scholarly journals Segmenting biological specimens from photos to understand the evolution of UV plumage in passerine birds

2021 ◽  
Author(s):  
Yichen He ◽  
Christopher R Cooney ◽  
Zoe K Varley ◽  
Lara O Nouri ◽  
Christopher J. A. Moody ◽  
...  
Keyword(s):  
Deep Learning ◽  
Bird Species ◽  
Whole Body ◽  
Accurate Information ◽  
Passerine Birds ◽  
Plumage Colouration ◽  
Segmentation Methods ◽  
Uv Reflectance ◽  
Role Of Light

Ultraviolet (UV) colouration is thought to be an important signalling mechanism in many bird species, yet broad insights regarding the prevalence of UV plumage colouration and the factors promoting its evolution are currently lacking. Here, we develop a novel image segmentation pipeline based on deep learning that considerably outperforms classical (i.e. non-deep learning) segmentation methods, and use this to extract accurate information on whole-body plumage colouration from photographs of >24,000 museum specimens covering >4,500 species of passerine birds. Our results demonstrate that UV reflectance, particularly as a component of other colours, is widespread across the passerine radiation but is strongly phylogenetically conserved. We also find clear evidence in support of the role of light environment in promoting the evolution of UV plumage colouration, and a weak trend towards higher UV plumage reflectance among bird species with ultraviolet rather than violet-sensitive visual systems. Overall, our study provides important broad-scale insight into an enigmatic component of avian colouration, as well as demonstrating that deep learning has considerable promise for allowing new data to be bought to bare on long-standing questions in ecology and evolution.

scholarly journals Differential Role of Passerine Birds in Distribution of Borrelia Spirochetes, Based on Data from Ticks Collected from Birds during the Postbreeding Migration Period in Central Europe

2008 ◽  
Vol 75 (3) ◽  
pp. 596-602 ◽  
Author(s):  
Lenka Dubska ◽  
Ivan Literak ◽  
Elena Kocianova ◽  
Veronika Taragelova ◽  
Oldrich Sychra
Keyword(s):  
Czech Republic ◽  
Urban Areas ◽  
Bird Species ◽  
Parus Major ◽  
Passerine Bird ◽  
Sensu Stricto ◽  
Passerine Birds ◽  
Borrelia Garinii ◽  
The Czech Republic

ABSTRACT Borrelia spirochetes in bird-feeding ticks were studied in the Czech Republic. During the postbreeding period (July to September 2005), 1,080 passerine birds infested by 2,240 Ixodes ricinus subadult ticks were examined. Borrelia garinii was detected in 22.2% of the ticks, Borrelia valaisiana was detected in 12.8% of the ticks, Borrelia afzelii was detected in 1.6% of the ticks, and Borrelia burgdorferi sensu stricto was detected in 0.3% of the ticks. After analysis of infections in which the blood meal volume and the stage of the ticks were considered, we concluded that Eurasian blackbirds (Turdus merula), song thrushes (Turdus philomelos), and great tits (Parus major) are capable of transmitting B. garinii; that juvenile blackbirds and song thrushes are prominent reservoirs for B. garinii spirochetes; that some other passerine birds investigated play minor roles in transmitting B. garinii; and that the presence B. afzelii in ticks results from infection in a former stage. Thus, while B. garinii transmission is associated with only a few passerine bird species, these birds have the potential to distribute millions of Lyme disease spirochetes between urban areas.

