Aircraft noise prediction program theoretical manual: Rotorcraft System Noise Prediction System (ROTONET), part 4

This document describes the theoretical methods used in the rotorcraft noise prediction system (ROTONET), which is a part of the NASA Aircraft Noise Prediction Program (ANOPP). The ANOPP code consists of an executive, database manager, and prediction modules for jet engine, propeller, and rotor noise. The ROTONET subsystem contains modules for the prediction of rotor airloads and performance with momentum theory and prescribed wake aerodynamics, rotor tone noise with compact chordwise and full-surface solutions to the Ffowcs-Williams-Hawkings equations, semiempirical airfoil broadband noise, and turbulence ingestion broadband noise. Flight dynamics, atmosphere propagation, and noise metric calculations are covered in NASA TM-83199, Parts 1, 2, and 3

Use of chromatin immunoprecipitation (ChIP) to detect transcription factor binding to highly homologous promoters in chromatin isolated from unstimulated and activated primary human B cells

The Chromatin Immunoprecipiation (ChIP) provides a powerful technique for identifying the in vivo association of transcription factors with regulatory elements. However, obtaining meaningful information for promoter interactions is extremely challenging when the promoter is a member of a class of highly homologous elements. Use of PCR primers with small numbers of mutations can limit cross-hybridization with non-targeted sequences and distinguish a pattern of binding for factors with the regulatory element of interest. In this report, we demonstrate the selective in vivo association of NF-κB, p300 and CREB with the human Iγ1 promoter located in the intronic region upstream of the Cγ1 exons in the immunoglobulin heavy chain locus. These methods have the ability to extend ChIP analysis to promoters with a high degree of homology

Improved protein structure prediction using potentials from deep learning

Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence1. This problem is of fundamental importance as the structure of a protein largely determines its function2; however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous sequences, which aids in the prediction of protein structures3. Here we show that we can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force4 that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction5 (CASP13)—a blind assessment of the state of the field—AlphaFold created high-accuracy structures (with template modelling (TM) scores6 of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no structures for homologous proteins have been experimentally determined7

Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)

We describe AlphaFold, the protein structure prediction system that was entered by the group A7D in CASP13 Submissions were made by three free-modelling methods which combine the predictions of three neural networks. All three systems were guided by predictions of distances between pairs of residues produced by a neural network. Two systems assembled fragments produced by a generative neural network, one using scores from a network trained to regress GDT_TS. The third system shows that simple gradient descent on a properly constructed potential is able to perform on-par with more expensive traditional search techniques and without requiring domain segmentation. In the CASP13 free-modelling assessors' ranking by summed z-scores, this system scored highest with 68.3 vs 48.2 for the next closest group. (An average GDT_TS of 61.4.) The system produced high-accuracy structures (with GDT_TS scores of 70 or higher) for 11 out of 43 free-modelling domains. Despite not explicitly using template information, the results in the template category were comparable to the best performing template-based methods

Carbene footprinting accurately maps binding sites in protein–ligand and protein–protein interactions

Specific interactions between proteins and their binding partners are fundamental to life processes. The ability to detect protein complexes, and map their sites of binding, is crucial to understanding basic biology at the molecular level. Methods that employ sensitive analytical techniques such as mass spectrometry have the potential to provide valuable insights with very little material and on short time scales. Here we present a differential protein footprinting technique employing an efficient photo-activated probe for use with mass spectrometry. Using this methodology the location of a carbohydrate substrate was accurately mapped to the binding cleft of lysozyme, and in a more complex example, the interactions between a 100 kDa, multi-domain deubiquitinating enzyme, USP5 and a diubiquitin substrate were located to different functional domains. The much improved properties of this probe make carbene footprinting a viable method for rapid and accurate identification of protein binding sites utilizing benign, near-UV photoactivation

Measurements of double-helicity asymmetries in inclusive J/ψJ/\psi production in longitudinally polarized p+pp+p collisions at s=510\sqrt{s}=510 GeV

