Trace gas mixing ratio variability versus lifetime in the troposphere and stratosphere: Observations

Several archived data sets have been reviewed to examine the relationship between mixing ratio variability and lifetime for hydrocarbon and halocarbon species in the troposphere and stratosphere. The dependence on lifetime was described by the power law relationship slnX = Aτ-b, where slnX is the standard deviation of the In of the mixing ratios, A is a proportionality coefficient, and b is an exponent that relates to the dominance of sink terms in the regional variability budget. At the Harvard forest ground site, winter and summer data displayed the same lifetime dependence, τ-0.18, which was significantly weaker than the τ-0.5 dependence of remote tropospheric data, indicating that source terms dominated regional variability at Harvard. In addition, the ratio of summer to winter slnX values was found to be similar for all species except ethane, averaging 1.54 ± 0.04. This ratio is consistent with a factor of 11 seasonal change in the species lifetimes, given a τ-0.18 lifetime dependence. Stratospheric data displayed a stronger lifetime dependence than tropospheric trends, indicating a more dominant role for sink terms in describing spatial variability in this region of the atmosphere. We show that a unique power law relationship between slnX ratios for two species Xi and Xj and the kinetic slope of ln(Xi) versus ln(Xj) correlation plots is found to hold in both observations and theory. Thus knowledge of the coefficient b allows for a clearer understanding of the relationship between observed slopes of ln(Xi) versus ln(Xj) correlation plots and the ratio of the species lifetimes. Copyright 1999 by the American Geophysical Union

Does 4D transperineal ultrasound have additional value over 2D transperineal ultrasound for diagnosing posterior pelvic floor disorders in women with obstructed defecation syndrome?

Objective To establish the diagnostic test accuracy of two‐dimensional (2D) and four‐dimensional (4D) transperineal ultrasound (TPUS) for diagnosis of posterior pelvic floor disorders in women with obstructed defecation syndrome (ODS), in order to assess if 4D ultrasound imaging provides additional value. Methods This was a prospective cohort study of 121 consecutive women with ODS. Symptoms of ODS and pelvic organ prolapse on clinical examination were assessed using validated methods. All women underwent both 2D‐ and 4D‐TPUS. Imaging analysis was performed by two blinded observers. Posterior pelvic floor disorders were dichotomized into presence or absence, according to predefined cut‐off values. In the absence of a reference standard, a composite reference standard was created from a combination of results of evacuation proctography, magnetic resonance imaging and endovaginal ultrasound. Primary outcome measures were diagnostic test characteristics of 2D‐ and 4D‐TPUS for rectocele, enterocele, intussusception and anismus. Secondary outcome measures were interobserver agreement, agreement between the two imaging techniques, and association of severity of ODS symptoms and degree of posterior vaginal wall prolapse with conditions observed on imaging. Results For diagnosis of all four posterior pelvic floor disorders, there was no difference in sensitivity or specificity between 2D‐ and 4D‐TPUS (P = 0.131–1.000). Good agreement between 2D‐ and 4D‐TPUS was found for diagnosis of rectocele (κ = 0.675) and moderate agreement for diagnoses of enterocele, intussusception and anismus (κ = 0.465–0.545). There was no difference in rectocele depth measurements between the techniques (19.9 mm for 2D vs 19.0 mm for 4D, P = 0.802). Interobserver agreement was comparable for both techniques, although 2D‐TPUS had excellent interobserver agreement for diagnosis of enterocele and rectocele depth measurements, while this was only moderate and good, respectively, for 4D‐TPUS. Diagnoses of rectocele and enterocele on both 2D‐ and 4D‐TPUS were significantly associated with degree of posterior vaginal wall prolapse on clinical examination (odds ratio (OR) = 1.89–2.72). The conditions observed using either imaging technique were not associated with severity of ODS symptoms (OR = 0.82–1.13). Conclusions There is no evidence of superiority of 4D ultrasound acquisition to dynamic 2D ultrasound acquisition for the diagnosis of posterior pelvic floor disorders. 2D‐ and 4D‐TPUS could be used interchangeably to screen women with symptoms of ODS

Toe clearance and velocity profiles of young and elderly during walking on sloped surfaces

