21 research outputs found
Observations of a rotating pyroconvective plume
Background: There is an ongoing need for improved understanding of wildfire plume dynamics. Aims: To improve process-level understanding of wildfire plume dynamics including strong (\u3e10 m s-1) fire-generated winds and pyrocumulus (pyroCu) development. Methods: Ka-band Doppler radar and two Doppler lidars were used to quantify plume dynamics during a high-intensity prescribed fire and airborne laser scanning (ALS) to quantify the fuel consumption. Key results: We document the development of a strongly rotating (\u3e10 m s-1) pyroCu-topped plume reaching 10 km. Plume rotation develops during merging of discrete plume elements and is characterised by inflow and rotational winds an order of magnitude stronger than the ambient flow. Deep pyroCu is initiated after a sequence of plume-deepening events that push the plume top above its condensation level. The pyroCu exhibits a strong central updraft (35 m s-1) flanked by mechanically and evaporative forced downdrafts. The downdrafts do not reach the surface and have no impact on fire behaviour. ALS data show plume development is linked to large fuel consumption (20 kg m-2). Conclusions: Interactions between discrete plume elements contributed to plume rotation and large fuel consumption led to strong updrafts triggering deep pyroCu. Implications: These results identify conditions conducive to strong plume rotation and deep pyroCu initiation
Lynx Mission Concept Status
Lynx is a concept under study for prioritization in the 2020 Astrophysics Decadal Survey. Providing orders of magnitude increase in sensitivity over Chandra, Lynx will examine the first black holes and their galaxies, map the large-scale structure and galactic halos, and shed new light on the environments of young stars and their planetary systems. In order to meet the Lynx science goals, the telescope consists of a high-angular resolution optical assembly complemented by an instrument suite that may include a High Definition X-ray Imager, X-ray Microcalorimeter and an X-ray Grating Spectrometer. The telescope is integrated onto the spacecraft to form a comprehensive observatory concept. Progress on the formulation of the Lynx telescope and observatory configuration is reported in this paper
Schizophrenia-associated somatic copy-number variants from 12,834 cases reveal recurrent NRXN1 and ABCB11 disruptions
While germline copy-number variants (CNVs) contribute to schizophrenia (SCZ) risk, the contribution of somatic CNVs (sCNVs)—present in some but not all cells—remains unknown. We identified sCNVs using blood-derived genotype arrays from 12,834 SCZ cases and 11,648 controls, filtering sCNVs at loci recurrently mutated in clonal blood disorders. Likely early-developmental sCNVs were more common in cases (0.91%) than controls (0.51%, p = 2.68e−4), with recurrent somatic deletions of exons 1–5 of the NRXN1 gene in five SCZ cases. Hi-C maps revealed ectopic, allele-specific loops forming between a potential cryptic promoter and non-coding cis-regulatory elements upon 5′ deletions in NRXN1. We also observed recurrent intragenic deletions of ABCB11, encoding a transporter implicated in anti-psychotic response, in five treatment-resistant SCZ cases and showed that ABCB11 is specifically enriched in neurons forming mesocortical and mesolimbic dopaminergic projections. Our results indicate potential roles of sCNVs in SCZ risk
25th annual computational neuroscience meeting: CNS-2016
The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong
Regional Modeling of Forest Fuels and Structural Attributes Using Airborne Laser Scanning Data in Oregon
Airborne laser scanning (ALS) acquisitions provide piecemeal coverage across the western US, as collections are organized by local managers of individual project areas. In this study, we analyze different factors that can contribute to developing a regional strategy to use information from completed ALS data acquisitions and develop maps of multiple forest attributes in new ALS project areas in a rapid manner. This study is located in Oregon, USA, and analyzes six forest structural attributes for differences between: (1) synthetic (i.e., not-calibrated), and calibrated predictions, (2) parametric linear and semiparametric models, and (3) models developed with predictors computed for point clouds enclosed in the areas where field measurements were taken, i.e., “point-cloud predictors”, and models developed using predictors extracted from pre-rasterized layers, i.e., “rasterized predictors”. Forest structural attributes under consideration are aboveground biomass, downed woody biomass, canopy bulk density, canopy height, canopy base height, and canopy fuel load. Results from our study indicate that semiparametric models perform better than parametric models if no calibration is performed. However, the effect of the calibration is substantial in reducing the bias of parametric models but minimal for the semiparametric models and, once calibrations are performed, differences between parametric and semiparametric models become negligible for all responses. In addition, minimal differences between models using point-cloud predictors and models using rasterized predictors were found. We conclude that the approach that applies semiparametric models and rasterized predictors, which represents the easiest workflow and leads to the most rapid results, is justified with little loss in accuracy or precision even if no calibration is performed
