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Islet autoantibody seroconversion in type-1 diabetes is associated with metagenome-assembled genomes in infant gut microbiomes
Get PDFThe immune system of some genetically susceptible children can be triggered by certain environmental factors to produce islet autoantibodies (IA) against pancreatic β cells, which greatly increases their risk for Type-1 diabetes. An environmental factor under active investigation is the gut microbiome due to its important role in immune system education. Here, we study gut metagenomes that are de-novo-assembled in 887 at-risk children in the Environmental Determinants of Diabetes in the Young (TEDDY) project. Our results reveal a small set of core protein families, present in >50% of the subjects, which account for 64% of the sequencing reads. Time-series binning generates 21,536 high-quality metagenome-assembled genomes (MAGs) from 883 species, including 176 species that hitherto have no MAG representation in previous comprehensive human microbiome surveys. IA seroconversion is positively associated with 2373 MAGs and negatively with 1549 MAGs. Comparative genomics analysis identifies lipopolysaccharides biosynthesis in Bacteroides MAGs and sulfate reduction in Anaerostipes MAGs as functional signatures of MAGs with positive IA-association. The functional signatures in the MAGs with negative IA-association include carbohydrate degradation in lactic acid bacteria MAGs and nitrate reduction in Escherichia MAGs. Overall, our results show a distinct set of gut microorganisms associated with IA seroconversion and uncovered the functional genomics signatures of these IA-associated microorganisms.We appreciate the data and technical support provided by the TEDDY project, which is supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). This work is supported by an R01 grant (R01AT011618) to C.P. and R.S.M. from National Center for Complementary & Integrative Health and National Institute of General Medical Sciences and a Team Science grant to K.R.J. and C.P. from Presbyterian Health Foundation of Oklahoma City and Harold Hamm Diabetes Center. The high-performance computing was provided by the OU Supercomputing Center for Education & Research (OSCER). Financial support was provided by the University of Oklahoma Libraries’ Open Access Fund.YesNature Communications thanks the anonymous reviewers for their contribution to the peer review of this work
An Experimental Setup to Measure Argon Leak Rate Through Barriers for Static and Rotational Motion
Get PDFAs a greenhouse gas, methane significantly impacts global warming and air pollution. Oil and gas production, crude oil transportation, refining, and natural gas processing, transportation, and distribution are considered the main sources of methane emission in the oil and gas industry. Specifically, valves, joints, and moving parts where barriers were used have a magnificent role in methane emission. To address this problem, International Standard Organization(ISO) through ISO 15848 suggested that the oil and gas industry test the fugitive emission on their products with helium as a testing gas. Although, helium is an inert gas and its specifications make it an ideal gas for emission tests in the oil and gas industry, difficulty in production, transportation, high price, and global helium shortage lead us to find an alternative material. In this study, a setup was built to test argon emission in 25C, 121C, and 204C and pressure ranges of 600, 2250, 3750, 6750, and 10,000 psi on v-rings for static and rotational shafts with 2 rpm and 10 rpm. Experimental results were used to generate a machine learning model. Finally, a general polynomial formula was presented based on the machine learning model for static and rotational shafts with 2 rpm and 10 rpm. Results show the impact of temperature and rotational shaft’s speed on leak rate is significant. This study’s results apply to the valve, compressors, and any dynamic seal test process. Generally, the applications of this study can be to reduce the cost of the production and leak rate tests in rotational equipment design for the oil and gas industry
Electromagnetic Modeling Methods for Microstrip Patch Antennas up to the Millimeter-Wave and Sub-Terahertz Bands
Get PDFIn the current world of highly integrated communications, reliable and robust systems will be required to develop the 6G networks. The millimeter-wave band (30 GHz–100 GHz) and the sub-terahertz band (100 GHz–300 GHz) have promising possibilities in radar and communication systems, such as broad bandwidth, device miniaturization, and high integration with electronic technology. As 6G communications will be the dominant technology in the coming years, highly-accurate antenna design is becoming essential to building systems that meet the expected performance standards. Despite the wide availability of antenna models working at frequencies below 10 GHz, they need to be in-depth reviewed and reformulated, especially in the sub-terahertz band. Thus, the work developed in this doctoral dissertation provides a framework of analytical methods for electromagnetic antenna modeling, enabling the design of microstrip patch antennae up to 300 GHz. This work covers unprecedentedly diverse models in frequency ranges from radio frequency to the sub-terahertz band. The proposed model formulations consider the geometrical and electrical imperfections of materials used for antenna design. They show high
accuracy in the modeled frequency response for measured antennas and transmission lines up to 110 GHz; and for simulated microstrip patch antennas up to 300 GHz, with thickness up to 5 % of the free-space wavelength, copper
layers up to 35 μm thick, and with surface roughness up to 1 μm
In Situ Observations of Southern Ocean Clouds from The SOCRATES Field Campaign: Evaluating Cloud Phase, Aerosol-Cloud Interactions, Cloud Layer Types and Entrainment-Mixing Impacts on Mixed Phase Clouds
Get PDFLow level clouds are ubiquitous over the Southern Ocean. However, climate and weather models fail to accurately simulate their radiative impact. This has been attributed in part to the inadequate representation of cloud phase distributions. Using in situ airborne observations acquired during the Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) campaign, this dissertation classifies cloud samples with horizontal spatial resolutions ranging from 120‒150 m as either liquid, ice or mixed phase (i.e., liquid and ice particles in the same volume). Cloud phase is determined using a combination of data from the in situ cloud probes and a supervised machine learning algorithm, which determines phase based on particle imagery. An abundance of liquid phase samples is observed over the region (70%) at temperatures from -20° to 0°C. The prevalence of supercooled liquid abruptly decreases to single digit percentages at temperatures less than -20°C. There is also a notable ice phase presence (10%) at relatively high temperatures (> -5°C).
