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A Hot Start Polymerase Chain Reaction (PCR) entails the withholding of at least one reagent from the reaction mixture until the reaction tube temperature has reached 60-80 degrees C. Hot Start amplification with an AmpliWax vapor barrier uses a layer of solid wax to separate the retained reagent(s) and the test sample from the bulk of the reagents until the first heating step of automated thermal cycling melts the wax and convectively mixes the two aqueous layers. Wax-mediated Hot Start PCR greatly increases the specificity, yield, and precision of amplifying low copy numbers of three HIV targets. In the presence of 1 microgram of human placental DNA (1.6 x 10(5) diploid genomes) the specificity improvement entails considerable to complete reduction in the amplification of mis-primed sequences and putative primer oligomers. When mis-priming is negligible, the procedural improvement still suppresses putative primer oligomerization. Hot Start PCR with an AmpliWax vapor barrier permits routine amplification of a single target molecule with detection by ethidium stained gel electrophoresis; nonisotopically visualized probing suffices for confirmation. The improved amplification performance is evident for target copy numbers below approximately 10(3).

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This work presents a systemic top-down visualization of Bitcoin transaction activity to explore dynamically generated patterns of algorithmic behavior. Bitcoin dominates the cryptocurrency markets and presents researchers with a rich source of real-time transactional data. The pseudonymous yet public nature of the data presents opportunities for the discovery of human and algorithmic behavioral patterns of interest to many parties such as financial regulators, protocol designers, and security analysts. However, retaining visual fidelity to the underlying data to retain a fuller understanding of activity within the network remains challenging, particularly in real time. We expose an effective force-directed graph visualization employed in our large-scale data observation facility to accelerate this data exploration and derive useful insight among domain experts and the general public alike. The high-fidelity visualizations demonstrated in this article allowed for collaborative discovery of unexpected high frequency transaction patterns, including automated laundering operations, and the evolution of multiple distinct algorithmic denial of service attacks on the Bitcoin network.

Explaining outputs determined algorithmically by machines is one of the most pressing and studied problems in Artificial Intelligence (AI) nowadays, but the equally pressing problem of using AI to explain outputs determined by humans is less studied. In this paper we advance a novel methodology integrating case-based reasoning and computational argumentation from AI to explain outcomes , determined by humans or by machines, indifferently, for cases characterised by discrete (static) features and/or (dynamic) stages. At the heart of our methodology lies the concept of arbitrated argumentative disputes between two fictitious disputants arguing, respectively, for or against a case's output in need of explanation, and where this case acts as an arbiter. Specifically, in explaining the outcome of a case in question, the disputants put forward as arguments relevant cases favouring their respective positions, with arguments/cases conflicting due to their features, stages and outcomes, and the applicability of arguments/cases arbitrated by the features and stages of the case in question. We in addition use arbitrated dispute trees to identify the excess features that help the winning disputant to win the dispute and thus complement the explanation.We evaluate our novel methodology theoretically, proving desirable properties thereof, and empirically, in the context of primary legislation in the United Kingdom (UK), concerning the passage of Bills that may or may not become laws. High-level factors underpinning a Bill's passage are its contentagnostic features such as type, number of sponsors, ballot order, as well as the UK Parliament's rules of conduct. Given high numbers of proposed legislation (hundreds of Bills a year), it is hard even for legal experts to explain on a large scale why certain Bills pass or not. We show how our methodology can address this problem by automatically providing high-level explanations of why Bills pass or not, based on the given Bills and their content-agnostic features.