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 Moreover, drug-induced liver injury (DILI) is the most frequent reason drugs are withdrawn from the market and causes 50% of acute liver failure cases in the United States.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 networkthe XenoSite reactivity modelusing 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 $\pm$ 40$_{stat}$ $\pm$ 140$_{syst}$ GeV.Comment: 17 pages, 2 figures, accepted for publication in PR