1,584 research outputs found
Modeling reactivity to biological macromolecules with a deep multitask network
Most
small-molecule drug candidates fail before entering the market,
frequently because of unexpected toxicity. Often, toxicity is detected
only late in drug development, because many types of toxicities, especially
idiosyncratic adverse drug reactions (IADRs), are particularly hard
to predict and detect. Moreover, drug-induced liver injury (DILI)
is the most frequent reason drugs are withdrawn from the market and
causes 50% of acute liver failure cases in the United States. A common
mechanism often underlies many types of drug toxicities, including
both DILI and IADRs. Drugs are bioactivated by drug-metabolizing enzymes
into reactive metabolites, which then conjugate to sites in proteins
or DNA to form adducts. DNA adducts are often mutagenic and may alter
the reading and copying of genes and their regulatory elements, causing
gene dysregulation and even triggering cancer. Similarly, protein
adducts can disrupt their normal biological functions and induce harmful
immune responses. Unfortunately, reactive metabolites are not reliably
detected by experiments, and it is also expensive to test drug candidates
for potential to form DNA or protein adducts during the early stages
of drug development. In contrast, computational methods have the potential
to quickly screen for covalent binding potential, thereby flagging
problematic molecules and reducing the total number of necessary experiments.
Here, we train a deep convolution neural networkthe XenoSite
reactivity modelusing literature data to accurately predict
both sites and probability of reactivity for molecules with glutathione,
cyanide, protein, and DNA. On the site level, cross-validated predictions
had area under the curve (AUC) performances of 89.8% for DNA and 94.4%
for protein. Furthermore, the model separated molecules electrophilically
reactive with DNA and protein from nonreactive molecules with cross-validated
AUC performances of 78.7% and 79.8%, respectively. On both the site-
and molecule-level, the model’s performances significantly
outperformed reactivity indices derived from quantum simulations that
are reported in the literature. Moreover, we developed and applied
a selectivity score to assess preferential reactions with the macromolecules
as opposed to the common screening traps. For the entire data set
of 2803 molecules, this approach yielded totals of 257 (9.2%) and
227 (8.1%) molecules predicted to be reactive only with DNA and protein,
respectively, and hence those that would be missed by standard reactivity
screening experiments. Site of reactivity data is an underutilized
resource that can be used to not only predict if molecules are reactive,
but also show where they might be modified to reduce toxicity while
retaining efficacy. The XenoSite reactivity model is available at http://swami.wustl.edu/xenosite/p/reactivity
Activity and Connectivity Differences Underlying Inhibitory Control Across the Adult Life Span.
Inhibitory control requires precise regulation of activity and connectivity within multiple brain networks. Previous studies have typically evaluated age-related changes in regional activity or changes in interregional interactions. Instead, we test the hypothesis that activity and connectivity make distinct, complementary contributions to performance across the life span and the maintenance of successful inhibitory control systems. A representative sample of healthy human adults in a large, population-based life span cohort performed an integrated Stop-Signal (SS)/No-Go task during functional magnetic resonance imaging (n = 119; age range, 18-88 years). Individual differences in inhibitory control were measured in terms of the SS reaction time (SSRT), using the blocked integration method. Linear models and independent components analysis revealed that individual differences in SSRT correlated with both activity and connectivity in a distributed inhibition network, comprising prefrontal, premotor, and motor regions. Importantly, this pattern was moderated by age, such that the association between inhibitory control and connectivity, but not activity, differed with age. Multivariate statistics and out-of-sample validation tests of multifactorial functional organization identified differential roles of activity and connectivity in determining an individual's SSRT across the life span. We propose that age-related differences in adaptive cognitive control are best characterized by the joint consideration of multifocal activity and connectivity within distributed brain networks. These insights may facilitate the development of new strategies to support cognitive ability