Host–microbiome intestinal interactions during early life: considerations for atopy and asthma development

Purpose of review: The body's largest microbial community, the gut microbiome, is in contact with mucosal surfaces populated with epithelial, immune, endocrine and nerve cells, all of which sense and respond to microbial signals. These mutual interactions have led to a functional coevolution between the microbes and human physiology. Examples of coadaptation are anaerobes Bifidobacteria and Bacteroides, which have adjusted their metabolism to dietary components of human milk, and infant immune development, which has evolved to become reliant on the presence of beneficial microbes. Current research suggests that specific composition of the early-life gut microbiome aligns with the maturation of host immunity. Disruptions of natural microbial succession patterns during gut colonization are a consistent feature of immune-mediated diseases, including atopy and asthma. Recent findings: Here, we catalog recent birth cohorts documenting associations between immune dysregulation and microbial alterations, and summarize the evidence supporting the role of the gut microbiome as an etiological determinant of immune-mediated allergic diseases. Summary: Ecological concepts that describe microbial dynamics in the context of the host environment, and a portray of immune and neuroendocrine signaling induced by host–microbiome interactions, have become indispensable in describing the molecular role of early-life microbiome in atopy and asthma susceptibility

DeepPrecip: A deep neural network for precipitation retrievals

Remotely-sensed precipitation retrievals are critical for advancing our understanding of global energy and hydrologic cycles in remote regions. Radar reflectivity profiles of the lower atmosphere are commonly linked to precipitation through empirical power laws, but these relationships are tightly coupled to particle microphysical assumptions that do not generalize well to different regional climates. Here, we develop a robust, highly generalized precipitation retrieval from a deep convolutional neural network (DeepPrecip) to estimate 20-minute average surface precipitation accumulation using near-surface radar data inputs. DeepPrecip displays high retrieval skill and can accurately model total precipitation accumulation, with a mean square error (MSE) 99 % lower, on average, than current methods. DeepPrecip also outperforms a less complex machine learning retrieval algorithm, demonstrating the value of deep learning when applied to precipitation retrievals. Predictor importance analyses suggest that a combination of both near-surface (below 1 km) and higher-altitude (1.5 &ndash; 2 km) radar measurements are the primary features contributing to retrieval accuracy. Further, DeepPrecip closely captures total precipitation accumulation magnitudes and variability across nine distinct locations without requiring any explicit descriptions of particle microphysics or geospatial covariates. This research reveals the important role for deep learning in extracting relevant information about precipitation from atmospheric radar retrievals.</p

Retrieval of Snow Water Equivalent by the Precipitation Imaging Package (PIP) in the Northern Great Lakes

Performance of the Precipitation Imaging Package (PIP) for estimating the snow water equivalent (SWE) is evaluated through a comparative study with the collocated National Oceanic and Atmospheric Administration National Weather Service snow stake field measurements. The PIP together with a vertically pointing radar, a weighing bucket gauge, and a laser-optical disdrometer was deployed at the NWS Marquette, Michigan, office building for a long-term field study supported by the National Aeronautics and Space Administration's Global Precipitation Measurement mission Ground Validation program. The site was also equipped with a weather station. During the 2017/18 winter, the PIP functioned nearly uninterrupted at frigid temperatures accumulating 2345.8 mm of geometric snow depth over a total of 499 h. This long record consists of 30 events, and the PIP-retrieved and snow stake field measured SWE differed less than 15% in every event. Two of the major events with the longest duration and the highest accumulation are examined in detail. The particle mass with a given diameter was much lower during a shallow, colder, uniform lake-effect event than in the deep, less cold, and variable synoptic event. This study demonstrated that the PIP is a robust instrument for operational use, and is reliable for deriving the bulk properties of falling snow.Peer reviewe

Spatial and temporal variability of snowfall over Greenland from CloudSat observations

