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Abstract Environments with aquatic vegetation can mitigate excess nitrogen (N) loads to downstream waters. However, complex interactions between multiple hydro‐biogeochemical processes control N removal within these environments and thus complicate implementation of aquatic vegetation as a management solution. Here, we conducted controlled experiments using a canopy of artificial rigid emergent vegetation in a recirculating flume mesocosm to quantify differences in rates of mass transport and nitrate (NO₃⁻N) removal between the open channel‐canopy interface across a range in nominal water velocities. We found NO₃⁻N removal rates were 86% greater with the canopy present compared to no canopy control experiments and were always greatest at intermediate velocity (6 cms⁻¹). With the canopy present, a hydrodynamically distinct mixing layer formed at the open channel‐canopy interface, and resources, such as carbon (C), CN ratios, and dissolved oxygen, differed between open channel and vegetated canopy. The dimensionless Damköhler (Da) number indicated NO₃⁻N removal rates were reaction limited (Da << 1) for all canopy experiments, yet across all velocities NO₃⁻N removal was more reaction limited in the open channel than the canopy due to higher rates of mixing and less contact time with reactive surfaces. We found significant relationships between NO₃⁻N removal rates and Da with hydrodynamic metrics (mixing zone width and Reynolds number, respectively), suggesting that NO₃⁻N removal in the presence of rigid vegetation can be enhanced by manipulating flow conditions. These findings demonstrate that rigid emergent vegetation‐open channel interfaces create conditions conducive for NO₃⁻N removal and with effective management can improve overall water quality. 

We present a new set of tools to derive systemic velocities for single-mode RR Lyrae stars from visual and near-infrared spectra. We derived scaling relations and line-of-sight velocity templates using both APOGEE andGaiaspectroscopic products combined with photometricG-band amplitudes. We provide a means to estimate systemic velocities for the RR Lyrae subclasses, RRab and RRc. Our analysis indicates that the scaling relation between the photometric and line-of-sight velocity amplitudes is nonlinear, with a break in a linear relation occurring around ≈0.4 mag in both theV-band andG-band amplitudes. We did not observe such a break in the relation for the first-overtone pulsators. Using stellar pulsation models, we further confirm and examine the nonlinearity in scaling relation for the RRab subclass. We observed little to no variation with stellar parameters (mass, metallicity, and luminosity) in the scaling relation between the photometric and line-of-sight velocity amplitudes for fundamental-mode pulsators. We observed an offset in the scaling relation between the observations and stellar pulsation models, mainly in the low-amplitude RR Lyrae regime. This offset disappears when different sets of convective parameters are used. Thus, the Fourier amplitudes obtained from the photometry and line-of-sight velocity measurements can be utilized to constrain convective parameters of stellar pulsation models. The scaling relations and templates for APOGEE andGaiadata accurately predict systemic velocities compared to literature values. In addition, our tools derived from theGaiaspectra improve the precision of the derived systemic velocities by approximately 50 percent and provide a better description of the uncertainty distribution in comparison with previous studies. Our newly derived tools will be used for RR Lyrae variables observed toward the Galactic bulge. 

We present a new set of period–absolute magnitude–metallicity (PMZ) relations for single-mode RR Lyrae stars calibrated for the opticalG_BP,V,G,G_RP, near-infraredI,J,H, andK_spassbands. We compiled a large dataset (over 100 objects) of fundamental and first-overtone RR Lyrae pulsators consisting of mean intensity magnitudes, reddenings, pulsation properties, iron abundances, and parallaxes measured by theGaiaastrometric satellite in its third data release. Our newly calibrated PMZ relations encapsulate the most up-to-date ingredients in terms of both data and methodology. They are intended to be used in conjunction with large photometric surveys targeting the Galactic bulge, including the Optical Gravitational Lensing Experiment (OGLE), the Vista Variables in the Vía Láctea Survey (VVV), and theGaiacatalog. In addition, our Bayesian probabilistic approach provides accurate uncertainty estimates of the predicted absolute magnitudes of individual RR Lyrae stars. Our derived PMZ relations provide consistent results when compared to benchmark distances to globular clusters NGC 6121 (also known as M 4), NGC 5139 (also known as omega Cen), and Large and Small Magellanic Clouds, which are stellar systems rich in RR Lyrae stars. Lastly, ourK_s-band PMZ relations match well with the previously published PMZ relations based onGaiadata and accurately predict the distance toward the prototype of this class of variables, the eponymic RR Lyr itself. 

The analysis of nuclear magnetic resonance (NMR) spectra for the comprehensive and unambiguous identification and characterization of peaks is a difficult, but critically important step in all NMR analyses of complex biological molecular systems. Here, we introduce DEEP Picker, a deep neural network (DNN)-based approach for peak picking and spectral deconvolution which semi-automates the analysis of two-dimensional NMR spectra. DEEP Picker includes 8 hidden convolutional layers and was trained on a large number of synthetic spectra of known composition with variable degrees of crowdedness. We show that our method is able to correctly identify overlapping peaks, including ones that are challenging for expert spectroscopists and existing computational methods alike. We demonstrate the utility of DEEP Picker on NMR spectra of folded and intrinsically disordered proteins as well as a complex metabolomics mixture, and show how it provides access to valuable NMR information. DEEP Picker should facilitate the semi-automation and standardization of protocols for better consistency and sharing of results within the scientific community. 

We present a chemo-dynamical study of the Orphan stellar stream using a catalog of RR Lyrae pulsating variable stars for which photometric, astrometric, and spectroscopic data are available. Employing low-resolution spectra from the Sloan Digital Sky Survey (SDSS), we determined line-of-sight velocities for individual exposures and derived the systemic velocities of the RR Lyrae stars. In combination with the stars’ spectroscopic metallicities and Gaia EDR3 astrometry, we investigated the northern part of the Orphan stream. In our probabilistic approach, we found 20 single mode RR Lyrae variables likely associated with the Orphan stream based on their positions, proper motions, and distances. The acquired sample permitted us to expand our search to nonvariable stars in the SDSS dataset, utilizing line-of-sight velocities determined by the SDSS. We found 54 additional nonvariable stars linked to the Orphan stream. The metallicity distribution for the identified red giant branch stars and blue horizontal branch stars is, on average, −2.13 ± 0.05 dex and −1.87 ± 0.14 dex, with dispersions of 0.23 and 0.43 dex, respectively. The metallicity distribution of the RR Lyrae variables peaks at −1.80 ± 0.06 dex and a dispersion of 0.25 dex. Using the collected stellar sample, we investigated a possible link between the ultra-faint dwarf galaxy Grus II and the Orphan stream. Based on their kinematics, we found that both the stream RR Lyrae and Grus II are on a prograde orbit with similar orbital properties, although the large uncertainties on the dynamical properties render an unambiguous claim of connection difficult. At the same time, the chemical analysis strongly weakens the connection between both. We argue that Grus II in combination with the Orphan stream would have to exhibit a strong inverse metallicity gradient, which to date has not been detected in any Local Group system.