Better Guider Predicts Future Better: Difference Guided Generative Adversarial Networks

Predicting the future is a fantasy but practicality work. It is the key component to intelligent agents, such as self-driving vehicles, medical monitoring devices and robotics. In this work, we consider generating unseen future frames from previous obeservations, which is notoriously hard due to the uncertainty in frame dynamics. While recent works based on generative adversarial networks (GANs) made remarkable progress, there is still an obstacle for making accurate and realistic predictions. In this paper, we propose a novel GAN based on inter-frame difference to circumvent the difficulties. More specifically, our model is a multi-stage generative network, which is named the Difference Guided Generative Adversarial Netwok (DGGAN). The DGGAN learns to explicitly enforce future-frame predictions that is guided by synthetic inter-frame difference. Given a sequence of frames, DGGAN first uses dual paths to generate meta information. One path, called Coarse Frame Generator, predicts the coarse details about future frames, and the other path, called Difference Guide Generator, generates the difference image which include complementary fine details. Then our coarse details will then be refined via guidance of difference image under the support of GANs. With this model and novel architecture, we achieve state-of-the-art performance for future video prediction on UCF-101, KITTI.Comment: To appear in ACCV 201

Retrospective study of necrotizing fasciitis and characterization of its associated Methicillin-resistant Staphylococcus aureus in Taiwan

<p>Abstract</p> <p>Background</p> <p>Methicillin-resistant <it>Staphylococcus aureus </it>(MRSA) has emerged as a prevalent pathogen of necrotizing fasciitis (NF) in Taiwan. A four-year NF cases and clinical and genetic differences between hospital acquired (HA)- and community-acquired (CA)-MRSA infection and isolates were investigated.</p> <p>Methods</p> <p>A retrospective study of 247 NF cases in 2004-2008 and antimicrobial susceptibilities, staphylococcal chromosomal cassette <it>mec </it>(SCC<it>mec</it>) types, pulsed field gel electrophoresis (PFGE) patterns, virulence factors, and multilocus sequence typing (MLST) of 16 NF-associated MRSA in 2008 were also evaluated.</p> <p>Results</p> <p>In 247 cases, 42 microbial species were identified. <it>S. aureus </it>was the major prevalent pathogen and MRSA accounted for 19.8% of NF cases. Most patients had many coexisting medical conditions, including diabetes mellitus, followed by hypertension, chronic azotemia and chronic hepatic disease in order of decreasing prevalence. Patients with MRSA infection tended to have more severe clinical outcomes in terms of amputation rate (p < 0.05) and reconstruction rate (p = 0.001) than those with methicillin-sensitive <it>S. aureus </it>or non-<it>S. aureus </it>infection. NF patients infected by HA-MRSA had a significantly higher amputation rate, comorbidity, C-reactive protein level, and involvement of lower extremity than those infected by CA-MRSA. In addition to over 90% of MRSA resistant to erythromycin and clindamycin, HA-MRSA was more resistant than CA-MRSA to trimethoprim-sulfamethoxazole (45.8% <it>vs</it>. 4%). ST59/pulsotype C/SCC<it>mec </it>IV and ST239/pulsotype A/SCC<it>mec </it>III isolates were the most prevalent CA- and HA-MRSA, respectively in 16 isolates obtained in 2008. In contrast to the gene for γ-hemolysin found in all MRSA, the gene for Panton-Valentine leukocidin was only identified in ST59 MRSA isolates. Other three virulence factors TSST-1, ETA, and ETB were occasionally identified in MRSA isolates tested.</p> <p>Conclusion</p> <p>NF patients with MRSA infection, especially HA-MRSA infection, had more severe clinical outcomes than those infected by other microbial. The prevalent NF-associated MRSA clones in Taiwan differed distinctly from the most predominant NF-associated USA300 CA-MRSA clone in the USA. Initial empiric antimicrobials with a broad coverage for MRSA should be considered in the treatment of NF patients in an endemic area.</p

Identification and differential regional expression of KOR-3/ORL-1 gene splice variants in mouse brain