We use the CloudSat 2006&ndash;2016 data record to estimate snowfall over the Greenland Ice Sheet (GrIS). We first evaluate CloudSat snowfall retrievals with respect to remaining ground-clutter issues. Comparing CloudSat observations to the GrIS topography (obtained from airborne altimetry measurements during IceBridge) we find that at the edges of the GrIS spurious high-snowfall retrievals caused by ground clutter occasionally affect the operational snowfall product. After correcting for this effect, the height of the lowest valid CloudSat observation is about 1200&thinsp;m above the local topography as defined by IceBridge. We then use ground-based millimeter wavelength cloud radar (MMCR) observations obtained from the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit, Greenland (ICECAPS) experiment to devise a simple, empirical correction to account for precipitation processes occurring between the height of the observed CloudSat reflectivities and the snowfall near the surface. Using the height-corrected, clutter-cleared CloudSat reflectivities we next evaluate various Z&ndash;S relationships in terms of snowfall accumulation at Summit through comparison with weekly stake field observations of snow accumulation available since 2007. Using a set of three Z&ndash;S relationships that best agree with the observed accumulation at Summit, we then calculate the annual cycle snowfall over the entire GrIS as well as over different drainage areas and compare the derived mean values and annual cycles of snowfall to ERA-Interim reanalysis. We find the annual mean snowfall over the GrIS inferred from CloudSat to be 34&plusmn;7.5&thinsp;cm&thinsp;yr&minus;1 liquid equivalent (where the uncertainty is determined by the range in values between the three different Z&ndash;S relationships used). In comparison, the ERA-Interim reanalysis product only yields 30&thinsp;cm&thinsp;yr&minus;1 liquid equivalent snowfall, where the majority of the underestimation in the reanalysis appears to occur in the summer months over the higher GrIS and appears to be related to shallow precipitation events. Comparing all available estimates of snowfall accumulation at Summit Station, we find the annually averaged liquid equivalent snowfall from the stake field to be between 20 and 24&thinsp;cm&thinsp;yr&minus;1, depending on the assumed snowpack density and from CloudSat 23&plusmn;4.5&thinsp;cm&thinsp;yr&minus;1. The annual cycle at Summit is generally similar between all data sources, with the exception of ERA-Interim reanalysis, which shows the aforementioned underestimation during summer months. </div

Exploring Snowfall Variability through the High-Latitude Measurement of Snowfall (HiLaMS) Field Campaign

The High-Latitude Measurement of Snowfall (HiLaMS) campaign explored variability in snowfall properties and processes at meteorologically distinct field sites located in Haukeliseter, Norway, and Kiruna, Sweden, during the winters of 2016/17 and 2017/18, respectively. Campaign activities were founded upon the sensitivities of a low-cost, core instrumentation suite consisting of Micro Rain Radar, Precipitation Imaging Package, and Multi-Angle Snow Camera. These instruments are highly portable to remote field sites and, considered together, provide a unique and complementary set of snowfall observations including snowflake habit, particle size distributions, fall speeds, surface snowfall accumulations, and vertical profiles of radar moments and snow water content. These snow-specific parameters, used in combination with existing observations from the field sites such as snow gauge accumulations and ambient weather conditions, allow for advanced studies of snowfall processes. HiLaMS observations were used to 1) successfully develop a combined radar and in situ microphysical property retrieval scheme to estimate both surface snowfall accumulation and the vertical profile of snow water content, 2) identify the predominant snowfall regimes at Haukeliseter and Kiruna and characterize associated macrophysical and microphysical properties, snowfall production, and meteorological conditions, and 3) identify biases in the HARMONIE-AROME numerical weather prediction model for forecasts of snowfall accumulations and vertical profiles of snow water content for the distinct snowfall regimes observed at the mountainous Haukeliseter site. HiLaMS activities and results suggest value in the deployment of this enhanced snow observing instrumentation suite to new and diverse high-latitude locations that may be underrepresented in climate and weather process studies.Exploring Snowfall Variability through the High-Latitude Measurement of Snowfall (HiLaMS) Field CampaignpublishedVersio