Background Most falls in older adults are reported during locomotion and tripping has been identified as a major cause of falls. Challenging environments (e.g., walking on slopes) are potential interventions for maintaining balance and gait skills. The aims of this study were: 1) to investigate whether or not distributions of two important gait variables [minimum toe clearance (MTC) and foot velocity at MTC (VelMTC)] and locomotor control strategies are altered during walking on sloped surfaces, and 2) if altered, are they maintained at two groups (young and elderly female groups). Methods MTC and VelMTC data during walking on a treadmill at sloped surfaces (+3°, 0° and -3°) were analysed for 9 young (Y) and 8 elderly (E) female subjects. Results MTC distributions were found to be positively skewed whereas VelMTC distributions were negatively skewed for both groups on all slopes. Median MTC values increased (Y = 33%, E = 7%) at negative slope but decreased (Y = 25%, E = 15%) while walking on the positive slope surface compared to their MTC values at the flat surface (0°). Analysis of VelMTC distributions also indicated significantly (p < 0.05) lower minimum and 25th percentile (Q1) values in the elderly at all slopes. Conclusion The young displayed a strong positive correlation between MTC median changes and IQR (interquartile range) changes due to walking on both slopes; however, such correlation was weak in the older adults suggesting differences in control strategies being employed to minimize the risk of tripping

Airborne observations of methane emissions from rice cultivation in the Sacramento Valley of California

Airborne measurements of methane (CH4) and carbon dioxide (CO2) were taken over the rice growing region of California's Sacramento Valley in the late spring of 2010 and 2011. From these and ancillary measurements, we show that CH4 mixing ratios were higher in the planetary boundary layer above the Sacramento Valley during the rice growing season than they were before it, which we attribute to emissions from rice paddies. We derive daytime emission fluxes of CH4 between 0.6 and 2.0% of the CO2 taken up by photosynthesis on a per carbon, or mole to mole, basis. We also use a mixing model to determine an average CH 4/CO2 flux ratio of -0.6% for one day early in the growing season of 2010. We conclude the CH4/CO2 flux ratio estimates from a single rice field in a previous study are representative of rice fields in the Sacramento Valley. If generally true, the California Air Resources Board (CARB) greenhouse gas inventory emission rate of 2.7×1010g CH4/yr is approximately three times lower than the range of probable CH4 emissions (7.8-9.3×10 10g CH4/yr) from rice cultivation derived in this study. We attribute this difference to decreased burning of the residual rice crop since 1991, which leads to an increase in CH4 emissions from rice paddies in succeeding years, but which is not accounted for in the CARB inventory. © 2012. American Geophysical Union. All Rights Reserved

Testing KiDS cross-correlation redshifts with simulations

Measuring cosmic shear in wide-field imaging surveys requires accurate knowledge of the redshift distribution of all sources. The clustering-redshift technique exploits the angular cross-correlation of a target galaxy sample with unknown redshifts and a reference sample with known redshifts. It represents an attractive alternative to colour-based methods of redshift calibration. Here we test the performance of such clustering redshift measurements using mock catalogues that resemble the Kilo-Degree Survey (KiDS). These mocks are created from the MICE simulation and closely mimic the properties of the KiDS source sample and the overlapping spectroscopic reference samples. We quantify the performance of the clustering redshifts by comparing the cross-correlation results with the true redshift distributions in each of the five KiDS photometric redshift bins. Such a comparison to an informative model is necessary due to the incompleteness of the reference samples at high redshifts. Clustering mean redshifts are unbiased at |Δz|< 0.006 under these conditions. The redshift evolution of the galaxy bias of the reference and target samples represents one of the most important systematic errors when estimating clustering redshifts. It can be reliably mitigated at this level of precision using auto-correlation measurements and self-consistency relations, and will not become a dominant source of systematic error until the arrival of Stage-IV cosmic shear surveys. Using redshift distributions from a direct colour-based estimate instead of the true redshift distributions as a model for comparison with the clustering redshifts increases the biases in the mean to up to |Δz|∼0.04. This indicates that the interpretation of clustering redshifts in real-world applications will require more sophisticated (parameterised) models of the redshift distribution in the future. If such better models are available, the clustering-redshift technique promises to be a highly complementary alternative to other methods of redshift calibration

Quantifying sources of methane using light alkanes in the Los Angeles basin, California

