Optical propagation measurements at Emerson Lake, 1968

Optical propagation measurements in inhomogeneous atmosphere at Emerson Lake, California for optical propagation theory validity testin

Tundrenone: An Atypical Secondary Metabolite from Bacteria with Highly Restricted Primary Metabolism

Methane-oxidizing bacteria, aerobes that utilize methane as their sole carbon and energy source, are being increasingly studied for their environmentally significant ability to remove methane from the atmosphere. Their genomes indicate that they also have a robust and unusual secondary metabolism. Bioinformatic analysis of the Methylobacter tundripaludum genome identified biosynthetic gene clusters for several intriguing metabolites, and this report discloses the structural and genetic characterization of tundrenone, one of these metabolites. Tundrenone is a highly oxidized metabolite that incorporates both a modified bicyclic chorismate-derived fragment and a modified lipid tail bearing a β,γ-unsaturated α-hydroxy ketone. Tundrenone has been genetically linked to its biosynthetic gene cluster, and quorum sensing activates its production. M. tundripaludum’s genome and tundrenone’s discovery support the idea that additional studies of methane-oxidizing bacteria will reveal new naturally occurring molecular scaffolds and the biosynthetic pathways that produce them

Canvass: a crowd-sourced, natural-product screening library for exploring biological space

NCATS thanks Dingyin Tao for assistance with compound characterization. This research was supported by the Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health (NIH). R.B.A. acknowledges support from NSF (CHE-1665145) and NIH (GM126221). M.K.B. acknowledges support from NIH (5R01GM110131). N.Z.B. thanks support from NIGMS, NIH (R01GM114061). J.K.C. acknowledges support from NSF (CHE-1665331). J.C. acknowledges support from the Fogarty International Center, NIH (TW009872). P.A.C. acknowledges support from the National Cancer Institute (NCI), NIH (R01 CA158275), and the NIH/National Institute of Aging (P01 AG012411). N.K.G. acknowledges support from NSF (CHE-1464898). B.C.G. thanks the support of NSF (RUI: 213569), the Camille and Henry Dreyfus Foundation, and the Arnold and Mabel Beckman Foundation. C.C.H. thanks the start-up funds from the Scripps Institution of Oceanography for support. J.N.J. acknowledges support from NIH (GM 063557, GM 084333). A.D.K. thanks the support from NCI, NIH (P01CA125066). D.G.I.K. acknowledges support from the National Center for Complementary and Integrative Health (1 R01 AT008088) and the Fogarty International Center, NIH (U01 TW00313), and gratefully acknowledges courtesies extended by the Government of Madagascar (Ministere des Eaux et Forets). O.K. thanks NIH (R01GM071779) for financial support. T.J.M. acknowledges support from NIH (GM116952). S.M. acknowledges support from NIH (DA045884-01, DA046487-01, AA026949-01), the Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Medical Research Program (W81XWH-17-1-0256), and NCI, NIH, through a Cancer Center Support Grant (P30 CA008748). K.N.M. thanks the California Department of Food and Agriculture Pierce's Disease and Glassy Winged Sharpshooter Board for support. B.T.M. thanks Michael Mullowney for his contribution in the isolation, elucidation, and submission of the compounds in this work. P.N. acknowledges support from NIH (R01 GM111476). L.E.O. acknowledges support from NIH (R01-HL25854, R01-GM30859, R0-1-NS-12389). L.E.B., J.K.S., and J.A.P. thank the NIH (R35 GM-118173, R24 GM-111625) for research support. F.R. thanks the American Lebanese Syrian Associated Charities (ALSAC) for financial support. I.S. thanks the University of Oklahoma Startup funds for support. J.T.S. acknowledges support from ACS PRF (53767-ND1) and NSF (CHE-1414298), and thanks Drs. Kellan N. Lamb and Michael J. Di Maso for their synthetic contribution. B.S. acknowledges support from NIH (CA78747, CA106150, GM114353, GM115575). W.S. acknowledges support from NIGMS, NIH (R15GM116032, P30 GM103450), and thanks the University of Arkansas for startup funds and the Arkansas Biosciences Institute (ABI) for seed money. C.R.J.S. acknowledges support from NIH (R01GM121656). D.S.T. thanks the support of NIH (T32 CA062948-Gudas) and PhRMA Foundation to A.L.V., NIH (P41 GM076267) to D.S.T., and CCSG NIH (P30 CA008748) to C.B. Thompson. R.E.T. acknowledges support from NIGMS, NIH (GM129465). R.J.T. thanks the American Cancer Society (RSG-12-253-01-CDD) and NSF (CHE1361173) for support. D.A.V. thanks the Camille and Henry Dreyfus Foundation, the National Science Foundation (CHE-0353662, CHE-1005253, and CHE-1725142), the Beckman Foundation, the Sherman Fairchild Foundation, the John Stauffer Charitable Trust, and the Christian Scholars Foundation for support. J.W. acknowledges support from the American Cancer Society through the Research Scholar Grant (RSG-13-011-01-CDD). W.M.W.acknowledges support from NIGMS, NIH (GM119426), and NSF (CHE1755698). A.Z. acknowledges support from NSF (CHE-1463819). (Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health (NIH); CHE-1665145 - NSF; CHE-1665331 - NSF; CHE-1464898 - NSF; RUI: 213569 - NSF; CHE-1414298 - NSF; CHE1361173 - NSF; CHE1755698 - NSF; CHE-1463819 - NSF; GM126221 - NIH; 5R01GM110131 - NIH; GM 063557 - NIH; GM 084333 - NIH; R01GM071779 - NIH; GM116952 - NIH; DA045884-01 - NIH; DA046487-01 - NIH; AA026949-01 - NIH; R01 GM111476 - NIH; R01-HL25854 - NIH; R01-GM30859 - NIH; R0-1-NS-12389 - NIH; R35 GM-118173 - NIH; R24 GM-111625 - NIH; CA78747 - NIH; CA106150 - NIH; GM114353 - NIH; GM115575 - NIH; R01GM121656 - NIH; T32 CA062948-Gudas - NIH; P41 GM076267 - NIH; R01GM114061 - NIGMS, NIH; R15GM116032 - NIGMS, NIH; P30 GM103450 - NIGMS, NIH; GM129465 - NIGMS, NIH; GM119426 - NIGMS, NIH; TW009872 - Fogarty International Center, NIH; U01 TW00313 - Fogarty International Center, NIH; R01 CA158275 - National Cancer Institute (NCI), NIH; P01 AG012411 - NIH/National Institute of Aging; Camille and Henry Dreyfus Foundation; Arnold and Mabel Beckman Foundation; Scripps Institution of Oceanography; P01CA125066 - NCI, NIH; 1 R01 AT008088 - National Center for Complementary and Integrative Health; W81XWH-17-1-0256 - Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Medical Research Program; P30 CA008748 - NCI, NIH, through a Cancer Center Support Grant; California Department of Food and Agriculture Pierce's Disease and Glassy Winged Sharpshooter Board; American Lebanese Syrian Associated Charities (ALSAC); University of Oklahoma Startup funds; 53767-ND1 - ACS PRF; PhRMA Foundation; P30 CA008748 - CCSG NIH; RSG-12-253-01-CDD - American Cancer Society; RSG-13-011-01-CDD - American Cancer Society; CHE-0353662 - National Science Foundation; CHE-1005253 - National Science Foundation; CHE-1725142 - National Science Foundation; Beckman Foundation; Sherman Fairchild Foundation; John Stauffer Charitable Trust; Christian Scholars Foundation)Published versionSupporting documentatio

