Designs of multi-spacecraft swarms for the deflection of apophis by solar sublimation

This paper presents two conceptual designs of multi-spacecraft swarms used for deflecting Apophis. Each spacecraft is equipped with a solar concentrator assembly, which focuses the solar light, and a beaming system that projects a beam of light onto the surface of the asteroid. When the beams from each spacecraft are superimposed, the temperature on the surface is enough to sublimate the rock, creating a debris plume with enough force to slowly alter the orbit of Apophis. An overview of the dynamics, control and navigation strategies are presented along with preliminary system budgets

PhenoLearn: a user-friendly toolkit for image annotation and deep learning-based phenotyping for biological datasets

The digitisation of natural history specimens has unlocked opportunities for large-scale phenotypic trait analysis. In recent years, deep learning has shown significant results in accurately predicting annotations on 2D specimen photographs. However, it can be challenging for biologists without extensive related expertise to easily use deep learning. Here, we introduce PhenoLearn, a toolkit developed for biologists to generate annotations on 2D specimen images using deep learning. PhenoLearn integrates graphical user interfaces (GUIs) within its two main modules, PhenoLabel for image annotation and PhenoTrain for model training and prediction. GUIs increase accessibility and reduce the need for computational expertise, allowing biologists to intuitively go through a workflow of labelling training sets, using deep learning, and reviewing predictions in the same tool. We demonstrate PhenoLearn's capabilities through a case study involving the segmentation of plumage areas on bird images, showcasing prediction accuracy and the running time with and without GPU, highlighting its potential to generate annotations with minimal computational cost and time. The toolkit's modular design and flexibility ensure adaptability, allowing for integration with other tools amidst rapidly evolving deep learning approaches. PhenoLearn bridges the gap between specimen digitisation and downstream analysis, providing biologists with broader access to deep learning. The source code, installation guides, tutorials with screenshots, and a small demo dataset for PhenoLearn can be found at https://github.com/echanhe/phenolearn

Using pose estimation to identify regions and points on natural history specimens

A key challenge in mobilising growing numbers of digitised biological specimens for scientific research is finding high-throughput methods to extract phenotypic measurements on these datasets. In this paper, we test a pose estimation approach based on Deep Learning capable of accurately placing point labels to identify key locations on specimen images. We then apply the approach to two distinct challenges that each requires identification of key features in a 2D image: (i) identifying body region-specific plumage colouration on avian specimens and (ii) measuring morphometric shape variation in Littorina snail shells. For the avian dataset, 95% of images are correctly labelled and colour measurements derived from these predicted points are highly correlated with human-based measurements. For the Littorina dataset, more than 95% of landmarks were accurately placed relative to expert-labelled landmarks and predicted landmarks reliably captured shape variation between two distinct shell ecotypes (‘crab’ vs ‘wave’). Overall, our study shows that pose estimation based on Deep Learning can generate high-quality and high-throughput point-based measurements for digitised image-based biodiversity datasets and could mark a step change in the mobilisation of such data. We also provide general guidelines for using pose estimation methods on large-scale biological datasets

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

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 bear on long-standing questions in ecology and evolution

National Beef Quality Audit–2016: Transportation, mobility, live cattle, and carcass assessments of targeted producer-related characteristics that affect value of market cows and bulls, their carcasses, and associated by-products

The National Beef Quality Audit–2016 marks the fourth iteration in a series assessing the quality of live beef and dairy cows and bulls and their carcass counterparts. The objective was to determine the incidence of producer-related defects, and report cattle and carcass traits associated with producer management. Conducted from March through December of 2016, trailers (n = 154), live animals (n = 5,470), hide-on carcasses (n = 5,278), and hide-off hot carcasses (n = 5,510) were surveyed in 18 commercial packing facilities throughout the United States. Cattle were allowed 2.3 m2 of trailer space on average during transit indicating some haulers are adhering to industry handling guidelines for trailer space requirements. Of the mixed gender loads arriving at processing facilities, cows and bulls were not segregated on 64.4% of the trailers surveyed. When assessed for mobility, the greatest majority of cattle surveyed were sound. Since the inception of the quality audit series, beef cows have shown substantial improvements in muscle. Today over 90.0% of dairy cows are too light muscled. The mean body condition score for beef animals was 4.7 and for dairy cows and bulls was 2.6 and 3.3, respectively. Dairy cattle were lighter muscled, yet fatter than the dairy cattle surveyed in 2007. Of cattle surveyed, most did not have horns, nor any visible live animal defects. Unbranded hides were observed on 77.3% of cattle. Carcass bruising was seen on 64.1% of cow carcasses and 42.9% of bull carcasses. However, over half of all bruises were identified to only be minor in severity. Nearly all cattle (98.4%) were free of visible injection-site lesions. Current results suggest improvements have been made in cattle and meat quality in the cow and bull sector. Furthermore, the results provide guidance for continued educational and research efforts for improving market cow and bull beef quality

National Beef Quality Audit–2016: Transportation, mobility, live cattle, and carcass assessments of targeted producer-related characteristics that affect value of market cows and bulls, their carcasses, and associated by-products

The National Beef Quality Audit–2016 marks the fourth iteration in a series assessing the quality of live beef and dairy cows and bulls and their carcass counterparts. The objective was to determine the incidence of producer-related defects, and report cattle and carcass traits associated with producer management. Conducted from March through December of 2016, trailers (n = 154), live animals (n = 5,470), hide-on carcasses (n = 5,278), and hide-off hot carcasses (n = 5,510) were surveyed in 18 commercial packing facilities throughout the United States. Cattle were allowed 2.3 m2 of trailer space on average during transit indicating some haulers are adhering to industry handling guidelines for trailer space requirements. Of the mixed gender loads arriving at processing facilities, cows and bulls were not segregated on 64.4% of the trailers surveyed. When assessed for mobility, the greatest majority of cattle surveyed were sound. Since the inception of the quality audit series, beef cows have shown substantial improvements in muscle. Today over 90.0% of dairy cows are too light muscled. The mean body condition score for beef animals was 4.7 and for dairy cows and bulls was 2.6 and 3.3, respectively. Dairy cattle were lighter muscled, yet fatter than the dairy cattle surveyed in 2007. Of cattle surveyed, most did not have horns, nor any visible live animal defects. Unbranded hides were observed on 77.3% of cattle. Carcass bruising was seen on 64.1% of cow carcasses and 42.9% of bull carcasses. However, over half of all bruises were identified to only be minor in severity. Nearly all cattle (98.4%) were free of visible injection-site lesions. Current results suggest improvements have been made in cattle and meat quality in the cow and bull sector. Furthermore, the results provide guidance for continued educational and research efforts for improving market cow and bull beef quality