Data science and informatics tools are developing at a blistering rate, but their users often lack the educational background or resources to efficiently apply the methods to their research. Training resources and vignettes that accompany these tools often deprecate because their maintenance is not prioritized by funding, giving teams little time to devote to such endeavors. Our group has developed Open-source Tools for Training Resources (OTTR) to offer greater efficiency and flexibility for creating and maintaining these training resources. OTTR empowers creators to customize their work and allows for a simple workflow to publish using multiple platforms. OTTR allows content creators to publish training material to multiple massive online learner communities using familiar rendering mechanics. OTTR allows the incorporation of pedagogical practices like formative and summative assessments in the form of multiple choice questions and fill in the blank problems that are automatically graded. No local installation of any software is required to begin creating content with OTTR. Thus far, 15 training courses have been created with OTTR repository template. By using the OTTR system, the maintenance workload for updating these courses across platforms has been drastically reduced. For more information about OTTR and how to get started, go to ottrproject.org.

The formation of lateral microdomains is emerging as a central organizing principle in bacterial membranes. These microdomains are targets of antibiotic development and have the potential to enhance natural product synthesis, but the rules governing their assembly are unclear. Previous studies have suggested that microdomain formation is promoted by lipid phase separation, particularly by cardiolipin (CL) and isoprenoid lipids, and there is strong evidence that CL biosynthesis is required for recruitment of membrane proteins to cell poles and division sites. New work demonstrates that additional bacterial lipids may mediate membrane protein localization and function, opening the field for mechanistic evaluation of lipid-driven membrane organization in vivo.

The biogeochemical cycling of nitrogen (N) plays a critical role in supporting marine ecosystems and controlling primary production. Nitrification, the oxidation of ammonia (NH3) by microorganisms, is an important process in the marine N cycle, supplying nitrate (NO3-, the primary source of N that fuels new phytoplankton growth, and the primary substrate for the microbial process of denitrification. Understanding nitrification in the Chukchi Sea, the shallow sea overlying the continental shelf north of Alaska and the Bering Strait, is particularly important as phytoplankton growth there has been shown to be limited by N. However, the controls on nitrification in the water column and potential effects of climate change remain unknown. This study seeks to characterize the controls on nitrification in the Chukchi Sea. We found light to be a strong control on nitrification rates. Nitrification was undetectable at light levels above 23 mu mol photons m(-2) s(-1). Subsequently, sea ice concentration was related to nitrification, with rates being higher at stations with high ice cover where light transmission to the water column was reduced. High ammonium (NH4+ concentrations also enhanced nitrification, suggesting that nitrifying organisms were substrate-limited, likely due to competition for NH4+ from phytoplankton. Unlike previous experimental studies, we found that nitrification rates were higher under low pH conditions. As the effects of ocean acidification and warming disproportionately impact the Arctic, nitrification rates will undoubtedly be affected. Our results will help guide future studies on potential implications of climate change on the biogeochemistry of N in the Chukchi Sea.

The K-type star TOI-2525 has an estimated mass of M = 0.849(-0.033)(+0.024) M-circle dot and radius of R = 0.785(-0.007)(+0.007) R-circle dot observed by the TESS mission in 22 sectors (within sectors 1 and 39). The TESS light curves yield significant transit events of two companions, which show strong transit timing variations (TTVs) with a semiamplitude of similar to 6 hr. We performed TTV dynamical and photodynamical light-curve analysis of the TESS data combined with radial velocity measurements from FEROS and PFS, and we confirmed the planetary nature of these companions. The TOI-2525 system consists of a transiting pair of planets comparable to Neptune and Jupiter with estimated dynamical masses of m(b) = 0.088(-0.004)(+0.005) and m(c) = 0.709(-0.033)(+0.034) M-Jup, radii of r(b) = 0.88(-0.02)(+0.02) and r(c) = 0.98(-0.02)(+0.02) R-Jup, and orbital periods of P-b = 23.288(-0.002)(+0.001) and P-c = 49.260(-0.001)(+0.001) days for the inner and outer planet, respectively. The period ratio is close to the 2:1 period commensurability, but the dynamical simulations of the system suggest that it is outside the mean-motion resonance (MMR) dynamical configuration. Object TOI-2525 b is among the lowest-density Neptune-mass planets known to date, with an estimated median density of rho(b) = 0.174(-0.015)(+0.016) g cm(-3). The TOI-2525 system is very similar to the other K dwarf systems discovered by TESS, TOI-2202 and TOI-216, which are composed of almost identical K dwarf primaries and two warm giant planets near the 2:1 MMR.

We present the largest and most homogeneous collection of near-infrared (NIR) spectra of Type Ia supernovae (SNe Ia): 339 spectra of 98 individual SNe obtained as part of the Carnegie Supernova Project-II. These spectra, obtained with the FIRE spectrograph on the 6.5 m Magellan Baade telescope, have a spectral range of 0.8-2.5 mu m. Using this sample, we explore the NIR spectral diversity of SNe Ia and construct a template of spectral time series as a function of the light-curve-shape parameter, color stretch s ( BV ). Principal component analysis is applied to characterize the diversity of the spectral features and reduce data dimensionality to a smaller subspace. Gaussian process regression is then used to model the subspace dependence on phase and light-curve shape and the associated uncertainty. Our template is able to predict spectral variations that are correlated with s ( BV ), such as the hallmark NIR features: Mg ii at early times and the H-band break after peak. Using this template reduces the systematic uncertainties in K-corrections by similar to 90% compared to those from the Hsiao template. These uncertainties, defined as the mean K-correction differences computed with the color-matched template and observed spectra, are on the level of 4 x 10(-4) mag on average. This template can serve as the baseline spectral energy distribution for light-curve fitters and can identify peculiar spectral features that might point to compelling physics. The results presented here will substantially improve future SN Ia cosmological experiments, for both nearby and distant samples.