Ice nucleating particle (INP) and cloud condensation nuclei (CCN) concentrations are compared with relative cloud phase frequencies within and above the boundary layer. A positive correlation is found between INP concentrations and ice-containing cloud phase (i.e., ice and mixed phase) frequencies in select cases. However, many cases do not exhibit significant correlation, suggesting a prevalence of alternative ice initiation/growth processes, such as secondary ice production. CCN concentrations are negatively correlated with ice-containing frequencies above the boundary layer, which may be related to longer lifetimes of supercooled liquid clouds in high CCN environments. A strong negative correlation is also found between CCN and large cloud drop (> 25 μm) number concentrations, suggesting secondary ice production may be inhibited in the presence of high CCN concentrations.
A novel cloud layer classification method is introduced to classify cloud layers into single-layer and multi-layer clouds. Normalized occurrence frequencies of ice-containing phases are greater for multi-layer clouds (0.10‒0.32) compared with single-layer clouds (0.05). Frequencies are greatest for the lowest cloud layers of multi-layer clouds, and then incrementally decrease up to the third highest layer. When classifying multi-layer clouds as the lowest, highest, and middle cloud layers, ice-containing frequencies for the lowest and middle cloud layers are similar. These frequencies are greater than those for the highest layers and single-layer clouds, which are also similar to each other. The tendency of greater ice-containing frequencies within the lowest layers of multi-layer clouds suggests a prominent seeder-feeder mechanism exists over the region.
A novel quantitative measure of phase spatial heterogeneity is introduced and used to show that the mixed (liquid) phase is the most (least) spatially heterogeneous phase from temperatures between 20° and 0°C. Greater spatial heterogeneity is associated with broader vertical velocity distributions, suggesting increased turbulence is directly related to spatial heterogeneity. Distributions of precipitation-size particle (diameter > 50 μm) mass and mean diameter shift towards smaller values with greater heterogeneity. These particles are primarily ice, which are observed in mixed and ice phase samples. This may be due to a relationship between cloud lifetime and spatial heterogeneity, where ice particles grow as a mixed phase cloud glaciates resulting in decreasing spatial heterogeneity.
Differences in microphysical properties between coupled and decoupled environments are examined. No significant differences are observed for relative phase frequencies or the spatial heterogeneity. However, drop number concentrations were approximately doubled in coupled environments compared to decoupled environments.
Entrainment-mixing has been shown to impact drop size distributions in warm clouds, but few studies have considered the impacts on mixed phase clouds. By taking advantage of strong correlations between droplet clustering and entrainment-mixing, a clustering metric is used as a proxy to assess the degree of entrainment-mixing in order to maximize the sample size for a statistical analysis of entrainment-mixing impacts on mixed phase cloud properties. A positive relationship is found between the magnitude of droplet clustering and large ice crystal concentrations (diameters greater than ~300 μm), suggesting entrainment-mixing can enhance the Wegener-Bergeron-Findeisen (WBF) process. Particle size distribution functions averaged over different ranges of liquid (LWC) to total water content (TWC) ratio provide insight into the relation of entrainment-mixing to mixed phase cloud evolution. Mixed phase samples with the greatest large ice crystal concentrations occur for LWC/TWC30 μm) are preferentially removed as LWC/TWC transitions from 1 to 0, representative of glaciation.
These results should provide key insights towards improving the representation of Southern Ocean clouds in both low and high resolution models, as well as improve our overall understanding of varying Southern Ocean cloud types and mixed phase clouds
