115 research outputs found
An Ammonia Spectral Atlas of Dense Cores in Perseus
We present ammonia observations of 193 dense cores and core candidates in the
Perseus molecular cloud made using the Robert F. Byrd Green Bank Telescope. We
simultaneously observed the NH3(1,1), NH3(2,2), CCS (2_1 -> 1_0) and CC34S (2_1
-> 1_0) transitions near 23 GHz for each of the targets with a spectral
resolution of dv ~ 0.024 km/s. We find ammonia emission associated with nearly
all of the (sub)millimeter sources as well as at several positions with no
associated continuum emission. For each detection, we have measured physical
properties by fitting a simple model to every spectral line simultaneously.
Where appropriate, we have refined the model by accounting for low optical
depths, multiple components along the line of sight and imperfect coupling to
the GBT beam. For the cores in Perseus, we find a typical kinetic temperature
of T=11 K, a typical column density of N(NH3)~ 10^14.5 /cm^2 and velocity
dispersions ranging from sigma_v = 0.07 km/s to 0.7 km/s. However, many cores
with velocity dispersions > 0.2 km/s show evidence for multiple velocity
components along the line of sight.Comment: 19 pages; Accepted to ApJS; version with high resolution figures
available at http://www.cfa.harvard.edu/COMPLETE/papers/nh3-paper1.pdf ;
online data at
http://www.cfa.harvard.edu/COMPLETE/data_html_pages/GBT_NH3.htm
Revisited experimental comparison of node-link and matrix representations
Visualizing network data is applicable in domains such as biology, engineering, and social sciences. We report the results of a study comparing the effectiveness of the two primary techniques for showing network data: node-link diagrams and adjacency matrices. Specifically, an evaluation with a large number of online participants revealed statistically significant differences between the two visualizations. Our work adds to existing research in several ways. First, we explore a broad spectrum of network tasks, many of which had not been previously evaluated. Second, our study uses a large dataset, typical of many real-life networks not explored by previous studies. Third, we leverage crowdsourcing to evaluate many tasks with many participants
The Bones of the Milky Way
The very long, thin infrared dark cloud "Nessie" is even longer than had been previously claimed, and an analysis of its Galactic location suggests that it lies directly in the Milky Way’s mid-plane, tracing out a highly elongated bone-like feature within the prominent Scutum-Centaurus spiral arm. Re-analysis of mid-infrared imagery from the Spitzer Space Telescope shows that this IRDC is at least 2, and possibly as many as 8 times longer than had originally been claimed by Nessie’s discoverers, Jackson et al. (2010); its aspect ratio is therefore at least 150:1, and possibly as large as 800:1. A careful accounting for both the Sun’s offset from the Galactic plane (∼25 pc) and the Galactic center’s offset from the ()=(0,0) position defined by the IAU in 1959 shows that the latitude of the true Galactic mid-plane at the 3.1 kpc distance to the Scutum-Centaurus Arm is not b=0, but instead closer to b=−0.5, which is the latitude of Nessie to within a few pc. Apparently, Nessie lies in the Galactic mid-plane. An analysis of the radial velocities of low-density (CO) and high-density () gas associated with the Nessie dust feature suggests that Nessie runs along the Scutum-Centaurus Arm in position-position-velocity space, which means it likely forms a dense ‘spine’ of the arm in real space as well. No galaxy-scale simulation to date has the spatial resolution to predict a Nessie-like feature, but extant simulations do suggest that highly elongated over-dense filaments should be associated with a galaxy’s spiral arms. Nessie is situated in the closest major spiral arm to the Sun toward the inner Galaxy, and appears almost perpendicular to our line of sight, making it the easiest feature of its kind to detect from our location (a shadow of an Arm’s bone, illuminated by the Galaxy beyond). Although the Sun’s (∼25 pc) offset from the Galactic plane is not large in comparison with the half-thickness of the plane as traced by Population I objects such as GMCs and HII regions (∼200 pc; Rix et al. (2013)), it may be significant compared with an extremely thin layer that might be traced out by Nessie-like ”bones“ of the Milky Way. Future high-resolution extinction and molecular line data may therefore allow us to exploit the Sun’s position above the plane to gain a (very foreshortened) view "from above” of dense gas in Milky Way’s disk and its structure.Astronom
Interspecific variation and elevated CO2 influence the relationship between plant chemical resistance and regrowth tolerance
To understand how comprehensive plant defense phenotypes will respond to global change, we investigated the legacy effects of elevated CO2 on the relationships between chemical resistance (constitutive and induced via mechanical damage) and regrowth tolerance in four milkweed species (Asclepias). We quantified potential resistance and tolerance trade‐offs at the physiological level following simulated mowing, which are relevant to milkweed ecology and conservation. We examined the legacy effects of elevated CO2 on four hypothesized trade‐offs between the following: (a) plant growth rate and constitutive chemical resistance (foliar cardenolide concentrations), (b) plant growth rate and mechanically induced chemical resistance, (c) constitutive resistance and regrowth tolerance, and (d) regrowth tolerance and mechanically induced resistance. We observed support for one trade‐off between plant regrowth tolerance and mechanically induced resistance traits that was, surprisingly, independent of CO2 exposure. Across milkweed species, mechanically induced resistance increased by 28% in those plants previously exposed to elevated CO2. In contrast, constitutive resistance and the diversity of mechanically induced chemical resistance traits declined in response to elevated CO2 in two out of four milkweed species. Finally, previous exposure to elevated CO2 uncoupled the positive relationship between plant growth rate and regrowth tolerance following damage. Our data highlight the complex and dynamic nature of plant defense phenotypes under environmental change and question the generality of physiologically based defense trade‐offs.To understand how comprehensive plant defense phenotypes will respond to global change, we investigated the legacy effects of elevated CO2 on the relationships between chemical resistance and regrowth tolerance in four milkweed species (Asclepias). We found interspecific variation among milkweed species influenced the relationship between mechanically induced chemical resistance and regrowth tolerance. Previous exposure to elevated CO2 increased mechanically induced resistance by 28% and uncoupled the positive relationship between plant growth rate and regrowth tolerance following damage.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155974/1/ece36284_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155974/2/ece36284.pd
