While the normal microbiota has been implicated as a critical defense against invading pathogens, the impact of enteropathogenic infection and host inflammation on intestinal microbial communities ...has not been elucidated. Using mouse models of Citrobacter rodentium, which closely mimics human diarrheal pathogens inducing host intestinal inflammation, and Campylobacter jejuni infection, as well as chemically and genetically induced models of intestinal inflammation, we demonstrate that host-mediated inflammation in response to an infecting agent, a chemical trigger, or genetic predisposition markedly alters the colonic microbial community. While eliminating a subset of indigenous microbiota, host-mediated inflammation supported the growth of either the resident or introduced aerobic bacteria, particularly of the Enterobacteriaceae family. Further, assault by an enteropathogen and host-mediated inflammation combined to significantly reduce the total numbers of resident colonic bacteria. These findings underscore the importance of intestinal microbial ecosystems in infectious colitis and noninfectious intestinal inflammatory conditions,such as inflammatory bowel disease.
A combination of new measurements and analysis of historical data from several geographic regions was used to address four issues that affect the reliability and interpretation of colored dissolved ...organic matter (CDOM) measured by remote sensing of inland waters. First, high variability of CDOM levels in lakes and rivers was found at seasonal and multi-year time scales and at shorter intervals in some rivers and lakes. Coefficients of variation (CVs) of 30%–50% for absorptivity at 440nm (a440) were common in historical and new data sets we examined. CDOM values used to calibrate imagery thus should be measured close to the image acquisition date, preferably within 1–2 months in lakes and a few days in large rivers, unless it can be shown that CDOM levels are temporally stable over longer (or shorter) time periods in a given aquatic system. Second, spectral slopes (S) for CDOM in the visible range vary little over time (even over multi-year periods) within sites. Substantial variation was found between sites, however, and most spectra showed a change in slope near 460nm. Values of S400–460 for waters with moderate to high CDOM levels generally were within a narrow range (~0.014–0.018) and similar to reported S values in the near UV. Values of S400–460 for waters with low CDOM generally were smaller and more variable, as were values for S460–650 for all waters. Overall, the variability of spectral slopes in the visible range should not have a large effect on the reliability of a440 estimates made from remote sensing, which in many models involve reflectance measurements at wavelengths>500nm. Third, although a strong correlation (r2=0.925) was found between CDOM levels and DOC concentrations in 34 surface waters sampled in 2013, the standard error of estimate suggests an uncertainty of~±20% in predicting DOC at a440=5m−1 (a moderate CDOM level). Moreover, CDOM–DOC relationships for unpublished data sets we analyzed and those reported in the literature indicate that both the fraction of DOC that is colored and slopes of regressions between CDOM and DOC are highly variable in space and time. Prediction of DOC concentrations in water bodies from CDOM levels (whether measured in the laboratory or by remote sensing) thus is associated with considerable uncertainty. For the present, this implies that field sampling is required to verify DOC concentrations predicted from remotely sensed CDOM measurements until we have a better understanding of variations in DOC–CDOM relationships. Fourth, shapes of reflectance spectra for CDOM-rich waters varied greatly depending on the concentrations of other constituents (suspended solids and chlorophyll) that affect the optical properties of water. Nonetheless, it is not obvious from our results for several predictive models that different remote sensing algorithms are needed to calculate CDOM levels accurately for waters where CDOM is the only variable affecting reflectance versus waters where other constituents also affect the spectra. The best band or band ratio models for simulated Landsat 8, Sentinel-2 and Sentinel-3 bands from field measured reflectance spectra yielded high r2 values (0.84–0.86) for a440. The broader Landsat 8 bands worked nearly as well for a440 as the narrower Sentinel band sets and hyperspectral bands, probably because CDOM is characterized by a broad exponential increase in absorbance with decreasing wavelength rather than specific peaks or troughs in absorbance or reflectance.
•Evaluates temporal variability of CDOM in surface waters•Evaluates spatial and temporal variability of CDOM spectral slopes in inland waters•Evaluates relationships between CDOM and DOC in inland waters•Reports reflectance spectra for CDOM-dominated and optically complex waters•Evaluates predictive models for CDOM for simple and optically complex waters
Spatial and temporal variations in stable carbon isotope ratios (i.e., δ 13C) of primary producers are common but poorly understood features of isotopic characterizations of aquatic food webs. I ...investigated factors that control δ 13C of algae (concentration and δ 13C of inorganic carbon, algal fractionation, and growth rates) in riffle habitats across a gradient in stream size and productivity in northern California. There was considerable seasonal and spatial variation in δ 13C of the green alga Cladophora glomerata, microalgal-influenced epilithic biofilms, and their herbivores. Algal and herbivore δ 13C were depleted in 13C in small, unproductive tributary streams (-44‰ to -30‰) compared with more productive sites downstream (-31‰ to -23‰). The majority of variation in algal δ 13C of Cladophora and epilithic biofilms was determined by dissolved $\text{CO}_{2}(\text{CO}_{2\text{aq}})$ via effects on δ 13C of $\text{CO}_{2\text{aq}}$ and photosynthetic fractionation. In contrast, two other taxa (the cyanobacterium Nostoc pruniforme and the red alga Lemanea sp.) showed little variation in δ 13C or fractionation in response to varied inorganic carbon availability because of their distinct modes of inorganic carbon acquisition. Although variation in algal δ 13C might complicate use of δ 13C to resolve consumer diet sources under some circumstances, better understanding of such variation should improve the use of δ 13C techniques in aquatic food web studies.
