Combining Data Sources to Understand Drivers of Spotted Salamander (Ambystoma maculatum) Population Abundance

2018 ◽  
Vol 52 (2) ◽  
pp. 116-126 ◽  
Author(s):  
Courtney L. Davis ◽  
Eric W. Teitsworth ◽  
David A. W. Miller
Keyword(s):  
Ambystoma Maculatum ◽  
Data Sources ◽  
Population Abundance ◽  
Spotted Salamander ◽  
Combining Data

scholarly journals A computationally tractable birth-death model that combines phylogenetic and epidemiological data

2020 ◽  
Author(s):  
Alexander E. Zarebski ◽  
Louis du Plessis ◽  
Kris V. Parag ◽  
Oliver G. Pybus
Keyword(s):  
Transmission Rate ◽  
Epidemiological Data ◽  
Heterogeneous Data ◽  
Pathogen Transmission ◽  
Data Sources ◽  
Primary Data ◽  
Analytic Approximation ◽  
Data Set ◽  
Combining Data ◽  
Birth Death

Inferring the dynamics of pathogen transmission during an outbreak is an important problem in both infectious disease epidemiology and phylodynamics. In mathematical epidemiology, estimates are often informed by time-series of infected cases while in phylodynamics genetic sequences sampled through time are the primary data source. Each data type provides different, and potentially complementary, insights into transmission. However inference methods are typically highly specialised and field-specific. Recent studies have recognised the benefits of combining data sources, which include improved estimates of the transmission rate and number of infected individuals. However, the methods they employ are either computationally prohibitive or require intensive simulation, limiting their real-time utility. We present a novel birth-death phylogenetic model, called TimTam which can be informed by both phylogenetic and epidemiological data. Moreover, we derive a tractable analytic approximation of the TimTam likelihood, the computational complexity of which is linear in the size of the data set. Using the TimTam we show how key parameters of transmission dynamics and the number of unreported infections can be estimated accurately using these heterogeneous data sources. The approximate likelihood facilitates inference on large data sets, an important consideration as such data become increasingly common due to improving sequencing capability.

scholarly journals URBAN SCENE CLASSIFICATION USING FEATURES EXTRACTED FROM PHOTOGRAMMETRIC POINT CLOUDS ACQUIRED BY UAV

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 511-518 ◽  
Author(s):  
G. G. Pessoa ◽  
R. C. Santos ◽  
A. C. Carrilho ◽  
M. Galo ◽  
A. Amorim
Keyword(s):  
Point Clouds ◽  
Bare Soil ◽  
Urban Environments ◽  
Data Sources ◽  
Classification Error ◽  
Error Matrix ◽  
Urban Scenes ◽  
Optical Images ◽  
Combining Data ◽  
Quantitative Analyses

<p><strong>Abstract.</strong> Images and LiDAR point clouds are the two major data sources used by the photogrammetry and remote sensing community. Although different, the synergy between these two data sources has motivated exploration of the potential for combining data in various applications, especially for classification and extraction of information in urban environments. Despite the efforts of the scientific community, integrating LiDAR data and images remains a challenging task. For this reason, the development of Unmanned Aerial Vehicles (UAVs) along with the integration and synchronization of positioning receivers, inertial systems and off-the-shelf imaging sensors has enabled the exploitation of the high-density photogrammetric point cloud (PPC) as an alternative, obviating the need to integrate LiDAR and optical images. This study therefore aims to compare the results of PPC classification in urban scenes considering radiometric-only, geometric-only and combined radiometric and geometric data applied to the Random Forest algorithm. For this study the following classes were considered: buildings, asphalt, trees, grass, bare soil, sidewalks and power lines, which encompass the most common objects in urban scenes. The classification procedure was performed considering radiometric features (Green band, Red band, NIR band, NDVI and Saturation) and geometric features (Height – nDSM, Linearity, Planarity, Scatter, Anisotropy, Omnivariance and Eigenentropy). The quantitative analyses were performed by means of the classification error matrix using the following metrics: overall accuracy, recall and precision. The quantitative analyses present overall accuracy of 0.80, 0.74 and 0.98 for classification considering radiometric, geometric and both data combined, respectively.</p>

Discussion of “Combining Health Data Uses to Ignite Health System Learning”

2015 ◽  
Vol 54 (06) ◽  
pp. 488-499 ◽  
Author(s):  
S. Denaxas ◽  
C. P. Friedman ◽  
A. Geissbuhler ◽  
H. Hemingway ◽  
D. Kalra ◽  
...  
Keyword(s):  
Health System ◽  
Health Data ◽  
Data Sources ◽  
Letters To The Editor ◽  
Discussion Section ◽  
International Group ◽  
Combining Data

SummaryThis article is part of a For-Discussion-Section of Methods of Information in Medicine about the paper “Combining Health Data Uses to Ignite Health System Learning” written by John D. Ainsworth and Iain E. Buchan [1]. It is introduced by an editorial. This article contains the combined commentaries invited to independently comment on the paper of Ainsworth and Buchan. In subsequent issues the discussion can continue through letters to the editor.With these comments on the paper “Combining Health Data Uses to Ignite Health System Learning”, written by John D. Ainsworth and Iain E. Buchan [1], the journal seeks to stimulate a broad discussion on new ways for combining data sources for the reuse of health data in order to identify new opportunities for health system learning. An international group of experts has been invited by the editor of Methods to comment on this paper. Each of the invited commentaries forms one section of this paper.

Combining Data Sources to Monitor the HIV Epidemic in Canada

2003 ◽  
Vol 32 ◽  
pp. S24-S32 ◽  
Author(s):  
Chris P. Archibald ◽  
Jason Sutherland ◽  
Jennifer Geduld ◽  
Donald Sutherland ◽  
Ping Yan
Keyword(s):  
Data Sources ◽  
Hiv Epidemic ◽  
Combining Data

scholarly journals A Long-term Demographic Study of a Spotted Salamander (Ambystoma maculatum) Population in Central Ohio

Herpetologica ◽  
2018 ◽  
Vol 74 (2) ◽  
pp. 109-116 ◽  
Author(s):  
Rebecca N. Homan ◽  
Meredith A. Holgerson ◽  
Lindsay M. Biga
Keyword(s):  
Ambystoma Maculatum ◽  
Spotted Salamander ◽  
Demographic Study

Predation by the Caddisfly Banksiola dossuaria on Egg Masses of the Spotted Salamander Ambystoma maculatum

1992 ◽  
Vol 127 (2) ◽  
pp. 368 ◽  
Author(s):  
Ben M. Stout III ◽  
Kathy K. Stout ◽  
Craig W. Stihler
Keyword(s):  
Ambystoma Maculatum ◽  
Spotted Salamander ◽  
Egg Masses
Sign in / Sign up

Export Citation Format

Share Document