We report the double helicity asymmetry, $A_{LL}^{J/\psi}$, in inclusive $J/\psi$ production at forward rapidity as a function of transverse momentum $p_T$ and rapidity $|y|$. The data analyzed were taken during $\sqrt{s}=510$ GeV longitudinally polarized $p$$+$$p$ collisions at the Relativistic Heavy Ion Collider (RHIC) in the 2013 run using the PHENIX detector. At this collision energy, $J/\psi$ particles are predominantly produced through gluon-gluon scatterings, thus $A_{LL}^{J/\psi}$ is sensitive to the gluon polarization inside the proton. We measured $A_{LL}^{J/\psi}$ by detecting the decay daughter muon pairs $\mu^+ \mu^-$ within the PHENIX muon spectrometers in the rapidity range $1.2<|y|<2.2$. In this kinematic range, we measured the $A_{LL}^{J/\psi}$ to be $0.012 \pm 0.010$~(stat)~$\pm$~$0.003$(syst). The $A_{LL}^{J/\psi}$ can be expressed to be proportional to the product of the gluon polarization distributions at two distinct ranges of Bjorken $x$: one at moderate range $x \approx 0.05$ where recent RHIC data of jet and $\pi^0$ double helicity spin asymmetries have shown evidence for significant gluon polarization, and the other one covering the poorly known small-$x$ region $x \approx 2\times 10^{-3}$. Thus our new results could be used to further constrain the gluon polarization for $x< 0.05$.Comment: 335 authors, 10 pages, 4 figures, 3 tables, 2013 data. Version accepted for publication by Phys. Rev. D. Plain text data tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.htm

Nuclear dependence of the transverse single-spin asymmetry in the production of charged hadrons at forward rapidity in polarized p+pp+p, p+p+Al, and p+p+Au collisions at sNN=200\sqrt{s_{_{NN}}}=200 GeV

We report on the nuclear dependence of transverse single-spin asymmetries (TSSAs) in the production of positively-charged hadrons in polarized $p^{\uparrow}+p$, $p^{\uparrow}+$Al and $p^{\uparrow}+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV. The measurements have been performed at forward rapidity ($1.4<\eta<2.4$) over the range of $1.8<p_{T}<7.0$ GeV$/c$ and $0.1<x_{F}<0.2$. We observed a positive asymmetry $A_{N}$ for positively-charged hadrons in \polpp collisions, and a significantly reduced asymmetry in $p^{\uparrow}$+$A$ collisions. These results reveal a nuclear dependence of charged hadron $A_N$ in a regime where perturbative techniques are relevant. These results provide new opportunities to use \polpA collisions as a tool to investigate the rich phenomena behind TSSAs in hadronic collisions and to use TSSA as a new handle in studying small-system collisions.Comment: 303 authors from 66 institutions, 9 pages, 2 figures, 1 table. v1 is version accepted for publication in Physical Review Letters. Plain text data tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.htm

Measurement of higher cumulants of net-charge multiplicity distributions in Au++Au collisions at sNN=7.7−200\sqrt{s_{_{NN}}}=7.7-200 GeV

We report the measurement of cumulants ($C_n, n=1\ldots4$) of the net-charge distributions measured within pseudorapidity ($|\eta|<0.35$) in Au$+$Au collisions at $\sqrt{s_{_{NN}}}=7.7-200$ GeV with the PHENIX experiment at the Relativistic Heavy Ion Collider. The ratios of cumulants (e.g. $C_1/C_2$, $C_3/C_1$) of the net-charge distributions, which can be related to volume independent susceptibility ratios, are studied as a function of centrality and energy. These quantities are important to understand the quantum-chromodynamics phase diagram and possible existence of a critical end point. The measured values are very well described by expectation from negative binomial distributions. We do not observe any nonmonotonic behavior in the ratios of the cumulants as a function of collision energy. The measured values of $C_1/C_2 = \mu/\sigma^2$ and $C_3/C_1 = S\sigma^3/\mu$ can be directly compared to lattice quantum-chromodynamics calculations and thus allow extraction of both the chemical freeze-out temperature and the baryon chemical potential at each center-of-mass energy.Comment: 512 authors, 8 pages, 4 figures, 1 table. v2 is version accepted for publication in Phys. Rev. C as a Rapid Communication. Plain text data tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.htm