Dispersal Dynamics in a Wind-Driven Benthic System

Bedload and water column traps were used with simultaneous wind and water velocity measurements to study postlarval macrofaunal dispersal dynamics in Manukau Harbour, New Zealand. A 12-fold range in mean wind condition resulted in large differences in water flow (12-fold), sediment flux (285-fold), and trap collection of total number of individuals (95-fold), number of the dominant infaunal organism (84-fold for the bivalve Macomona liliana), and number of species (4-fold). There were very strong, positive relationships among wind condition, water velocity, sediment flux, and postlarval dispersal, especially in the bedload. Local density in the ambient sediment was not a good predictor of dispersal. Results indicate that postlarval dispersal may influence benthic abundance pat- terns over a range of spatial scales

Deep machine learning provides state-of-the art performance in image-based plant phenotyping

Deep learning is an emerging field that promises unparalleled results on many data analysis problems. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping, and demonstrate state-of-the-art results for root and shoot feature identification and localisation. We predict a paradigm shift in image-based phenotyping thanks to deep learning approaches

The construction of marketing measures: the case of viewability

This study seeks to develop a critical understanding of marketing measures. Marketing measures inform a variety of marketing practices and have been subject to ethical, discursive and epistemological critique. Informed by a range of theoretical work, this study sheds light on the construction of a key marketing measure in digital advertising: viewability. It shows how a range of competing interests can be mobilized and aligned; how an object of interest can be stabilized; and how standards for measurement can be reconciled. Across this account, we can see how issues of accuracy, ideology and ethics are bracketed off as participants agree on which things matter and which things count

Visual tracking for the recovery of multiple interacting plant root systems from X-ray μCT images

We propose a visual object tracking framework for the extraction of multiple interacting plant root systems from three-dimensional X-ray micro computed tomography images of plants grown in soil. Our method is based on a level set framework guided by a greyscale intensity distribution model to identify object boundaries in image cross-sections. Root objects are followed through the data volume, while updating the tracker's appearance models to adapt to changing intensity values. In the presence of multiple root systems, multiple trackers can be used, but need to distinguish target objects from one another in order to correctly associate roots with their originating plants. Since root objects are expected to exhibit similar greyscale intensity distributions, shape information is used to constrain the evolving level set interfaces in order to lock trackers to their correct targets. The proposed method is tested on root systems of wheat plants grown in soil

AutoRoot: open-source software employing a novel image analysis approach to support fully-automated plant phenotyping

Background: Computer-based phenotyping of plants has risen in importance in recent years. Whilst much software has been written to aid phenotyping using image analysis, to date the vast majority has been only semi-automatic. However, such interaction is not desirable in high throughput approaches. Here, we present a system designed to analyse plant images in a completely automated manner, allowing genuine high throughput measurement of root traits. To do this we introduce a new set of proxy traits. Results: We test the system on a new, automated image capture system, the Microphenotron, which is able to image many 1000s of roots/h. A simple experiment is presented, treating the plants with differing chemical conditions to produce different phenotypes. The automated imaging setup and the new software tool was used to measure proxy traits in each well. A correlation matrix was calculated across automated and manual measures, as a validation. Some particular proxy measures are very highly correlated with the manual measures (e.g. proxy length to manual length, r2 > 0.9). This suggests that while the automated measures are not directly equivalent to classic manual measures, they can be used to indicate phenotypic differences (hence the term, proxy). In addition, the raw discriminative power of the new proxy traits was examined. Principal component analysis was calculated across all proxy measures over two phenotypically-different groups of plants. Many of the proxy traits can be used to separate the data in the two conditions. Conclusion: The new proxy traits proposed tend to correlate well with equivalent manual measures, where these exist. Additionally, the new measures display strong discriminative power. It is suggested that for particular phenotypic differences, different traits will be relevant, and not all will have meaningful manual equivalent measures. However, approaches such as PCA can be used to interrogate the resulting data to identify differences between datasets. Select images can then be carefully manually inspected if the nature of the precise differences is required. We suggest such flexible measurement approaches are necessary for fully automated, high throughput systems such as the Microphenotron

Suicide among Arab-Americans

BACKGROUND: Arab-American (AA) populations in the US are exposed to discrimination and acculturative stress-two factors that have been associated with higher suicide risk. However, prior work suggests that socially oriented norms and behaviors, which characterize recent immigrant ethnic groups, may be protective against suicide risk. Here we explored suicide rates and their determinants among AAs in Michigan, the state with the largest proportion of AAs in the US. METHODOLOGY/PRINCIPAL FINDINGS: ICD-9/10 underlying cause of death codes were used to identify suicide deaths from among all deaths in Michigan between 1990 and 2007. Data from the 2000 U.S. Census were collected for population denominators. Age-adjusted suicide rates among AAs and non-ethnic whites were calculated by gender using the direct method of standardization. We also stratified by residence inside or outside of Wayne County (WC), the county with the largest AA population in the state. Suicide rates were 25.10 per 100,000 per year among men and 6.40 per 100,000 per year among women in Michigan from 1990 to 2007. AA men had a 51% lower suicide rate and AA women had a 33% lower rate than non-ethnic white men and women, respectively. The suicide rate among AA men in WC was 29% lower than in all other counties, while the rate among AA women in WC was 20% lower than in all other counties. Among non-ethnic whites, the suicide rate in WC was higher compared to all other counties among both men (12%) and women (16%). CONCLUSIONS/SIGNIFICANCE: Suicide rates were higher among non-ethnic white men and women compared to AA men and women in both contexts. Arab ethnicity may protect against suicide in both sexes, but more so among men. Additionally, ethnic density may protect against suicide among Arab-Americans

Teaching Implicit Leadership Theories to develop leaders and leadership – How and why it can make a difference

Implicit leadership theories (ILTs) are lay images of leadership, which are individually and socially determined. We discuss how teaching ILTs contributes to developing leaders and leaderships by raising self- and social awareness for the contexts in which leadership takes place. We present and discuss a drawing exercise to illustrate different ILTs and discuss the implications for leaders and leadership, with a particular focus on how leaders claim, and are granted, leader identities in groups