SN 2018aoz is a Type Ia SN with a B-band plateau and excess emission in infant-phase light curves ?1 day after the first light, evidencing an over-density of surface iron-peak elements as shown in our previous study. Here, we advance the constraints on the nature and origin of SN 2018aoz based on its evolution until the nebular phase. Near-peak spectroscopic features show that the SN is intermediate between two subtypes of normal Type Ia: core normal and broad line. The excess emission may be attributable to the radioactive decay of surface iron-peak elements as well as the interaction of ejecta with either the binary companion or a small torus of circumstellar material. Nebular-phase limits on Ha and He i favor a white dwarf companion, consistent with the small companion size constrained by the low early SN luminosity, while the absence of [O i] and He i disfavors a violent merger of the progenitor. Of the two main explosion mechanisms proposed to explain the distribution of surface iron-peak elements in SN 2018aoz, the asymmetric Chandrasekhar-mass explosion is less consistent with the progenitor constraints and the observed blueshifts of nebular-phase [Fe ii] and [Ni ii]. The helium-shell double-detonation explosion is compatible with the observed lack of C spectral features, but current 1D models are incompatible with the infant-phase excess emission, Bmax-Vmax

Using data from the Complete Nearby (redshift z (host) < 0.02) sample of Type Ia Supernovae (CNIa0.02), we find a linear relation between two parameters derived from the B - V color curves of Type Ia supernovae: the color stretch s ( BV ) and the rising color slope s(0)*(B-V) BV . The s ( BV ) parameter is known to be tightly correlated with the peak luminosity, especially for fast decliners (dim Type Ia supernovae), and the luminosity correlation with s ( BV ) is markedly better than with the classic light-curve width parameters such as ?m (15)(B). Thus, our new linear relation can be used to infer peak luminosity from s(0)* s ( BV ) (or ?m (15)(B)), the measurement of s(0)*(B-V)

In this work, we present a new catalogue of >40 000 ionised nebulae distributed across the 19 galaxies observed by the PHANGS-MUSE survey. The nebulae have been classified using a new model-comparison-based algorithm that exploits the odds ratio principle to assign a probabilistic classification to each nebula in the sample. The resulting catalogue is the largest catalogue containing complete spectral and spatial information for a variety of ionised nebulae available so far in the literature. We developed this new algorithm to address some of the main limitations of the traditional classification criteria, such as their binarity, the sharpness of the involved limits, and the limited amount of data they rely on for the classification. The analysis of the catalogue shows that the algorithm performs well when selecting H II regions. In fact, we can recover their luminosity function, and its properties are in line with what is available in the literature. We also identify a rather significant population of shock-ionised regions (mostly composed of supernova remnants), which is an order of magnitude larger than any other homogeneous catalogue of supernova remnants currently available in the literature. The number of supernova remnants we identify per galaxy is in line with results in our Galaxy and in other very nearby sources. However, limitations in the source detection algorithm result in an incomplete sample of planetary nebulae, even though their classification seems robust. Finally, we demonstrate how applying a correction for the contribution of the diffuse ionised gas to the nebulae's spectra is essential to obtain a robust classification of the objects and how a correct measurement of the extinction using diffuse-ionised-gas-corrected line fluxes prompts the use of a higher theoretical H alpha/H beta ratio (3.03) than what is commonly used when recovering the E(B - V) via the Balmer decrement technique in massive star-forming galaxies.

The locations of minerals and mineral-forming environments, despite being of great scientific importance and economic interest, are often difficult to predict due to the complex nature of natural systems. In this work, we embrace the complexity and inherent "messiness" of our planet's intertwined geological, chemical, and biological systems by employing machine learning to characterize patterns embedded in the multidimensionality of mineral occurrence and associations. These patterns are a product of, and therefore offer insight into, the Earth's dynamic evolutionary history. Mineral association analysis quantifies high-dimensional multicorrelations in mineral localities across the globe, enabling the identification of previously unknown mineral occurrences, as well as mineral assemblages and their associated paragenetic modes. In this study, we have predicted (i) the previously unknown mineral inventory of the Mars analogue site, Tecopa Basin, (ii) new locations of uranium minerals, particularly those important to understanding the oxidation-hydration history of uraninite, (iii) new deposits of critical minerals, specifically rare earth element (REE)- and Li-bearing phases, and (iv) changes in mineralization and mineral associations through deep time, including a discussion of possible biases in mineralogical data and sampling; furthermore, we have (v) tested and confirmed several of these mineral occurrence predictions in nature, thereby providing ground truth of the predictive method. Mineral association analysis is a predictive method that will enhance our understanding of mineralization and mineralizing environments on Earth, across our solar system, and through deep time.