in old age.SIGNIFICANCE STATEMENT The preservation of cognitive and motor control is crucial for maintaining well being across the life span. We show that such control is determined by both activity and connectivity within distributed brain networks. In a large, population-based cohort, we used a novel whole-brain multivariate approach to estimate the functional components of inhibitory control, in terms of their activity and connectivity. Both activity and connectivity in the inhibition network changed with age. But only the association between performance and connectivity, not activity, differed with age. The results suggest that adaptive control is best characterized by the joint consideration of multifocal activity and connectivity. These insights may facilitate the development of new strategies to maintain cognitive ability across the life span in health and disease
Infrared Luminosities and Dust Properties of z ~ 2 Dust-Obscured Galaxies
We present SHARC-II 350um imaging of twelve 24um-bright (F_24um > 0.8 mJy)
Dust-Obscured Galaxies (DOGs) and CARMA 1mm imaging of a subset of 2 DOGs, all
selected from the Bootes field of the NOAO Deep Wide-Field Survey. Detections
of 4 DOGs at 350um imply IR luminosities which are consistent within a factor
of 2 of expectations based on a warm dust spectral energy distribution (SED)
scaled to the observed 24um flux density. The 350um upper limits for the 8
non-detected DOGs are consistent with both Mrk231 and M82 (warm dust SEDs), but
exclude cold dust (Arp220) SEDs. The two DOGs targeted at 1mm were not detected
in our CARMA observations, placing strong constraints on the dust temperature:
T_dust > 35-60 K. Assuming these dust properties apply to the entire sample, we
find dust masses of ~3x10^8 M_sun. In comparison to other dusty z ~ 2 galaxy
populations such as sub-millimeter galaxies (SMGs) and other Spitzer-selected
high-redshift sources, this sample of DOGs has higher IR luminosities (2x10^13
L_sun vs. 6x10^12 L_sun for the other galaxy populations), warmer dust
temperatures (>35-60 K vs. ~30 K), and lower inferred dust masses (3x10^8 M_sun
vs. 3x10^9 M_sun). Herschel and SCUBA-2 surveys should be able to detect
hundreds of these power-law dominated DOGs. We use HST and Spitzer/IRAC data to
estimate stellar masses of these sources and find that the stellar to gas mass
ratio may be higher in our 24um-bright sample of DOGs than in SMGs and other
Spitzer-selected sources. Although larger sample sizes are needed to provide a
definitive conclusion, the data are consistent with an evolutionary trend in
which the formation of massive galaxies at z~2 involves a sub-millimeter
bright, cold-dust and star-formation dominated phase followed by a 24um-bright,
warm-dust and AGN-dominated phase.Comment: 16 pages, 7 figures, 6 tables; accepted to the Ap
An unmanned aerial vehicle sampling platform for atmospheric water vapor isotopes in polar environments
Above polar ice sheets, atmospheric water vapor exchange occurs across the planetary boundary layer (PBL) and is an important mechanism in a number of processes that affect the surface mass balance of the ice sheets. Yet, this exchange is not well understood and has substantial implications for modeling and remote sensing of the polar hydrologic cycle. Efforts to characterize the exchange face substantial logistical challenges including the remoteness of ice sheet field camps, extreme weather conditions, low humidity and temperature that limit the effectiveness of instruments, and dangers associated with flying manned aircraft at low altitudes. Here, we present an unmanned aerial vehicle (UAV) sampling platform for operation in extreme polar environments that is capable of sampling atmospheric water vapor for subsequent measurement of water isotopes. This system was deployed to the East Greenland Ice-core Project (EastGRIP) camp in northeast Greenland during summer 2019. Four sampling flight missions were completed. With a suite of atmospheric measurements aboard the UAV (temperature, humidity, pressure, GPS) we determine the height of the PBL using online algorithms, allowing for strategic decision-making by the pilot to sample water isotopes above and below the PBL. Water isotope data were measured by a Picarro L2130-i instrument using flasks of atmospheric air collected within the nose cone of the UAV. The internal repeatability for δD and δ18O was 2.8 ‰ and 0.45 ‰, respectively, which we also compared to independent EastGRIP tower-isotope data. Based on these results, we demonstrate the efficacy of this new UAV-isotope platform and present improvements to be utilized in future polar field campaigns. The system is also designed to be readily adaptable to other fields of study, such as measurement of carbon cycle gases or remote sensing of ground conditions.publishedVersio