280-LB: Role of A1 and A3 Adenosine Receptors in Whole Body Glucose Metabolism

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 280-LB ◽  
Author(s):  
SHANU JAIN ◽  
DILIP K. TOSH ◽  
MARC REITMAN ◽  
KENNETH A. JACOBSON
Keyword(s):  
Glucose Metabolism ◽  
Adenosine Receptors ◽  
Whole Body

Determinants of maximal whole-body fat oxidation in elite cross-country skiers: Role of skeletal muscle mitochondria

2018 ◽  
Vol 28 (12) ◽  
pp. 2494-2504 ◽  
Author(s):  
Sune Dandanell ◽  
Anne-Kristine Meinild-Lundby ◽  
Andreas B. Andersen ◽  
Paul F. Lang ◽  
Laura Oberholzer ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Body Fat ◽  
Fat Oxidation ◽  
Whole Body ◽  
Muscle Mitochondria ◽  
Skeletal Muscle Mitochondria ◽  
Cross Country

scholarly journals Domain randomization-enhanced deep learning models for bird detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Mao ◽  
Jun Kang Chow ◽  
Pin Siang Tan ◽  
Kuan-fu Liu ◽  
Jimmy Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Continuous Monitoring ◽  
Bird Species ◽  
Training Data ◽  
Learning Models ◽  
Fine Grained ◽  
Bird Detection ◽  
Relationship Of ◽  
The Relationship

AbstractAutomatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.

scholarly journals Towards 6G IoT: Tracing Mobile Sensor Nodes with Deep Learning Clustering in UAV Networks

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3936
Author(s):  
Yannis Spyridis ◽  
Thomas Lagkas ◽  
Panagiotis Sarigiannidis ◽  
Vasileios Argyriou ◽  
Antonios Sarigiannidis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Heuristic Method ◽  
Sensor Nodes ◽  
Convolutional Network ◽  
Received Signal Strength Indicator ◽  
Anchor Nodes ◽  
Time Required ◽  
Rf Signal ◽  
Deep Learning Model

Unmanned aerial vehicles (UAVs) in the role of flying anchor nodes have been proposed to assist the localisation of terrestrial Internet of Things (IoT) sensors and provide relay services in the context of the upcoming 6G networks. This paper considered the objective of tracing a mobile IoT device of unknown location, using a group of UAVs that were equipped with received signal strength indicator (RSSI) sensors. The UAVs employed measurements of the target’s radio frequency (RF) signal power to approach the target as quickly as possible. A deep learning model performed clustering in the UAV network at regular intervals, based on a graph convolutional network (GCN) architecture, which utilised information about the RSSI and the UAV positions. The number of clusters was determined dynamically at each instant using a heuristic method, and the partitions were determined by optimising an RSSI loss function. The proposed algorithm retained the clusters that approached the RF source more effectively, removing the rest of the UAVs, which returned to the base. Simulation experiments demonstrated the improvement of this method compared to a previous deterministic approach, in terms of the time required to reach the target and the total distance covered by the UAVs.

scholarly journals Role of Deep Learning in Early Detection of COVID-19: Scoping Review

2021 ◽  
pp. 100025
Author(s):  
Mahmood Alzubaidi ◽  
Haider Dhia Zubaydi ◽  
Ali Bin-Salem ◽  
Alaa A Abd-Alrazaq ◽  
Arfan Ahmed ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Detection ◽  
Scoping Review

Reducing misperceptions through news stories with data visualization: The role of readers’ prior knowledge and prior beliefs

Journalism ◽  
2021 ◽  
pp. 146488492110287
Author(s):  
Paul Mena
Keyword(s):  
Experimental Design ◽  
Prior Knowledge ◽  
Data Visualization ◽  
Accurate Information ◽  
Graphical Representations ◽  
Prior Beliefs ◽  
News Stories ◽  
Issue Involvement

Amid the global discussion on ways to fight misinformation, journalists have been writing stories with graphical representations of data to expose misperceptions and provide readers with more accurate information. Employing an experimental design, this study explored to what extent news stories correcting misperceptions are effective in reducing them when the stories include data visualization and how influential readers’ prior beliefs, issue involvement and prior knowledge may be in that context. The study found that the presence of data visualization in news articles correcting misperceptions significantly enhanced the reduction of misperceptions among news readers with less than average prior knowledge about an issue. In addition, it was found that prior beliefs had a significant effect on news readers’ misperceptions regardless of the presence or absence of data visualization. In this way, this research offers some support for the notion that data visualization may be useful to decrease misperceptions under certain circumstances.

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