Evaluating the Mid-Infrared Bi-spectral Index for improved assessment of low-severity fire effects in a conifer forest
A Comparison of Multitemporal Airborne Laser Scanning Data and the Fuel Characteristics Classification System for Estimating Fuel Load and Consumption
Characterizing pre-fire fuel load and fuel consumption are critical for assessing fire behavior, fire effects, and smoke emissions. Two approaches for quantifying fuel load are airborne laser scanning (ALS) and the Fuel Characteristic Classification System (FCCS). The implementation of multitemporal ALS (i.e., the use of two or more ALS datasets across time at a given location) in conjunction with empirical models trained with field data can be used to measure fuel and estimate fuel consumption from a fire. FCCS, adapted for use in LANDFIRE (LF), provides 30 m resolution estimates of fuel load across the contiguous United States and can be used to estimate fuel consumption through software programs such as Fuel and Fire Tools (FFT). This study compares the two approaches for two wildfires in the northwestern United States having predominantly sagebrush steppe and ponderosa pine savanna ecosystems. The results showed that the LF FCCS approach yielded higher pre-fire fuel loads and fuel consumption than the ALS approach and that the coarser scale LF FCCS data did not capture as much heterogeneity as the ALS data. At Tepee, 50.0% of the difference in fuel load and 87.3% of the difference in fuel consumption were associated with distinguishing sparse trees from rangeland. At Keithly, this only accounted for 8.2% and 8.6% of the differences, demonstrating the significance of capturing heterogeneity in rangeland vegetation structure and fire effects. Our results suggest future opportunities to use ALS data to better parametrize fine-scale fuel load variability that LF FCCS does not capture
Bacterial Emission Factors: A Foundation for the Terrestrial-Atmospheric Modeling of Bacteria Aerosolized by Wildland Fires
Wildland fire is a major global driver in the exchange of aerosols between terrestrial environments and the atmosphere. This exchange is commonly quantified using emission factors or the mass of a pollutant emitted per mass of fuel burned. However, emission factors for microbes aerosolized by fire have yet to be determined. Using bacterial cell concentrations collected on unmanned aircraft systems over forest fires in Utah, USA, we determine bacterial emission factors (BEFs) for the first time. We estimate that 1.39 × 1010 and 7.68 × 1011 microbes are emitted for each Mg of biomass consumed in fires burning thinning residues and intact forests, respectively. These emissions exceed estimates of background bacterial emissions in other studies by 3–4 orders of magnitude. For the ~2631 ha of similar forests in the Fishlake National Forest that burn each year on average, an estimated 1.35 × 1017 cells or 8.1 kg of bacterial biomass were emitted. BEFs were then used to parametrize a computationally scalable particle transport model that predicted over 99% of the emitted cells were transported beyond the 17.25 x 17.25 km model domain. BEFs can be used to expand understanding of global wildfire microbial emissions and their potential consequences to ecosystems, the atmosphere, and humans
Bacterial Emission Factors: A Foundation for the Terrestrial-Atmospheric Modeling of Bacteria Aerosolized by Wildland Fires
Wildland fire is a major global driver in the exchange
of aerosols
between terrestrial environments and the atmosphere. This exchange
is commonly quantified using emission factors or the mass of a pollutant
emitted per mass of fuel burned. However, emission factors for microbes
aerosolized by fire have yet to be determined. Using bacterial cell
concentrations collected on unmanned aircraft systems over forest
fires in Utah, USA, we determine bacterial emission factors (BEFs)
for the first time. We estimate that 1.39 × 1010 and
7.68 × 1011 microbes are emitted for each Mg of biomass
consumed in fires burning thinning residues and intact forests, respectively.
These emissions exceed estimates of background bacterial emissions
in other studies by 3–4 orders of magnitude. For the ∼2631
ha of similar forests in the Fishlake National Forest that burn each
year on average, an estimated 1.35 × 1017 cells or
8.1 kg of bacterial biomass were emitted. BEFs were then used to parametrize
a computationally scalable particle transport model that predicted
over 99% of the emitted cells were transported beyond the 17.25 x
17.25 km model domain. BEFs can be used to expand understanding of
global wildfire microbial emissions and their potential consequences
to ecosystems, the atmosphere, and humans