The intestinal microbiota: Antibiotics, colonization resistance, and enteric pathogens.

The human gastrointestinal tract hosts a diverse network of microorganisms, collectively known as the microbiota that plays an important role in health and disease. For instance, the intestinal microbiota can prevent invading microbes from colonizing the gastrointestinal tract, a phenomenon known as colonization resistance. Perturbations to the microbiota, such as antibiotic administration, can alter microbial composition and result in the loss of colonization resistance. Consequently, the host may be rendered susceptible to colonization by a pathogen. This is a particularly relevant concern in the hospital setting, where antibiotic use and antibiotic-resistant pathogen exposure are more frequent. Many nosocomial infections arise from gastrointestinal colonization. Due to their resistance to antibiotics, treatment is often very challenging. However, recent studies have demonstrated that manipulating the commensal microbiota can prevent and treat various infections in the intestine. In this review, we discuss the members of the microbiota, as well as the mechanisms, that govern colonization resistance against specific pathogens. We also review the effects of antibiotics on the microbiota, as well as the unique epidemiology of immunocompromised patients that renders them a particularly high-risk population to intestinal nosocomial infections.

Future methane emissions from the heavy-duty natural gas transportation sector for stasis, high, medium, and low scenarios in 2035.

Today's heavy-duty natural gas-fueled fleet is estimated to represent less than 2% of the total fleet. However, over the next couple of decades, predictions are that the percentage could grow to represent as much as 50%. Although fueling switching to natural gas could provide a climate benefit relative to diesel fuel, the potential for emissions of methane (a potent greenhouse gas) from natural gas-fueled vehicles has been identified as a concern. Since today's heavy-duty natural gas-fueled fleet penetration is low, today's total fleet-wide emissions will be also be low regardless of per vehicle emissions. However, predicted growth could result in a significant quantity of methane emissions. To evaluate this potential and identify effective options for minimizing emissions, future growth scenarios of heavy-duty natural gas-fueled vehicles, and compressed natural gas and liquefied natural gas fueling stations that serve them, have been developed for 2035, when the populations could be significant. The scenarios rely on the most recent measurement campaign of the latest manufactured technology, equipment, and vehicles reported in a companion paper as well as projections of technology and practice advances. These "pump-to-wheels"(PTW) projections do not include methane emissions outside of the bounds of the vehicles and fuel stations themselves and should not be confused with a complete wells-to-wheels analysis. Stasis, high, medium, and low scenario PTW emissions projections for 2035 were 1.32%, 0.67%, 0.33%, and 0.15% of the fuel used. The scenarios highlight that a large emissions reductions could be realized with closed crankcase operation, improved best practices, and implementation of vent mitigation technologies. Recognition of the potential pathways for emissions reductions could further enhance the heavy-duty transportation sectors ability to reduce carbon emissions. IMPLICATIONS: Newly collected pump-to-wheels methane emissions data for current natural gas technologies were combined with future market growth scenarios, estimated technology advancements, and best practices to examine the climate benefit of future fuel switching. The analysis indicates the necessary targets of efficiency, methane emissions, market penetration, and best practices necessary to enable a pathway for natural gas to reduce the carbon intensity of the heavy-duty transportation sector.

Rapid evaporative ionisation mass spectrometry of electrosurgical vapours for the identification of breast pathology: towards an intelligent knife for breast cancer surgery.

BACKGROUND: Re-operation for positive resection margins following breast-conserving surgery occurs frequently (average = 20-25%), is cost-inefficient, and leads to physical and psychological morbidity. Current margin assessment techniques are slow and labour intensive. Rapid evaporative ionisation mass spectrometry (REIMS) rapidly identifies dissected tissues by determination of tissue structural lipid profiles through on-line chemical analysis of electrosurgical aerosol toward real-time margin assessment. METHODS: Electrosurgical aerosol produced from ex-vivo and in-vivo breast samples was aspirated into a mass spectrometer (MS) using a monopolar hand-piece. Tissue identification results obtained by multivariate statistical analysis of MS data were validated by histopathology. Ex-vivo classification models were constructed from a mass spectral database of normal and tumour breast samples. Univariate and tandem MS analysis of significant peaks was conducted to identify biochemical differences between normal and cancerous tissues. An ex-vivo classification model was used in combination with bespoke recognition software, as an intelligent knife (iKnife), to predict the diagnosis for an ex-vivo validation set. Intraoperative REIMS data were acquired during breast surgery and time-synchronized to operative videos. RESULTS: A classification model using histologically validated spectral data acquired from 932 sampling points in normal tissue and 226 in tumour tissue provided 93.4% sensitivity and 94.9% specificity. Tandem MS identified 63 phospholipids and 6 triglyceride species responsible for 24 spectral differences between tissue types. iKnife recognition accuracy with 260 newly acquired fresh and frozen breast tissue specimens (normal n = 161, tumour n = 99) provided sensitivity of 90.9% and specificity of 98.8%. The ex-vivo and intra-operative method produced visually comparable high intensity spectra. iKnife interpretation of intra-operative electrosurgical vapours, including data acquisition and analysis was possible within a mean of 1.80 seconds (SD ±0.40). CONCLUSIONS: The REIMS method has been optimised for real-time iKnife analysis of heterogeneous breast tissues based on subtle changes in lipid metabolism, and the results suggest spectral analysis is both accurate and rapid. Proof-of-concept data demonstrate the iKnife method is capable of online intraoperative data collection and analysis. Further validation studies are required to determine the accuracy of intra-operative REIMS for oncological margin assessment.

