Discriminatory vs Uniform Price Auction: Auction Revenue

We compare auction revenues from discriminatory auctions and uniform price auctions in the case of the Korean treasury bonds auction market. For this purpose, we employ detailed bidder level data for each of 16 discriminatory auctions recently carried out in Korea. We first theoretically recover unobserved individual bidding functions under counter-factual uniform price auctions from the observed bidding functions under the actual discriminatory auctions, and then empirically estimate revenue differences. To test significance of the auction revenue differences, we use Bootstrap re-sampling methods where uncertainty in the cut-off yield spreads and uncertainty in the bidders are addressed individually as well as simultaneously. Our results indicate that uniform price auction increases the auction revenue relative to the discriminatory auction in most of the 16 cases, justifying the Korean government’s decision to switch to the uniform price auction mechanism in August 2000Treasury bonds auction, discriminatory auction, uniform price auction, hazard rate, Bootstrap re-sampling, yield spread, bidding function, bid shading

Acceleration of Large-Scale Electronic Structure Simulations with Heterogeneous Parallel Computing

Large-scale electronic structure simulations coupled to an empirical modeling approach are critical as they present a robust way to predict various quantum phenomena in realistically sized nanoscale structures that are hard to be handled with density functional theory. For tight-binding (TB) simulations of electronic structures that normally involve multimillion atomic systems for a direct comparison to experimentally realizable nanoscale materials and devices, we show that graphical processing unit (GPU) devices help in saving computing costs in terms of time and energy consumption. With a short introduction of the major numerical method adopted for TB simulations of electronic structures, this work presents a detailed description for the strategies to drive performance enhancement with GPU devices against traditional clusters of multicore processors. While this work only uses TB electronic structure simulations for benchmark tests, it can be also utilized as a practical guideline to enhance performance of numerical operations that involve large-scale sparse matrices

Genome-wide analysis to predict protein sequence variations that change phosphorylation sites or their corresponding kinases

We define phosphovariants as genetic variations that change phosphorylation sites or their interacting kinases. Considering the essential role of phosphorylation in protein functions, it is highly likely that phosphovariants change protein functions and may constitute a proportion of the mechanisms by which genetic variations cause individual differences or diseases. We categorized phosphovariants into three subtypes and developed a system that predicts them. Our method can be used to screen important polymorphisms and help to identify the mechanisms of genetic diseases

Lack of Association between Apolipoprotein E Polymorphism with Age at Onset of Subcortical Vascular Dementia

Background and Purpose: The relationship between apolipoprotein E (ApoE) and onset of vascular dementia remains controversial. The aim of this study was to evaluate the relationship between ApoE polymorphism and the onset of subcortical vascular dementia (SVaD) compared to Alzheimer’s disease (AD) and normal controls. Methods: The study was comprised of 61 patients with SVaD (42 Binswanger type, 19 lacunar type) and 112 patients with AD (16 early-onset AD, 96 late-onset AD) as well as 284 age-, gender- and education-matched normal controls. The diagnosis of SVaD was based on modified NINDS-AIREN criteria, and the diagnosis of AD was based on NINCDS-ADRDA criteria. ApoE polymorphism was genotyped in all participants. Results: None of the three ApoE alleles was more prevalent in SVaD patients compared to normal controls, which was the case when both Binswanger and lacunar types were analyzed separately. ApoE Ε4 did not accelerate the onset of SVaD (OR 1.66, 95% CI: 0.8–3.4), in contrast to a significant relation with late-onset AD (OR 3.78, 95% CI: 2.2–6.5). Conclusion: Our results suggest that ApoE polymorphism is not associated with the onset of SVaD and that the two subtypes of SVaD may share similar pathophysiologies

Plasma 2020 - Intracluster Medium Plasmas

Galaxy clusters are the largest and most massive bound objects resulting from cosmic hierarchical structure formation. Baryons account for somewhat more than 10% of that mass, with roughly 90% of the baryonic matter distributed throughout the clusters as hot ($T>1$ keV), high-$\beta$, very weakly collisional plasma; the so-called "intracluster medium" (ICM). Cluster mergers, close gravitational encounters and accretion, along with violent feedback from galaxies and relativistic jets from active galactic nuclei, drive winds, gravity waves, turbulence and shocks within the ICM. Those dynamics, in turn, generate cluster-scale magnetic fields and accelerate and mediate the transport of high-energy charged particles. Kinetic-scale, collective plasma processes define the basic character and fundamental signatures of these ICM phenomena, which are observed primarily by X-ray and radio astronomers.Comment: 5 pages, 2 figures, submitted for the 2020 Decadal Assessment of Plasma Scienc

Identification of protein functions using a machine-learning approach based on sequence-derived properties

<p>Abstract</p> <p>Background</p> <p>Predicting the function of an unknown protein is an essential goal in bioinformatics. Sequence similarity-based approaches are widely used for function prediction; however, they are often inadequate in the absence of similar sequences or when the sequence similarity among known protein sequences is statistically weak. This study aimed to develop an accurate prediction method for identifying protein function, irrespective of sequence and structural similarities.</p> <p>Results</p> <p>A highly accurate prediction method capable of identifying protein function, based solely on protein sequence properties, is described. This method analyses and identifies specific features of the protein sequence that are highly correlated with certain protein functions and determines the combination of protein sequence features that best characterises protein function. Thirty-three features that represent subtle differences in local regions and full regions of the protein sequences were introduced. On the basis of 484 features extracted solely from the protein sequence, models were built to predict the functions of 11 different proteins from a broad range of cellular components, molecular functions, and biological processes. The accuracy of protein function prediction using random forests with feature selection ranged from 94.23% to 100%. The local sequence information was found to have a broad range of applicability in predicting protein function.</p> <p>Conclusion</p> <p>We present an accurate prediction method using a machine-learning approach based solely on protein sequence properties. The primary contribution of this paper is to propose new <it>PNPRD </it>features representing global and/or local differences in sequences, based on positively and/or negatively charged residues, to assist in predicting protein function. In addition, we identified a compact and useful feature subset for predicting the function of various proteins. Our results indicate that sequence-based classifiers can provide good results among a broad range of proteins, that the proposed features are useful in predicting several functions, and that the combination of our and traditional features may support the creation of a discriminative feature set for specific protein functions.</p