AbstractKOR-3, also known as ORL-1, is a member of the opioid receptor family, encoding the murine receptor for orphanin FQ/nociceptin. In the current studies we have identified five different splice variants of KOR-3 in mouse brain, three of which have not been previously reported. In addition to variants with a 15 bp deletion at the 3′-end of the first coding exon (KOR-3d) and an 81 bp insertion between the second and third coding exons (KOR-3e), three new variants with insertions of 34 (KOR-3a), 98 (KOR-3b), and 139 bp (KOR-3c) between the first and second coding exons have been obtained. The expression of the three variants in mouse brain varies markedly among brain regions with a distribution which is quite distinct from KOR-3 itself. Of greatest interest was the presence of high levels of KOR-3a in the striatum, a region with no demonstrable KOR-3, and in the cortex. KOR-3c was seen in the periaqueductal gray and hypothalamus, regions where KOR-3 predominated. The brainstem had similar levels of KOR-3, KOR-3a, and KOR-3d. In contrast, KOR-3d was most prominent in the cerebellum. KOR-3b levels were very low throughout

Ser-634 and Ser-636 of Kaposi’s Sarcoma-Associated Herpesvirus RTA are Involved in Transactivation and are Potential Cdk9 Phosphorylation Sites

The replication and transcription activator (RTA) of Kaposi’s sarcoma-associated herpesvirus (KSHV), K-RTA, is a lytic switch protein that moderates the reactivation process of KSHV latency. By mass spectrometric analysis of affinity purified K-RTA, we showed that Thr-513 or Thr-514 was the primary in vivo phosphorylation site. Thr-513 and Thr-514 are proximal to the nuclear localization signal (527KKRK530) and were previously hypothesized to be target sites of Ser/Thr kinase hKFC. However, substitutions of Thr with Ala at 513 and 514 had no effect on K-RTA subcellular localization or transactivation activity. By contrast, replacement of Ser with Ala at Ser-634 and Ser-636 located in a Ser/Pro-rich region of K-RTA, designated as S634A/S636A, produced a polypeptide with ∼10 kDa shorter in molecular weight and reduced transactivation in a luciferase reporter assay relative to the wild type. In contrast to prediction, the decrease in molecular weight was not due to lack of phosphorylation because the overall Ser and Thr phosphorylation state in K-RTA and S634A/S636A were similar, excluding that Ser-634 or Ser-636 motif served as docking sites for consecutive phosphorylation. Interestingly, S634A/S636A lost ∼30% immuno-reactivity to MPM2, an antibody specific to pSer/pThr-Pro motif, indicating that 634SPSP637 motif was in vivo phosphorylated. By in vitro kinase assay, we showed that K-RTA is a substrate of CDK9, a Pro-directed Ser/Thr kinase central to transcriptional regulation. Importantly, the capability of K-RTA in associating with endogenous CDK9 was reduced in S634A/S636A, which suggested that Ser-634 and Ser-636 may be involved in CDK9 recruitment. In agreement, S634A/S636A mutant exhibited ∼25% reduction in KSHV lytic cycle reactivation relative to that by the wild type K-RTA. Taken together, our data propose that Ser-634 and Ser-636 of K-RTA are phosphorylated by host transcriptional kinase CDK9 and such a process contributes to a full transcriptional potency of K-RTA

Minimizing the Total Service Time of Discrete Dynamic Berth Allocation Problem by an Iterated Greedy Heuristic

Berth allocation is the forefront operation performed when ships arrive at a port and is a critical task in container port optimization. Minimizing the time ships spend at berths constitutes an important objective of berth allocation problems. This study focuses on the discrete dynamic berth allocation problem (discrete DBAP), which aims to minimize total service time, and proposes an iterated greedy (IG) algorithm to solve it. The proposed IG algorithm is tested on three benchmark problem sets. Experimental results show that the proposed IG algorithm can obtain optimal solutions for all test instances of the first and second problem sets and outperforms the best-known solutions for 35 out of 90 test instances of the third problem set