Abstract. While cloud computing has certainly gained attention, the potential for increased uptake of the technology is still large. As a consequence, how to move and migrate to the cloud is an unanswered question for many organisations. Gaining an understanding of cloud migration processes from on-premise architectures is our aim here. For this purpose, we look at three provider-driven case studies based on the common three layers of cloud computing: Infrastructure (IaaS), platform (PaaS) and software (SaaS) as a service. These shall be complemented by a fourth, independent systems integration perspective. We extract common migration process activities for the layer-specific processes and discuss commonalities, differences and open issues. The results presented are based on expert interviews and focus groups held with major international cloud solution providers and independent consultants.

BackgroundEukaryotic promoter prediction using computational analysis techniques is one of the most difficult jobs in computational genomics that is essential for constructing and understanding genetic regulatory networks. The increased availability of sequence data for various eukaryotic organisms in recent years has necessitated for better tools and techniques for the prediction and analysis of promoters in eukaryotic sequences. Many promoter prediction methods and tools have been developed to date but they have yet to provide acceptable predictive performance. One obvious criteria to improve on current methods is to devise a better system for selecting appropriate features of promoters that distinguish them from non-promoters. Secondly improved performance can be achieved by enhancing the predictive ability of the machine learning algorithms used.ResultsIn this paper, a novel approach is presented in which 128 4-mer motifs in conjunction with a non-linear machine-learning algorithm utilising a Support Vector Machine (SVM) are used to distinguish between promoter and non-promoter DNA sequences. By applying this approach to plant, Drosophila, human, mouse and rat sequences, the classification model has showed 7-fold cross-validation percentage accuracies of 83.81%, 94.82%, 91.25%, 90.77% and 82.35% respectively. The high sensitivity and specificity value of 0.86 and 0.90 for plant; 0.96 and 0.92 for Drosophila; 0.88 and 0.92 for human; 0.78 and 0.84 for mouse and 0.82 and 0.80 for rat demonstrate that this technique is less prone to false positive results and exhibits better performance than many other tools. Moreover, this model successfully identifies location of promoter using TATA weight matrix.ConclusionThe high sensitivity and specificity indicate that 4-mer frequencies in conjunction with supervised machine-learning methods can be beneficial in the identification of RNA pol II promoters comparative to other methods. This approach can be extended to identify promoters in sequences for other eukaryotic genomes.

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We outline the concept of an open technology platform that builds upon a publicly accessible library of fluidic designs, manufacturing processes, and experimental characterization, as well as virtualization by a "digital twin" based on modeling, simulation, and cloud computing. Backed by the rapidly emerging Web3 technology "Blockchain," we significantly extend traditional approaches to effectively incentivize broader participation by an interdisciplinary "value network" of diverse players. Ranging from skilled individuals (the "citizen scientist" and the "garage entrepreneur") and more established research institutions to companies with their infrastructures, equipment, and services, the novel platform approach enables all stakeholders to jointly contribute to value creation along more decentralized supply chain designs including research and technology development (RTD). A blockchain-enabled token economy efficiently leverages the "Wisdom of the Crowds" and secures "trust" and transparency by reputation systems requiring "skin in the game" from contributors. Prediction markets are created for guiding decision making, planning, and allocation of funding; competitive parallelization of work and its validation from independent participants substantially enhances quality, credibility, and speed of project outcomes in the real world along the entire path from RTD, fabrication, and testing to eventual commercialization. This novel, Blockchain-backed, open platform concept can be led by a corporation, academic entity, a loosely organized group, or even "chieflessly" within a smart-contract encoded Decentralized Autonomous Organization (DAO). The proposed strategy is particularly attractive for highly interdisciplinary fields like microfluidic Lab-on-a-Chip systems in the context of manifold applications in the Life Sciences. As an exemplar, we outline the centrifugal "Lab-on-a-Disc" technology. Rather than engaging in all sub-disciplines themselves, many smaller, highly innovative actors can focus on strengthening the product component distinguishing their unique selling point (USP), e.g., a particular bioassay, detection scheme, or application scenario. In this effort, system integrators access underlying commons like fluidic design, manufacture, instrumentation, and software from a more resilient and diversified supply chain, e.g., based on a verified pool of community-endorsed or certified providers.

An overwhelming majority of experts has been flagging for decades that "Saving the Planet" requires immediate, persistent and drastic action to curb a variety of catastrophic risks over the 21st century. However, despite compelling evidence and a range of suggested solutions, transnational coordination of effective measures to protect our biosphere continues to fall short. To remedy, we propose a novel platform for addressing the central issue of affording trust, transparency and truth while minimizing administrative overheads. This will empower an even loosely organized, global grassroots community to coordinate a large-scale project on a shared goal ("Commons") spanning the digital and real world. The Web3 concept is based on the swiftly emerging "Blockchain" and related cryptographic, distributed and permissionless technologies. "Wisdom of the crowds" mechanisms involving competitive parallelization and prediction markets are enabled by formalized reputation and staking to incentivize high-quality work, fair validation and best management practice. While these mechanisms have been (mostly separately) applied to science, business, governance, web, sensor, information and communication technologies (ICT), our integrative approach around Blockchainenabled 'operating principles and protocols' sets the basis for designing novel forms of potentially crowdfunded Decentralized Autonomous Organizations (DAOs).