Blockchain-based Perfect Sharing Project Platform based on the Proof of Atomicity Consensus Algorithm

The Korean government funded 12.8 billion USD to 652 research and development (R&D) projects supported by 20 ministries in 2019. Every year, various organizations are supported to conduct R&D projects focusing on selected core technologies by evaluating emerging technologies which industries are planning to develop. To manage the whole cycle of national R&D projects, information sharing on national R&D projects is very essential. The blockchain technology is considered as a core solution to share information reliably and prevent forgery in various fields. For efficient management of national R&D projects, we enhance and analyse the Perfect Sharing Project (PSP)-Platform based on a new blockchain-based platform for information sharing and forgery prevention. It is a shared platform for national ICT R&D projects management with excellent performance in preventing counterfeiting. As a consensus algorithm is very important to prevent forgery in blockchain, we survey not only architectural aspects and examples of the platform but also the consensus algorithms. Considering characteristics of the PSP-Platform, we adopt an atomic proof (POA) consensus algorithm as a new consensus algorithm in this paper. To prove the validity of the POA consensus algorithm, we have conducted experiments. The experiment results show the outstanding performance of the POA consensus algorithm used in the PSP-Platform in terms of block generation delay and block propagation time

Design and Implementation of a Trust Information Management Platform for Social Internet of Things Environments

As the vast amount of data in social Internet of Things (IoT) environments considering interactions between IoT and people is accumulated and processed through cloud and big data technologies, the services that utilize them are applied in various fields. The trust between IoT devices and their data is recognized as the core of IoT ecosystem creation and growth. Connection with suspicious IoT devices may pose a risk to services and system operation. Therefore, it is essential to analyze and manage trust information for devices, services, and people, as well as to provide the trust information to the other devices or users that need it. This paper presents a trust information management framework which contains a generic IoT reference model with trust capabilities to achieve the goal of converged trust information management. Additionally, a trust information management platform (TIMP) consisting of trust agents, trust information brokers, and trust information management systems has been proposed, which aims to provide trustworthy and safe interactions among people, virtual objects, and physical things. Implementing and deploying a TIMP enables a trustworthy ecosystem to be built while activating social IoT businesses by reducing transaction costs, as well as by eliminating the uncertainties in the use of social IoT services and data transactions

Improving digital image watermarking by means of optimal channel selection

Supporting safe and resilient authentication and integrity of digital images is of critical importance in a time of enormous creation and sharing of these contents. This paper presents an improved digital image watermarking model based on a coefficient quantization technique that intelligently encodes the owner’s information for each color channel to improve imperceptibility and robustness of the hidden information. Concretely, a novel color channel selection mechanism automatically selects the optimal HL4 and LH4 wavelet coefficient blocks for embedding binary bits by adjusting block differences, calculated between LH and HL coefficients of the host image. The channel selection aims to minimize the visual difference between the original image and the embedded image. On the other hand, the strength of the watermark is controlled by a factor to achieve an acceptable tradeoff between robustness and imperceptibility. The arrangement of the watermark pixels before shuffling and the channel into which each pixel is embedded is ciphered in an associated key. This key is utterly required to recover the original watermark, which is extracted through an adaptive clustering thresholding mechanism based on the Otsu’s algorithm. Benchmark results prove the model to support imperceptible watermarking as well as high robustness against common attacks in image processing, including geometric, non-geometric transformations, and lossy JPEG compression. The proposed method enhances more than 4 dB in the watermarked image quality and significantly reduces Bit Error Rate in the comparison of state-of-the-art approaches

Traffic Behavior Recognition Using the Pachinko Allocation Model

CCTV-based behavior recognition systems have gained considerable attention in recent years in the transportation surveillance domain for identifying unusual patterns, such as traffic jams, accidents, dangerous driving and other abnormal behaviors. In this paper, a novel approach for traffic behavior modeling is presented for video-based road surveillance. The proposed system combines the pachinko allocation model (PAM) and support vector machine (SVM) for a hierarchical representation and identification of traffic behavior. A background subtraction technique using Gaussian mixture models (GMMs) and an object tracking mechanism based on Kalman filters are utilized to firstly construct the object trajectories. Then, the sparse features comprising the locations and directions of the moving objects are modeled by PAMinto traffic topics, namely activities and behaviors. As a key innovation, PAM captures not only the correlation among the activities, but also among the behaviors based on the arbitrary directed acyclic graph (DAG). The SVM classifier is then utilized on top to train and recognize the traffic activity and behavior. The proposed model shows more flexibility and greater expressive power than the commonly-used latent Dirichlet allocation (LDA) approach, leading to a higher recognition accuracy in the behavior classification