Experimentally realized in situ backpropagation for deep learning in nanophotonic neural networks
Neural networks are widely deployed models across many scientific disciplines
and commercial endeavors ranging from edge computing and sensing to large-scale
signal processing in data centers. The most efficient and well-entrenched
method to train such networks is backpropagation, or reverse-mode automatic
differentiation. To counter an exponentially increasing energy budget in the
artificial intelligence sector, there has been recent interest in analog
implementations of neural networks, specifically nanophotonic neural networks
for which no analog backpropagation demonstration exists. We design
mass-manufacturable silicon photonic neural networks that alternately cascade
our custom designed "photonic mesh" accelerator with digitally implemented
nonlinearities. These reconfigurable photonic meshes program computationally
intensive arbitrary matrix multiplication by setting physical voltages that
tune the interference of optically encoded input data propagating through
integrated Mach-Zehnder interferometer networks. Here, using our packaged
photonic chip, we demonstrate in situ backpropagation for the first time to
solve classification tasks and evaluate a new protocol to keep the entire
gradient measurement and update of physical device voltages in the analog
domain, improving on past theoretical proposals. Our method is made possible by
introducing three changes to typical photonic meshes: (1) measurements at
optical "grating tap" monitors, (2) bidirectional optical signal propagation
automated by fiber switch, and (3) universal generation and readout of optical
amplitude and phase. After training, our classification achieves accuracies
similar to digital equivalents even in presence of systematic error. Our
findings suggest a new training paradigm for photonics-accelerated artificial
intelligence based entirely on a physical analog of the popular backpropagation
technique.Comment: 23 pages, 10 figure
Enhancer Sequence Variants and Transcription-Factor Deregulation Synergize to Construct Pathogenic Regulatory Circuits in B-Cell Lymphoma
SummaryMost B-cell lymphomas arise in the germinal center (GC), where humoral immune responses evolve from potentially oncogenic cycles of mutation, proliferation, and clonal selection. Although lymphoma gene expression diverges significantly from GC B cells, underlying mechanisms that alter the activities of corresponding regulatory elements (REs) remain elusive. Here we define the complete pathogenic circuitry of human follicular lymphoma (FL), which activates or decommissions REs from normal GC B cells and commandeers enhancers from other lineages. Moreover, independent sets of transcription factors, whose expression was deregulated in FL, targeted commandeered versus decommissioned REs. Our approach revealed two distinct subtypes of low-grade FL, whose pathogenic circuitries resembled GC B or activated B cells. FL-altered enhancers also were enriched for sequence variants, including somatic mutations, which disrupt transcription-factor binding and expression of circuit-linked genes. Thus, the pathogenic regulatory circuitry of FL reveals distinct genetic and epigenetic etiologies for GC B-cell transformation
Measurement of Cosmic-ray Electrons at TeV Energies by VERITAS
Cosmic-ray electrons and positrons (CREs) at GeV-TeV energies are a unique
probe of our local Galactic neighborhood. CREs lose energy rapidly via
synchrotron radiation and inverse-Compton scattering processes while
propagating within the Galaxy and these losses limit their propagation
distance. For electrons with TeV energies, the limit is on the order of a
kiloparsec. Within that distance there are only a few known astrophysical
objects capable of accelerating electrons to such high energies. It is also
possible that the CREs are the products of the annihilation or decay of heavy
dark matter (DM) particles. VERITAS, an array of imaging air Cherenkov
telescopes in southern Arizona, USA, is primarily utilized for gamma-ray
astronomy, but also simultaneously collects CREs during all observations. We
describe our methods of identifying CREs in VERITAS data and present an energy
spectrum, extending from 300 GeV to 5 TeV, obtained from approximately 300
hours of observations. A single power-law fit is ruled out in VERITAS data. We
find that the spectrum of CREs is consistent with a broken power law, with a
break energy at 710 40 140 GeV.Comment: 17 pages, 2 figures, accepted for publication in PR
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