QCD evolution of superfast quarks

Recent high-precision measurements of nuclear deep inelastic scattering at high x and moderate 6superfast region, in which the momentum fraction of the nucleon carried by its constituent quark is larger than the total fraction of the nucleon at rest, x\u3e1. We derive the leading-order QCD evolution equation for such quarks with the goal of relating the moderate-Q2 data to the two earlier measurements of superfast quark distributions at large 601.1 region is somewhat in the middle of the neutrino-nuclear and muon-nuclear scattering data

Probing superfast quarks in nuclei through dijet production at the LHC

We investigate dijet production from proton-nucleus collisions at the Large Hadron Collider (LHC) as a means for observing superfast quarks in nuclei with Bjorken $x>1$. Kinematically, superfast quarks can be identified through directly measurable jet kinematics. Dynamically, their description requires understanding several elusive properties of nuclear QCD, such as nuclear forces at very short distances, as well as medium modification of parton distributions in nuclei. In the present work, we develop a model for nuclear parton distributions at large $x$ in which the nuclear dynamics at short distance scales are described by two- and three-nucleon short range correlations (SRCs). Nuclear modifications are accounted for using the color screening model, and an improved description of the EMC effect is reached by using a structure function parametrization that includes higher-twist contributions. We apply QCD evolution at the leading order to obtain nuclear parton distributions in the kinematic regime of the LHC, and based on the obtained distributions calculate the cross section for dijet production. We find not only that superfast quarks can be observed at the LHC, but also that they provide sensitivity to the practically unexplored three-nucleon SRCs in nuclei. Additionally, the LHC can extend our knowledge of the EMC effect to large $Q^2$ where higher-twist effects are negligible.Comment: 44 pages, 17 figures, final version to be published in EJP

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts

Competitiveness and Macroeconomic Impacts of Reduced Wait Times at U.S. Land Freight Border Crossings

We analyze the macroeconomic and trade impacts of reducing wait times by adding inspection staff at each of the twelve major land freight crossings of the U.S. The change in wait time stemming from staffing changes is first estimated on the basis of primary data and then translated into changes in freight costs through a logistical model. The transportation cost changes are then fed into a multi-country computable general equilibrium model. We find that adding one inspection agent at each land border crossing would, on average per crossing, generate an increase in U.S. GDP of $250 thousand and 2.55 additional jobs