Polycomb- and Methylation-Independent Roles of EZH2 as a Transcription Activator

Summary: Enhancer of Zeste 2 (EZH2) is the enzymatic subunit of Polycomb Repressive Complex 2 (PRC2), which catalyzes histone H3 lysine 27 trimethylation (H3K27me3) at target promoters for gene silencing. Here, we report that EZH2 activates androgen receptor (AR) gene transcription through direct occupancy at its promoter. Importantly, this activating role of EZH2 is independent of PRC2 and its methyltransferase activities. Genome-wide assays revealed extensive EZH2 occupancy at promoters marked by either H3K27ac or H3K27me3, leading to gene activation or repression, respectively. Last, we demonstrate enhanced efficacy of enzymatic EZH2 inhibitors when used in combination with AR antagonists in blocking the dual roles of EZH2 and suppressing prostate cancer progression in vitro and in vivo. Taken together, our study reports EZH2 as a transcriptional activator, a key target of which is AR, and suggests a drug-combinatory approach to treat advanced prostate cancer. : Kim et al. report EZH2 as a transcriptional activator that directly induces AR gene expression in a Polycomb- and methylation-independent manner, providing a mechanism to escape enzymatic EZH2 inhibitors. Combination of inhibitors with AR-targeted therapies showed a strong synergy in blocking the EZH2 downstream pathways and suppressing prostate cancer progression. Keywords: epigenetic silencing, transcription activator, androgen receptor inhibitor, enzymatic EZH2 inhibitor, GSK126, EPZ-6438, AR antagonist enzalutamide, ChIP-se

New Photometric Pipeline To Explore Temporal And Spatial Variability With Kmtnet Deep-South Observations

The DEEP-South (the Deep Ecliptic Patrol of the Southern Sky) photometric census of small Solar System bodies produces massive time-series data of variable, transient or moving objects as a by-product. To fully investigate unexplored variable phenomena, we present an application of multi-aperture photometry and FastBit indexing techniques for faster access to a portion of the DEEP-South year-one data. Our new pipeline is designed to perform automated point source detection, robust high-precision photometry and calibration of non-crowded fields which have overlap with previously surveyed areas. In this paper, we show some examples of catalog-based variability searches to find new variable stars and to recover targeted asteroids. We discover 21 new periodic variables with period ranging between 0.1 and 31 days, including four eclipsing binary systems (detached, over-contact, and ellipsoidal variables), one white dwarf/M dwarf pair candidate, and rotating variable stars. We also recover astrometry (< +/- 1-2 arcsec level accuracy) and photometry of two targeted near-earth asteroids, 2006 DZ169 and 1996 SK, along with the small- (similar to 0.12 mag) and relatively large-amplitude (similar to 0.5 mag) variations of their dominant rotational signals in R-band

Targeting lonidamine to mitochondria mitigates lung tumorigenesis and brain metastasis

WOS:000468174600005Lung cancer often has a poor prognosis, with brain metastases a major reason for mortality. We modified lonidamine (LND), an antiglycolytic drug with limited efficacy, to mitochondria targeted mito-lonidamine (Mito-LND) which is 100-fold more potent. Mito-LND, a tumor selective inhibitor of oxidative phosphorylation, inhibits mitochondrial bioenergetics in lung cancer cells and mitigates lung cancer cell viability, growth, progression, and metastasis of lung cancer xenografts in mice. Mito-LND blocks lung tumor development and brain metastasis by inhibiting mitochondrial bioenergetics, stimulating the formation of reactive oxygen species, oxidizing mitochondrial peroxiredoxin, inactivating AKT/mTOR/p70S6K signaling, and inducing autophagic cell death in lung cancer cells. Mito-LND causes no toxicity in mice even when administered for eight weeks at 50 times the effective cancer inhibitory dose. Collectively, these findings show that mitochondrial targeting of LND is a promising therapeutic approach for investigating the role of autophagy in mitigating lung cancer development and brain metastasis

Posttranslational regulation of FOXA1 by Polycomb and BUB3/USP7 deubiquitin complex in prostate cancer

Forkhead box protein A1 (FOXA1) is essential for androgen-dependent prostate cancer (PCa) growth. However, how FOXA1 levels are regulated remains elusive and its therapeutic targeting proven challenging. Here, we report FOXA1 as a nonhistone substrate of enhancer of zeste homolog 2 (EZH2), which methylates FOXA1 at lysine-295. This methylation is recognized by WD40 repeat protein BUB3, which subsequently recruits ubiquitin-specific protease 7 (USP7) to remove ubiquitination and enhance FOXA1 protein stability. They functionally converge in regulating cell cycle genes and promoting PCa growth. FOXA1 is a major therapeutic target of the inhibitors of EZH2 methyltransferase activities in PCa. FOXA1-driven PCa growth can be effectively mitigated by EZH2 enzymatic inhibitors, either alone or in combination with USP7 inhibitors. Together, our study reports EZH2-catalyzed methylation as a key mechanism to FOXA1 protein stability, which may be leveraged to enhance therapeutic targeting of PCa using enzymatic EZH2 inhibitors