517 research outputs found
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 production in longitudinally polarized collisions at GeV
We report the double helicity asymmetry, , in inclusive
production at forward rapidity as a function of transverse momentum
and rapidity . The data analyzed were taken during
GeV longitudinally polarized collisions at the Relativistic Heavy Ion
Collider (RHIC) in the 2013 run using the PHENIX detector. At this collision
energy, particles are predominantly produced through gluon-gluon
scatterings, thus is sensitive to the gluon polarization
inside the proton. We measured by detecting the decay
daughter muon pairs within the PHENIX muon spectrometers in the
rapidity range . In this kinematic range, we measured the
to be ~(stat)~~(syst). The
can be expressed to be proportional to the product of the
gluon polarization distributions at two distinct ranges of Bjorken : one at
moderate range where recent RHIC data of jet and
double helicity spin asymmetries have shown evidence for significant gluon
polarization, and the other one covering the poorly known small- region . Thus our new results could be used to further
constrain the gluon polarization for .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 , Al, and Au collisions at GeV
We report on the nuclear dependence of transverse single-spin asymmetries
(TSSAs) in the production of positively-charged hadrons in polarized
, Al and Au collisions at
GeV. The measurements have been performed at forward
rapidity () over the range of GeV and
. We observed a positive asymmetry for
positively-charged hadrons in \polpp collisions, and a significantly reduced
asymmetry in + collisions. These results reveal a nuclear
dependence of charged hadron 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 AuAu collisions at GeV
We report the measurement of cumulants () of the net-charge
distributions measured within pseudorapidity () in AuAu
collisions at GeV with the PHENIX experiment at the
Relativistic Heavy Ion Collider. The ratios of cumulants (e.g. ,
) 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 and 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
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