Sympatric woodland Myotis bats form tight-knit social groups with exclusive roost home ranges
Background: The structuring of wild animal populations can influence population dynamics, disease spread, and information transfer. Social network analysis potentially offers insights into these processes but is rarely, if ever, used to investigate more than one species in a community. We therefore compared the social, temporal and spatial networks of sympatric Myotis bats (M. nattereri (Natterer's bats) and M. daubentonii (Daubenton's bats)), and asked: (1) are there long-lasting social associations within species? (2) do the ranges occupied by roosting social groups overlap within or between species? (3) are M. daubentonii bachelor colonies excluded from roosting in areas used by maternity groups?
Results: Using data on 490 ringed M. nattereri and 978 M. daubentonii from 379 colonies, we found that both species formed stable social groups encompassing multiple colonies. M. nattereri formed 11 mixed-sex social groups with few (4.3%) inter-group associations. Approximately half of all M. nattereri were associated with the same individuals when recaptured, with many associations being long-term (>100 days). In contrast, M. daubentonii were sexually segregated; only a quarter of pairs were associated at recapture after a few days, and inter-sex associations were not long-lasting. Social groups of M. nattereri and female M. daubentonii had small roost home ranges (mean 0.2 km2 in each case). Intra-specific overlap was low, but inter-specific overlap was high, suggesting territoriality within but not between species. M. daubentonii bachelor colonies did not appear to be excluded from roosting areas used by females.
Conclusions: Our data suggest marked species- and sex-specific patterns of disease and information transmission are likely between bats of the same genus despite sharing a common habitat. The clear partitioning of the woodland amongst social groups, and their apparent reliance on small patches of habitat for roosting, means that localised woodland management may be more important to bat conservation than previously recognised
The Feeling of Numbers: emotions in everyday engagements with data and their visualisation
This paper highlights the role that emotions play in engagements with data and their visualisation. To date, the relationship between data and emotions has rarely been noted, in part because data studies have not attended to everyday engagements with data. We draw on an empirical study to show a wide range of emotional engagements with diverse aspects of data and their visualisation, and so demonstrate the importance of emotions as vital components of making sense of data. We nuance the argument that regimes of datafication, in which numbers, metrics and statistics dominate, are characterised by a renewed faith in objectivity and rationality, arguing that in datafied times, it is not only numbers but also the feeling of numbers that is important. We build on the sociology of a) emotions and b) the everyday to do this, and in so doing, we contribute to the development of a sociology of data
Measures in Visualization Space
Postponed access: the file will be available after 2021-08-12Measurement is an integral part of modern science, providing the fundamental means for evaluation, comparison, and prediction. In the context of visualization, several different types of measures have been proposed, ranging from approaches that evaluate particular aspects of visualization techniques, their perceptual characteristics, and even economic factors. Furthermore, there are approaches that attempt to provide means for measuring general properties of the visualization process as a whole. Measures can be quantitative or qualitative, and one of the primary goals is to provide objective means for reasoning about visualizations and their effectiveness. As such, they play a central role in the development of scientific theories for visualization. In this chapter, we provide an overview of the current state of the art, survey and classify different types of visualization measures, characterize their strengths and drawbacks, and provide an outline of open challenges for future research.acceptedVersio
NetMets: software for quantifying and visualizing errors in biological network segmentation
One of the major goals in biomedical image processing is accurate segmentation of networks embedded in volumetric data sets. Biological networks are composed of a meshwork of thin filaments that span large volumes of tissue. Examples of these structures include neurons and microvasculature, which can take the form of both hierarchical trees and fully connected networks, depending on the imaging modality and resolution. Network function depends on both the geometric structure and connectivity. Therefore, there is considerable demand for algorithms that segment biological networks embedded in three-dimensional data. While a large number of tracking and segmentation algorithms have been published, most of these do not generalize well across data sets. One of the major reasons for the lack of general-purpose algorithms is the limited availability of metrics that can be used to quantitatively compare their effectiveness against a pre-constructed ground-truth. In this paper, we propose a robust metric for measuring and visualizing the differences between network models. Our algorithm takes into account both geometry and connectivity to measure network similarity. These metrics are then mapped back onto an explicit model for visualization
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