The Precipitation Imaging Package : Assessment of Microphysical and Bulk Characteristics of Snow

Remote-sensing observations are needed to estimate the regional and global impacts of snow. However, to retrieve accurate estimates of snow mass and rate, these observations require augmentation through additional information and assumptions about hydrometeor properties. The Precipitation Imaging Package (PIP) provides information about precipitation characteristics and can be utilized to improve estimates of snowfall rate and accumulation. Here, the goal is to demonstrate the quality and utility of two higher-order PIP-derived products: liquid water equivalent snow rate and an approximation of volume-weighted density called equivalent density. Accuracy of the PIP snow rate and equivalent density is obtained through intercomparison with established retrieval methods and through evaluation with colocated ground-based observations. The results confirm the ability of the PIP-derived products to quantify properties of snow rate and equivalent density, and demonstrate that the PIP produces physically realistic snow characteristics. When compared to the National Weather Service (NWS) snow field measurements of six-hourly accumulation, the PIP-derived accumulations were biased only +2.48% higher. Additionally, this work illustrates fundamentally different microphysical and bulk features of low and high snow-to-liquid ratio events, through assessment of observed particle size distributions, retrieved mass coefficients, and bulk properties. Importantly, this research establishes the role that PIP observations and higher-order products can serve for constraining microphysical assumptions in ground-based and spaceborne remotely sensed snowfall retrievals.Peer reviewe

The Location and Nature of General Anesthetic Binding Sites on the Active Conformation of Firefly Luciferase; A Time Resolved Photolabeling Study

Firefly luciferase is one of the few soluble proteins that is acted upon by a wide variety of general anesthetics and alcohols; they inhibit the ATP–driven production of light. We have used time–resolved photolabeling to locate the binding sites of alcohols during the initial light output, some 200 ms after adding ATP. The photolabel 3-azioctanol inhibited the initial light output with an IC50 of 200 µM, close to its general anesthetic potency. Photoincorporation of [3H]3-azioctanol into luciferase was saturable but weak. It was enhanced 200 ms after adding ATP but was negligible minutes later. Sequencing of tryptic digests by HPLC–MSMS revealed a similar conformation–dependence for photoincorporation of 3-azioctanol into Glu-313, a residue that lines the bottom of a deep cleft (vestibule) whose outer end binds luciferin. An aromatic diazirine analog of benzyl alcohol with broader side chain reactivity reported two sites. First, it photolabeled two residues in the vestibule, Ser-286 and Ile-288, both of which are implicated with Glu-313 in the conformation change accompanying activation. Second, it photolabeled two residues that contact luciferin, Ser-316 and Ser-349. Thus, time resolved photolabeling supports two mechanisms of action. First, an allosteric one, in which anesthetics bind in the vestibule displacing water molecules that are thought to be involved in light output. Second, a competitive one, in which anesthetics bind isosterically with luciferin. This work provides structural evidence that supports the competitive and allosteric actions previously characterized by kinetic studies

Modular architecture of eukaryotic RNase P and RNase MRP revealed by electron microscopy

Ribonuclease P (RNase P) and RNase MRP are closely related ribonucleoprotein enzymes, which process RNA substrates including tRNA precursors for RNase P and 5.8 S rRNA precursors, as well as some mRNAs, for RNase MRP. The structures of RNase P and RNase MRP have not yet been solved, so it is unclear how the proteins contribute to the structure of the complexes and how substrate specificity is determined. Using electron microscopy and image processing we show that eukaryotic RNase P and RNase MRP have a modular architecture, where proteins stabilize the RNA fold and contribute to cavities, channels and chambers between the modules. Such features are located at strategic positions for substrate recognition by shape and coordination of the cleaved-off sequence. These are also the sites of greatest difference between RNase P and RNase MRP, highlighting the importance of the adaptation of this region to the different substrates