TRX on Samsung’s Blockchain Keystore app

Justin Sun, the organizer and CEO of Tron reported the unification of TRX on Samsung's Blockchain Keystore application at the Samsung Developer Conference 2019 held at San Dieg go. Justin Sun, the organizer and CEO of Tron reported the unification of TRX on Samsung's Blockchain Keystore application at the Samsung Developer Conference 2019 held at San Dieg go.

Propelled in March 2019, the Samsung Blockchain Keystore is a stage that empowers its customers to appreciate full oversight over their information by managing with the private data and all the advanced keys. Consolidating TRX with it will give a great lift to both as it will effortlessly allow building dApps on the organization's gadgets.

News Source: TheCoinRepublic

Abstract
The increased availability of data and recent advancements in artificial intelligence present the unprecedented opportunities in healthcare and major challenges for the patients, developers, providers and regulators. The novel deep learning and transfer learning techniques are turning any data about the person into medical data transforming simple facial pictures and videos into powerful sources of data for predictive analytics. Presently, the patients do not have control over the access privileges to their medical records and remain unaware of the true value of the data they have. In this paper, we provide an overview of the next-generation artificial intelligence and blockchain technologies and present innovative solutions that may be used to accelerate the biomedical research and enable patients with new tools to control and profit from their personal data as well with the incentives to undergo constant health monitoring. We introduce new concepts to appraise and evaluate personal records, including the combination-, time- and relationship-value of the data. We also present a roadmap for a blockchain-enabled decentralized personal health data ecosystem to enable novel approaches for drug discovery, biomarker development, and preventative healthcare. A secure and transparent distributed personal data marketplace utilizing blockchain and deep learning technologies may be able to resolve the challenges faced by the regulators and return the control over personal data including medical records back to the individuals.
Keywords: artificial intelligence, deep learning, data management, blockchain, digital health

INTRODUCTION
The digital revolution in medicine produced a paradigm shift in the healthcare industry. One of the major benefits of the digital healthcare system and electronic medical records is the improved access to the healthcare records both for health professionals and patients. The success of initiatives that provides patients with the access to their electronic healthcare records, such as OpenNotes, suggests their potential to improve the quality and efficiency of medical care [ , ]. At the same time, biomedical data is not limited to the clinical records created by physicians, the substantial amount of data is retrieved from biomedical imaging, laboratory testing such as basic blood tests, and omics data. Notably, the amount of genomic data alone is projected to surpass the amount of data generated by other data-intensive fields such as social networks and online video-sharing platforms [ ]. National healthcare programs such as the UK Biobank (supported by the National Health Service (NHS)) [ ] or global programmes such as the LINCS consortium ( ) and the ENCODE project ( ), provide scientists with tens of thousands of high-quality samples. However, while increased data volume and complexity offers new exciting perspectives in healthcare industry development, it also introduces new challenges in data analysis and interpretation, and of course, privacy and security. Due to huge demand for the treatments and prevention of chronic diseases, mainly driven by aging of the population, there is a clear need for the new global integrative healthcare approaches [ ]. Majority of the recent approaches to personalized medicine in oncology and other diseases relied on the various data types including the multiple types of genomic [ - ], transcriptomic [ - ], microRNA [ ], proteomic [ ], antigen [ ], methylation [ ], imaging [ , ], metagenomic [ ], mitochondrial [ ], metabolic [ ], physiological [ ] and other data. And while several attempts were made to evaluate the clinical benefit of the different methods [ ] and multiple data types were used for evaluating the health status of the individual patients [ ] including the widely popularized “Snyderome” project [ ], none of these approaches are truly integrative on the population scale and compare the predictive nature and value of the various data types in the context of biomedicine. Introduction of new technologies, such as an artificial intelligence and blockchain, may enhance and scale up the pogre in health care sciences and lead to effective and cost-efficient healthcare ecosystems.
In this article we first review one of the recent achievements in next-generation artificial intelligence, deep learning, that holds the great promise as a biomedical research tool with many applications. We then discuss basic concepts of highly distributed storage systems (HDSS) as one of the advantageous solutions for data storage, introduce the open-source blockchain framework Exonum and review the application of blockchain for healthcare marketplace. For the first time we introduce half-life period of analysis significance, models of data value for single and group of users and cost of buying data in the context of biomedical applications. WHere we also present a blockchain-based platform for empowering patients to ensure that they havetake a control over their personal data, manage the access priviledges and to protect their data privacy, as well to allow patients to benefit from their data receiving a crypto tokesscurrency as a reward for their data or for healthy behaviorand to contribute to the overall biomedical pogre. We speculate that such systems may be used by the governments on the national scale to increase participation of the general public in preventative medicine and even provide the universal basic income to the their citizens willing to participate in such programs that will greatly decrease the burden of disease on the healthcare systems. Finally, we cover important aspects of data quality control using the recent advances in deep learning and other machine learning methods.