Pump-to-Wheels Methane Emissions from the Heavy-Duty Transportation Sector.

Pump-to-wheels (PTW) methane emissions from the heavy-duty (HD) transportation sector, which have climate change implications, are poorly documented. In this study, methane emissions from HD natural gas fueled vehicles and the compressed natural gas (CNG) and liquefied natural gas (LNG) fueling stations that serve them were characterized. A novel measurement system was developed to quantify methane leaks and losses. Engine related emissions were characterized from twenty-two natural gas fueled transit buses, refuse trucks, and over-the-road (OTR) tractors. Losses from six LNG and eight CNG stations were characterized during compression, fuel delivery, storage, and from leaks. Cryogenic boil-off pressure rise and pressure control venting from LNG storage tanks were characterized using theoretical and empirical modeling. Field and laboratory observations of LNG storage tanks were used for model development and evaluation. PTW emissions were combined with a specific scenario to view emissions as a percent of throughput. Vehicle tailpipe and crankcase emissions were the highest sources of methane. Data from this research are being applied by the authors to develop models to forecast methane emissions from the future HD transportation sector.

Design and Use of a Full Flow Sampling System (FFS) for the Quantification of Methane Emissions.

The use of natural gas continues to grow with increased discovery and production of unconventional shale resources. At the same time, the natural gas industry faces continued scrutiny for methane emissions from across the supply chain, due to methane's relatively high global warming potential (25-84x that of carbon dioxide, according to the Energy Information Administration). Currently, a variety of techniques of varied uncertainties exists to measure or estimate methane emissions from components or facilities. Currently, only one commercial system is available for quantification of component level emissions and recent reports have highlighted its weaknesses. In order to improve accuracy and increase measurement flexibility, we have designed, developed, and implemented a novel full flow sampling system (FFS) for quantification of methane emissions and greenhouse gases based on transportation emissions measurement principles. The FFS is a modular system that consists of an explosive-proof blower(s), mass airflow sensor(s) (MAF), thermocouple, sample probe, constant volume sampling pump, laser based greenhouse gas sensor, data acquisition device, and analysis software. Dependent upon the blower and hose configuration employed, the current FFS is able to achieve a flow rate ranging from 40 to 1,500 standard cubic feet per minute (SCFM). Utilization of laser-based sensors mitigates interference from higher hydrocarbons (C2+). Co-measurement of water vapor allows for humidity correction. The system is portable, with multiple configurations for a variety of applications ranging from being carried by a person to being mounted in a hand drawn cart, on-road vehicle bed, or from the bed of utility terrain vehicles (UTVs). The FFS is able to quantify methane emission rates with a relative uncertainty of ± 4.4%. The FFS has proven, real world operation for the quantification of methane emissions occurring in conventional and remote facilities.

Methane Emissions from Leak and Loss Audits of Natural Gas Compressor Stations and Storage Facilities.

As part of the Environmental Defense Fund's Barnett Coordinated Campaign, researchers completed leak and loss audits for methane emissions at three natural gas compressor stations and two natural gas storage facilities. Researchers employed microdilution high-volume sampling systems in conjunction with in situ methane analyzers, bag samples, and Fourier transform infrared analyzers for emissions rate quantification. All sites had a combined total methane emissions rate of 94.2 kg/h, yet only 12% of the emissions total resulted from leaks. Methane slip from exhausts represented 44% of the total emissions. Remaining methane emissions were attributed to losses from pneumatic actuators and controls, engine crankcases, compressor packing vents, wet seal vents, and slop tanks. Measured values were compared with those reported in literature. Exhaust methane emissions were lower than emissions factor estimates for engine exhausts, but when combined with crankcase emissions, measured values were 11.4% lower than predicted by AP-42 as applicable to emissions factors for four-stroke, lean-burn engines. Average measured wet seal emissions were 3.5 times higher than GRI values but 14 times lower than those reported by Allen et al. Reciprocating compressor packing vent emissions were 39 times higher than values reported by GRI, but about half of values reported by Allen et al. Though the data set was small, researchers have suggested a method to estimate site-wide emissions factors for those powered by four-stroke, lean-burn engines based on fuel consumption and site throughput.

Near-Field Characterization of Methane Emission Variability from a Compressor Station Using a Model Aircraft.

A model aircraft equipped with a custom laser-based, open-path methane sensor was deployed around a natural gas compressor station to quantify the methane leak rate and its variability at a compressor station in the Barnett Shale. The open-path, laser-based sensor provides fast (10 Hz) and precise (0.1 ppmv) measurements of methane in a compact package while the remote control aircraft provides nimble and safe operation around a local source. Emission rates were measured from 22 flights over a one-week period. Mean emission rates of 14 ± 8 g CH4 s(-1) (7.4 ± 4.2 g CH4 s(-1) median) from the station were observed or approximately 0.02% of the station throughput. Significant variability in emission rates (0.3-73 g CH4 s(-1) range) was observed on time scales of hours to days, and plumes showed high spatial variability in the horizontal and vertical dimensions. Given the high spatiotemporal variability of emissions, individual measurements taken over short durations and from ground-based platforms should be used with caution when examining compressor station emissions. More generally, our results demonstrate the unique advantages and challenges of platforms like small unmanned aerial vehicles for quantifying local emission sources to the atmosphere.