translation, often proceed through distinct initiation and elongation phases. The processive formation of polymeric ubiquitin chains can accordingly be catalyzed by specialized initiating and elongating E2 enzymes, but the functional significance for this division of labor has remained unclear. Here, we have identified sequence motifs in several substrates of the anaphase-promoting complex (APC/C) that are required for efficient chain initiation by its E2 Ube2C. Differences in the quality and accessibility of these chain initiation motifs can determine the rate of a substrate’s degradation without affecting its affinity for the APC/C, a mechanism used by the APC/C to control the timing of substrate proteolysis during the cell cycle. Based on our results, we propose that initiation motifs and their cognate E2s allow E3 enzymes to exert precise temporal control over substrate degradation.

Single image super-resolution (SISR) has been a very attractive research topic in recent years. Breakthroughs in SISR have been achieved due to deep learning and generative adversarial networks (GANs). However, the generated image still suffers from undesired artifacts. In this paper, we propose a new method named GMGAN for SISR tasks. In this method, to generate images more in line with human vision system (HVS), we design a quality loss by integrating an image quality assessment (IQA) metric named gradient magnitude similarity deviation (GMSD). To our knowledge, it is the first time to truly integrate an IQA metric into SISR. Moreover, to overcome the instability of the original GAN, we use a variant of GANs named improved training of Wasserstein GANs (WGAN-GP). Besides GMGAN, we highlight the importance of training datasets. Experiments show that GMGAN with quality loss and WGAN-GP can generate visually appealing results and set a new state of the art. In addition, large quantity of high-quality training images with rich textures can benefit the results.

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Cervical cancer is one of the most common cancers in women, with about 450,000 new cases diagnosed each year. Nearly all the cervical cancers are from Papillomaviruses (HPV) infection. HPV are DNA-based viruses that infect the skin and mucous membranes of humans and many other animals. Most cancers of the vulva and vagina are induced by oncogenic HPV types. In precancerous lesions, most HPV genomes persist in an episomal state whereas, in many high-grade lesions and carcinomas, genomes are found integrated into the host chromosome. Two viral genes, E6 and E7, are invariably expressed in HPV-positive cancer cells. Their gene products are known to inactivate the major tumour suppressors, p53 and retinoblastoma protein (pRB), respectively. In addition, E6 oncoprotein is also capable to up regulate the expression of inhibitors of apoptosis, and E6 and E7 cooperate to effectively immortalise primary epithelial cells. It has been demonstrated that HPV E6/E7 expression level plays a key role in the progression of invasive carcinoma of the uterine cervix via the deregulation of cellular genes controlling tumour cell proliferation. HPV E6/E7 oncogenes have been proved to be good biological markers for prognosis assessment and specific therapy of the disease. We have developed a sandwich nucleic acid hybridization assay using branched DNA to amplify the signals. The assay can simultaneously detect E6/E7 mRNAs of all 14 high risk HPV subtypes directly from Pap smear samples without RNA purification and RT-PCR. All HPV mRNA targets are captured through cooperative hybridization of multiple probes and probe set design determines the specificity of each HPV subtype of E6/E7 mRNA. Probe set oligonucleotides bind a contiguous region of the target E6/E7 mRNAs and selectively capture target RNAs to a solid surface during hybridization. Signal amplification is performed via sequential hybridization of Pre-Amplifier, Amplifier and label probe. The assay is highly specific and sensitive, which can detect as low as 50 transcript molecules. Citation Format: Aiguo Zhang, Lulu Zhang, Rachel Chuang, Xining Zhu, Rachel Diaz, Lily Chen. Directly Detect High Risk HPV Oncogenes E6/E7 mRNAs from Pap Smear Samples without RNA Purification and RT-PCR. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3482. doi:10.1158/1538-7445.AM2013-3482

Biometrics based personal authentication has been found to be an effective method for recognizing, with high confidence, a person’s identity. With the emergence of reliable and inexpensive 3D scanners, recent years have witnessed a growing interest in developing 3D biometrics systems. As a commonsense, matching algorithms are crucial for such systems. In this paper, we focus on investigating identification methods for two specific 3D biometric identifiers, 3D ear and 3D palmprint. Specifically, we propose a Multi-Dictionary based Collaborative Representation (MDCR) framework for classification, which can reduce the negative effects aroused by some local regions. With MDCR, a range map is partitioned into overlapping blocks and, from each block, a feature vector is extracted. At the dictionary construction stage, feature vectors from blocks having the same locations in gallery samples can form a dictionary and, accordingly, multiple dictionaries are obtained. Given a probe sample, by coding its each feature vector on the corresponding dictionary, multiple class labels can be obtained and then we use a simple majority-based voting scheme to make the final decision. In addition, a novel patch-wise and statistics-based feature extraction scheme is proposed, combining the range image’s local surface type information and local dominant orientation information. The effectiveness of the proposed approach has been corroborated by extensive experiments conducted on two large-scale and widely-used benchmark datasets, the UND Collection J2 3D ear dataset and the PolyU 3D palmprint dataset. To make the results reproducible, we have publicly released the source code.