Abstract
The gold-standard treatment for Parkinson’s disease is levodopa (L-DOPA), which is taken orally and absorbed intestinally. L-DOPA must reach the brain intact to exert its clinical effect; ...peripheral metabolism by host and microbial enzymes is a clinical management issue. The gut microbiota is altered in PD, with one consistent and unexplained observation being an increase in
Bifidobacterium
abundance among patients. Recently, certain
Bifidobacterium
species were shown to have the ability to metabolize L-tyrosine, an L-DOPA structural analog. Using both clinical cohort data and in vitro experimentation, we investigated the potential for commensal
Bifidobacteria
to metabolize this drug. In PD patients,
Bifidobacterium
abundance was positively correlated with L-DOPA dose and negatively with serum tyrosine concentration. In vitro experiments revealed that certain species, including
B. bifidum
,
B. breve
, and
B. longum
, were able to metabolize this drug via deamination followed by reduction to the compound 3,4-dihydroxyphenyl lactic acid (DHPLA) using existing tyrosine-metabolising genes
.
DHPLA appears to be a waste product generated during regeneration of NAD +. This metabolism occurs at low levels in rich medium, but is significantly upregulated in nutrient-limited minimal medium. Discovery of this novel metabolism of L-DOPA to DHPLA by a common commensal may help inform medication management in PD.
In December 2019, the International Association of Geomagnetism and Aeronomy (IAGA) Division V Working Group (V-MOD) adopted the thirteenth generation of the International Geomagnetic Reference Field ...(IGRF). This IGRF updates the previous generation with a definitive main field model for epoch 2015.0, a main field model for epoch 2020.0, and a predictive linear secular variation for 2020.0 to 2025.0. This letter provides the equations defining the IGRF, the spherical harmonic coefficients for this thirteenth generation model, maps of magnetic declination, inclination and total field intensity for the epoch 2020.0, and maps of their predicted rate of change for the 2020.0 to 2025.0 time period.
Fluid motions in the Earth’s core produce changes in the geomagnetic field (secular variation) and are also an important ingredient in the planet’s rotational dynamics. In this article we review ...current understanding of core dynamics focusing on short timescales of years to centuries. We describe both theoretical models and what may be inferred from geomagnetic and geodetic observations. The kinematic concepts of frozen flux and magnetic diffusion are discussed along with relevant dynamical regimes of magnetostrophic balance, tangential geostrophy, and quasi-geostrophy. An introduction is given to free modes and waves that are expected to be present in Earth’s core including axisymmetric torsional oscillations and non-axisymmetric Magnetic-Coriolis waves. We focus on important recent developments and promising directions for future investigations.
Inferring the core dynamics responsible for the observed geomagnetic secular variation requires knowledge of the magnetic field at the core‐mantle boundary together with its associated model ...covariances. However, most currently available field models have been built using regularization conditions, which force the expansions in the spatial and time domains to converge but also hinder the calculation of reliable second‐order statistics. To tackle this issue, we propose a stochastic approach that integrates, through time covariance functions, some prior information on the time evolution of the geomagnetic field. We consider the time series of spherical harmonic coefficients as realizations of a continuous and differentiable stochastic process. Our specific choice of process, such that it is not twice differentiable, mainly relies on two properties of magnetic observatory records (time spectra, existence of geomagnetic jerks). In addition, the required characteristic times for the low degree coefficients are obtained from available models of the magnetic field and its secular variation based on satellite data. We construct the new family COV‐OBS of field models spanning the observatory and satellite era of 1840–2010. These models include the external dipole and permit sharper time changes of the internal field compared to previous regularized reconstructions. The a posteriori covariance matrix displays correlations in both space and time, which should be accounted for through the secular variation error model in core flow inversions and geomagnetic data assimilation studies.
Key PointsModeling the geomagnetic secular vartiationUse of the stochastic processes frameworkObtain realistic model covariances
On page 625, Cheng et al.3 report that sources of river nitrogen pollution in the United States are often spatially separated from existing wetlands (Fig. 1), which can remove nitrate from water, and ...show that wetland restoration targeted to nitrate sources would yield substantial benefits for downstream water quality. ...wetlands provide other important ecosystem services such as sequestering atmospheric carbon, supporting biodiversity, and reducing flooding and stream-bank erosion6. ...the benefits of wetland restoration would extend to other ecosystem services. ...substantial policy and legal uncertainties regarding US federal rules governing water management on private land11 must be resolved to overcome barriers to conservation efforts.
Wetlands remove nitrate pollution from water effectively. An analysis shows that this effect is constrained in the United States by the distribution of wetlands, and could be increased by targeting ...wetland restoration to nitrate sources.
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