Tutorial : applying machine learning in behavioral research

Machine-learning algorithms hold promise for revolutionizing how educators and clinicians make decisions. However, researchers in behavior analysis have been slow to adopt this methodology to further develop their understanding of human behavior and improve the application of the science to problems of applied significance. One potential explanation for the scarcity of research is that machine learning is not typically taught as part of training programs in behavior analysis. This tutorial aims to address this barrier by promoting increased research using machine learning in behavior analysis. We present how to apply the random forest, support vector machine, stochastic gradient descent, and k-nearest neighbors algorithms on a small dataset to better identify parents of children with autism who would benefit from a behavior analytic interactive web training. These step-by-step applications should allow researchers to implement machine-learning algorithms with novel research questions and datasets

Marine Cyanobacteria Compounds with Anticancer Properties: Implication of Apoptosis

Marine cyanobacteria have been proved to be an important source of potential anticancer drugs. Although several compounds were found to be cytotoxic to cancer cells in culture, the pathways by which cells are affected are still poorly elucidated. For some compounds, cancer cell death was attributed to an implication of apoptosis through morphological apoptotic features, implication of caspases and proteins of the Bcl-2 family, and other mechanisms such as interference with microtubules dynamics, cell cycle arrest and inhibition of proteases other than caspases

A high-throughput, whole cell assay to identify compounds active against carbapenem-resistant Klebsiella pneumoniae.

Enteric Gram-negative rods (GNR), which are frequent causes of community-acquired and nosocomial infections, are increasingly resistant to the antibiotics in our current armamentarium. One solution to this medical dilemma is the development of novel classes of antimicrobial compounds. Here we report the development of a robust, whole cell-based, high-throughput metabolic assay that detects compounds with activity against carbapenem-resistant Klebsiella pneumoniae. We have used this assay to screen approximately 8,000 fungal extracts and 50,000 synthetic compounds with the goal of identifying extracts and compounds active against a highly resistant strain of Klebsiella pneumoniae. The primary screen identified 43 active fungal extracts and 144 active synthetic compounds. Patulin, a known fungal metabolite and inhibitor of bacterial quorum sensing and alanine racemase, was identified as the active component in the most potent fungal extracts. We did not study patulin further due to previously published evidence of toxicity. Three synthetic compounds termed O06, C17, and N08 were chosen for further study. Compound O06 did not have significant antibacterial activity but rather interfered with sugar metabolism, while compound C17 had only moderate activity against GNRs. Compound N08 was active against several resistant GNRs and showed minimal toxicity to mammalian cells. Preliminary studies suggested that it interferes with protein expression. However, its direct application may be limited by susceptibility to efflux and a tendency to form aggregates in aqueous media. Rapid screening of 58,000 test samples with identification of several compounds that act on CR-K. pneumoniae demonstrates the utility of this screen for the discovery of drugs active against this highly resistant GNR

The impact of stimulus preference, order-effects, and treatment component omission in evaluating treatment integrity

Prior research on treatment integrity has focused either on the lack of measurement of the independent variable or on methods to increase overall levels of treatment integrity. Little research has focused on the effectiveness of common interventions when implemented with less than perfect integrity. The current investigation evaluated the effectiveness of using differential reinforcement of alternative behavior (DRA) and prompting to increase math completion for 36 early elementary students. Treatment was evaluated when both components were implemented, when only reinforcement was implemented, when only prompting was implemented, and when neither was implemented. In addition, preferences for either attention or escape and order-effects of conditions were evaluated. Results indicated treatment was effective at all levels of implementation compared to baseline. However, when preferences for escape and attention were evaluated, analysis revealed individuals who preferred escape responded best when both treatment components were implemented, whereas for individuals who preferred attention, all treatment conditions were equally effective. In addition, results evaluating order effects indicated that exposure to either prompting or reinforcement prior to baseline significantly increased math completion as well as exposure to reinforcement in the first condition