Methane (CH4), carbon dioxide (CO2), carbon monoxide (CO), and C2-C5 alkanes were measured throughout the Los Angeles (L.A.) basin in May and June 2010. We use these data to show that the emission ratios of CH4/CO and CH4/CO2 in the L.A. basin are larger than expected from population-apportioned bottom-up state inventories, consistent with previously published work. We use experimentally determined CH4/CO and CH4/CO2 emission ratios in combination with annual State of California CO and CO2 inventories to derive a yearly emission rate of CH4 to the L.A. basin. We further use the airborne measurements to directly derive CH4 emission rates from dairy operations in Chino, and from the two largest landfills in the L.A. basin, and show these sources are accurately represented in the California Air Resources Board greenhouse gas inventory for CH4. We then use measurements of C2-C5 alkanes to quantify the relative contribution of other CH4 sources in the L.A. basin, with results differing from those of previous studies. The atmospheric data are consistent with the majority of CH4 emissions in the region coming from fugitive losses from natural gas in pipelines and urban distribution systems and/or geologic seeps, as well as landfills and dairies. The local oil and gas industry also provides a significant source of CH4 in the area. The addition of CH4 emissions from natural gas pipelines and urban distribution systems and/or geologic seeps and from the local oil and gas industry is sufficient to account for the differences between the top-down and bottom-up CH4 inventories identified in previously published work. Key PointsTop-down estimates of CH4 emissions in L.A. are greater than inventory estimatesEstimates of CH4 emissions from landfills in L.A. agree with CARB inventoryPipeline natural gas and/or seeps, and landfills are main sources of CH4 in L.A. ©2013. American Geophysical Union. All Rights Reserved

Emissions of organic carbon and methane from petroleum and dairy operations in California's San Joaquin Valley

Petroleum and dairy operations are prominent sources of gas-phase organic compounds in California's San Joaquin Valley. It is essential to understand the emissions and air quality impacts of these relatively understudied sources, especially for oil/gas operations in light of increasing US production. Ground site measurements in Bakersfield and regional aircraft measurements of reactive gas-phase organic compounds and methane were part of the CalNex (California Research at the Nexus of Air Quality and Climate Change) project to determine the sources contributing to regional gas-phase organic carbon emissions. Using a combination of near-source and downwind data, we assess the composition and magnitude of emissions, and provide average source profiles. To examine the spatial distribution of emissions in the San Joaquin Valley, we developed a statistical modeling method using ground-based data and the FLEXPART-WRF transport and meteorological model. We present evidence for large sources of paraffinic hydrocarbons from petroleum operations and oxygenated compounds from dairy (and other cattle) operations. In addition to the small straight-chain alkanes typically associated with petroleum operations, we observed a wide range of branched and cyclic alkanes, most of which have limited previous in situ measurements or characterization in petroleum operation emissions. Observed dairy emissions were dominated by ethanol, methanol, acetic acid, and methane. Dairy operations were responsible for the vast majority of methane emissions in the San Joaquin Valley; observations of methane were well correlated with non-vehicular ethanol, and multiple assessments of the spatial distribution of emissions in the San Joaquin Valley highlight the dominance of dairy operations for methane emissions. The petroleum operations source profile was developed using the composition of non-methane hydrocarbons in unrefined natural gas associated with crude oil. The observed source profile is consistent with fugitive emissions of condensate during storage or processing of associated gas following extraction and methane separation. Aircraft observations of concentration hotspots near oil wells and dairies are consistent with the statistical source footprint determined via our FLEXPART-WRF-based modeling method and ground-based data. We quantitatively compared our observations at Bakersfield to the California Air Resources Board emission inventory and find consistency for relative emission rates of reactive organic gases between the aforementioned sources and motor vehicles in the region. We estimate that petroleum and dairy operations each comprised 22% of anthropogenic non-methane organic carbon at Bakersfield and were each responsible for 8-13% of potential precursors to ozone. Yet, their direct impacts as potential secondary organic aerosol (SOA) precursors were estimated to be minor for the source profiles observed in the San Joaquin Valley

Vertical Field Effect Transistor based on Graphene-WS2 Heterostructures for flexible and transparent electronics

The celebrated electronic properties of graphene have opened way for materials just one-atom-thick to be used in the post-silicon electronic era. An important milestone was the creation of heterostructures based on graphene and other two-dimensional (2D) crystals, which can be assembled in 3D stacks with atomic layer precision. These layered structures have already led to a range of fascinating physical phenomena, and also have been used in demonstrating a prototype field effect tunnelling transistor - a candidate for post-CMOS technology. The range of possible materials which could be incorporated into such stacks is very large. Indeed, there are many other materials where layers are linked by weak van der Waals forces, which can be exfoliated and combined together to create novel highly-tailored heterostructures. Here we describe a new generation of field effect vertical tunnelling transistors where 2D tungsten disulphide serves as an atomically thin barrier between two layers of either mechanically exfoliated or CVD-grown graphene. Our devices have unprecedented current modulation exceeding one million at room temperature and can also operate on transparent and flexible substrates

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made both neurobiologically more plausible and computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, for example, fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics