A π-Calculus Specification of Prolog

A clear and modular specification of Prolog using the π-calculus is presented in this paper. Prolog goals are represented as π-calculus processes, and Prolog predicate definitions are translated into π-calculus agent definitions. Prolog\u27s depth-first left-right control strategy as well as the cut control operator are modeled by the synchronized communication among processes, which is similar in spirit to continuation-passing style implementation of Prolog. Prolog terms are represented by persistent processes, while logical variables are modeled by complex processes with channels that, at various times, can be written, read, and reset. Both unifications with and without backtracking are specified by π-calculus agent definitions. A smooth merging of the specification for control and the specification for unification gives a full specification for much of Prolog. Some related and further works are also discussed

Attosecond gamma-ray pulses via nonlinear Compton scattering in the radiation dominated regime

The feasibility of generation of bright ultrashort gamma-ray pulses is demonstrated in the interaction of a relativistic electron bunch with a counterpropagating tightly-focused superstrong laser beam in the radiation dominated regime. The Compton scattering spectra of gamma-radiation are investigated using a semiclassical description for the electron dynamics in the laser field and a quantum electrodynamical description for the photon emission. We demonstrate the feasibility of ultrashort gamma-ray bursts of hundreds of attoseconds and of dozens of megaelectronvolt photon energies in the near-backwards direction of the initial electron motion. The tightly focused laser field structure and radiation reaction are shown to be responsible for such short gamma-ray bursts which are independent of the durations of the electron bunch and of the laser pulse. The results are measurable with the laser technology available in a near-future

Troubleshooting Arterial-Phase MR Images of Gadoxetate Disodium-Enhanced Liver.

Gadoxetate disodium is a widely used magnetic resonance (MR) contrast agent for liver MR imaging, and it provides both dynamic and hepatobiliary phase images. However, acquiring optimal arterial phase images at liver MR using gadoxetate disodium is more challenging than using conventional extracellular MR contrast agent because of the small volume administered, the gadolinium content of the agent, and the common occurrence of transient severe motion. In this article, we identify the challenges in obtaining high-quality arterial-phase images of gadoxetate disodium-enhanced liver MR imaging and present strategies for optimizing arterial-phase imaging based on the thorough review of recent research in this field

New Insights into Traffic Dynamics: A Weighted Probabilistic Cellular Automaton Model

From the macroscopic viewpoint for describing the acceleration behavior of drivers, this letter presents a weighted probabilistic cellular automaton model (the WP model, for short) by introducing a kind of random acceleration probabilistic distribution function. The fundamental diagrams, the spatio-temporal pattern are analyzed in detail. It is shown that the presented model leads to the results consistent with the empirical data rather well, nonlinear velocity-density relationship exists in lower density region, and a new kind of traffic phenomenon called neo-synchronized flow is resulted. Furthermore, we give the criterion for distinguishing the high-speed and low-speed neo-synchronized flows and clarify the mechanism of this kind of traffic phenomena. In addition, the result that the time evolution of distribution of headways is displayed as a normal distribution further validates the reasonability of the neo-synchronized flow. These findings suggest that the diversity and randomicity of drivers and vehicles has indeed remarkable effect on traffic dynamics.Comment: 12 pages, 5 figures, submitted to Europhysics Letter

Automatic discovery of photoisomerization mechanisms with nanosecond machine learning photodynamics simulations

Photochemical reactions are widely used by academic and industrial researchers to construct complex molecular architectures via mechanisms that often require harsh reaction conditions. Photodynamics simulations provide time-resolved snapshots of molecular excited-state structures required to understand and predict reactivities and chemoselectivities. Molecular excited-states are often nearly degenerate and require computationally intensive multiconfigurational quantum mechanical methods, especially at conical intersections. Non-adiabatic molecular dynamics require thousands of these computations per trajectory, which limits simulations to ∼1 picosecond for most organic photochemical reactions. Westermayr et al. recently introduced a neural-network-based method to accelerate the predictions of electronic properties and pushed the simulation limit to 1 ns for the model system, methylenimmonium cation (CH$_{2}$NH$_{2}$$^{+}$). We have adapted this methodology to develop the Python-based, Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics (PyRAI$^{2}$MD) software for the cis–trans isomerization of trans-hexafluoro-2-butene and the 4π-electrocyclic ring-closing of a norbornyl hexacyclodiene. We performed a 10 ns simulation for trans-hexafluoro-2-butene in just 2 days. The same simulation would take approximately 58 years with traditional multiconfigurational photodynamics simulations. We generated training data by combining Wigner sampling, geometrical interpolations, and short-time quantum chemical trajectories to adaptively sample sparse data regions along reaction coordinates. The final data set of the cis–trans isomerization and the 4π-electrocyclic ring-closing model has 6207 and 6267 data points, respectively. The training errors in energy using feedforward neural networks achieved chemical accuracy (0.023–0.032 eV). The neural network photodynamics simulations of trans-hexafluoro-2-butene agree with the quantum chemical calculations showing the formation of the cis-product and reactive carbene intermediate. The neural network trajectories of the norbornyl cyclohexadiene corroborate the low-yielding syn-product, which was absent in the quantum chemical trajectories, and revealed subsequent thermal reactions in 1 ns

