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(2023) Chou, Renee Ti; Ouattara, Amed; Takala-Harrison, Shannon; Cummings, Michael P.
Intensive malaria control and elimination efforts have led to substantial reductions in malaria incidence over the past two decades. However, the reduction in Plasmodium falciparum malaria cases has led to a species shift in some geographic areas, with P. vivax predominating in many areas outside of Africa. Despite its wide geographic distribution, P. vivax vaccine development has lagged far behind that for P. falciparum, in part due to the inability to cultivate P. vivax in vitro, hindering traditional approaches for antigen identification. In a prior study, we have used a positive-unlabeled random forest (PURF) machine learning approach to identify P. falciparum antigens for consideration in vaccine development efforts. Here we integrate systems data from P. falciparum (the better-studied species) to improve PURF models to predict potential P. vivax vaccine antigen candidates. We further show that inclusion of known antigens from the other species is critical for model performance, but the inclusion of unlabeled proteins the other species can result in misdirection of the model toward predictors of species classification, rather than antigen identification. Beyond malaria, incorporating antigens from a closely related species may aid in vaccine development for emerging pathogens having few or no known antigens.
Here we use a box model to evaluate how much additional data from Δ12CH2D2 and Δ13CH3D may add to understanding the temporal trend in atmospheric methane, and specifically, whether they may differentiate the contributions of fossil fuel and microbial sources. EDGAR (Emissions Database for Global Atmospheric Research) provides high-quality constraints on methane fluxes from major anthropogenic sources, and different versions of EDGAR reflect uncertainty in understanding of the apportionment of these fluxes over the past few decades. We used two versions of EDGAR and also considered another model of fossil fuel flux to build four different scenarios for anthropogenic source fluxes for our box model. EDGAR does not include wetland emissions and those are calculated (a free variable) to close the flux balance needed by the model. Each scenario broadly follows one of four parameterizations of anthropogenic source fluxes to obtain an estimate of the composition and evolution of Δ12CH2D2 and Δ13CH3D through time.
(2023-09-07) Karim, Ramsey; Pound, Marc W.; Wolfire, Mark G.; Mundy, Lee; Tielens, Alexander G. G. M.
We present here the original observations used in the manuscript "SOFIA FEEDBACK Survey: The Pillars of Creation in [C II] and Molecular Lines" (Karim et al., Astronomical Journal, 2023). The data consist of FITS format images and datacubes of the rotational transitions of molecular lines CO, 13CO, C18O, CS, HCN, HCO+, and N2H+ in the 3mm spectral window, and the atomic spectral lines [C II] 158 micron and [O I] 63 micron.
We also present a snapshot copy of the scoby (Spectra from Catalogs of OB Stars) software repository, some model data necessary for it, and some examples of how to run it.
(2023) Tiwari, Maitraiyee; Kievit, Rens; Kabanovic, Slawa; Bonne, Lars; Falasca, F.; Guevara, Cristian; Higgins, Ronan; Justen, M.; Karim, Ramsey; Pabst, Cornelia; Pound, Marc W.; Schneider, Nicola; Simon, R.; Stutzki, Jurgen; Wolfire, Mark; Tielens, Alexander G. G. M.
We present a python software repository implementing the PyGMMis (Melchior & Goudling 2018) method to astronomical data cubes of velocity resolved line observations. This implementation is described extensively in Tiwari et al. 2023, ApJ. An example is included in /example/ containing the SOFIA data of RCW120 used in Tiwari et al. 2023, ApJ, along with example scripts describing the full implementation of our code. The majority of parameter tweaking can be performed within 'rcw120-params.txt' which is continuously called during the procedure. A full description of the code and how to use it is in README.md (markdown file).