IgG and IgM Autoantibody Differences in Discoid and Systemic Lupus Patients

Systemic lupus erythematosus (SLE) patients with discoid lupus erythematosus (DLE) were reported to have milder disease. To test this observation, we used sandwich arrays containing 98 autoantigens to compare autoantibody profiles of SLE subjects without DLE (DLE-SLE+) (N=9), SLE subjects with DLE (DLE+SLE+) (N=10), DLE subjects without SLE (DLE+SLE-) (N=11), and healthy controls (N=11). We validated differentially expressed autoantibodies using immunoassays in DLE-SLE+ (N=18), DLE+SLE+ (N=17), DLE+SLE- (N=23), and healthy subjects (N=22). Arrays showed 15 IgG autoantibodies (10 against nuclear antigens) and 4 IgM autoantibodies that were differentially expressed (q-value<0.05). DLE-SLE+ subjects had higher IgG autoantibodies against double-stranded DNA (dsDNA), single-stranded DNA (ssDNA), double-stranded RNA (dsRNA), histone H2A and H2B, and SS-A (52kDa) compared with all other groups including DLE+SLE+ subjects (P<0.05). Immunoassays measuring anti-dsDNA, -ssDNA, and -SS-A (52kDa) IgG autoantibodies showed similar trends (P<0.05). Healthy and DLE+SLE- subjects expressed higher IgM autoantibodies against alpha beta crystallin, lipopolysaccharide, heat-shock cognate 70, and desmoglein-3 compared with DLE+SLE+ and DLE-SLE+ subjects. IgG:IgM ratios of autoantibodies against nuclear antigens progressively rose from healthy to DLE-SLE+ subjects. In conclusion, lower IgG autoantibodies against nuclear antigens in DLE+SLE+ versus DLE-SLE+ subjects suggest that DLE indicates lower disease severity. Higher IgM autoantibodies against selected antigens in healthy and DLE+SLE- subjects may be nonpathogenic

Application of a synthetic human proteome to autoantigen discovery through PhIP-Seq

In this study, we improve on current autoantigen discovery approaches by creating a synthetic representation of the complete human proteome, the T7 “peptidome” phage display library (T7-Pep), and use it to profile the autoantibody repertoires of individual patients. We provide methods for 1) designing and cloning large libraries of DNA microarray-derived oligonucleotides encoding peptides for display on bacteriophage, and 2) analysis of the peptide libraries using high throughput DNA sequencing. We applied phage immunoprecipitation sequencing (PhIP-Seq) to identify both known and novel autoantibodies contained in the spinal fluid of three patients with paraneoplastic neurological syndromes. We also show how our approach can be used more generally to identify peptide-protein interactions and point toward ways in which this technology will be further developed in the future. We envision that PhIP-Seq can become an important new tool in autoantibody analysis, as well as proteomic research in general

Ginzburg Criterion for Coulombic Criticality

To understand the range of close-to-classical critical behavior seen in various electrolytes, generalized Debye-Hueckel theories (that yield density correlation functions) are applied to the restricted primitive model of equisized hard spheres. The results yield a Landau-Ginzburg free-energy functional for which the Ginzburg criterion can be explicitly evaluated. The predicted scale of crossover from classical to Ising character is found to be similar in magnitude to that derived for simple fluids in comparable fashion. The consequences in relation to experiments are discussed briefly.Comment: 4 pages, revtex, 2 tables (latex2.09 required due to revtex's incompatibility with latex2e tables

Assessing Matched Normal and Tumor Pairs in Next-Generation Sequencing Studies

Next generation sequencing technology has revolutionized the study of cancers. Through matched normal-tumor pairs, it is now possible to identify genome-wide germline and somatic mutations. The generation and analysis of the data requires rigorous quality checks and filtering, and the current analytical pipeline is constantly undergoing improvements. We noted however that in analyzing matched pairs, there is an implicit assumption that the sequenced data are matched, without any quality check such as those implemented in association studies. There are serious implications in this assumption as identification of germline and rare somatic variants depend on the normal sample being the matched pair. Using a genetics concept on measuring relatedness between individuals, we demonstrate that the matchedness of tumor pairs can be quantified and should be included as part of a quality protocol in analysis of sequenced data. Despite the mutation changes in cancer samples, matched tumor-normal pairs are still relatively similar in sequence compared to non-matched pairs. We demonstrate that the approach can be used to assess the mutation landscape between individuals

Can One Trust Quantum Simulators?

Various fundamental phenomena of strongly-correlated quantum systems such as high-$T_c$ superconductivity, the fractional quantum-Hall effect, and quark confinement are still awaiting a universally accepted explanation. The main obstacle is the computational complexity of solving even the most simplified theoretical models that are designed to capture the relevant quantum correlations of the many-body system of interest. In his seminal 1982 paper [Int. J. Theor. Phys. 21, 467], Richard Feynman suggested that such models might be solved by "simulation" with a new type of computer whose constituent parts are effectively governed by a desired quantum many-body dynamics. Measurements on this engineered machine, now known as a "quantum simulator," would reveal some unknown or difficult to compute properties of a model of interest. We argue that a useful quantum simulator must satisfy four conditions: relevance, controllability, reliability, and efficiency. We review the current state of the art of digital and analog quantum simulators. Whereas so far the majority of the focus, both theoretically and experimentally, has been on controllability of relevant models, we emphasize here the need for a careful analysis of reliability and efficiency in the presence of imperfections. We discuss how disorder and noise can impact these conditions, and illustrate our concerns with novel numerical simulations of a paradigmatic example: a disordered quantum spin chain governed by the Ising model in a transverse magnetic field. We find that disorder can decrease the reliability of an analog quantum simulator of this model, although large errors in local observables are introduced only for strong levels of disorder. We conclude that the answer to the question "Can we trust quantum simulators?" is... to some extent.Comment: 20 pages. Minor changes with respect to version 2 (some additional explanations, added references...