1.15 Sub-lethal effects at stake: Does the acaricide Coumaphos and fungicide Folpet affect the hypopharyngeal glands size?

Background: Pesticides are increasingly suspected to be involved at a global scale in honey bee decline. Most studies focuses on acute effects on mortality, whereas sub-lethal effects are poorly understood. Hypophryngeal glands (HPG), producing royal jelly to feed brood, are established marker to assess sub-lethal effects of pesticides where for example the size of the acini can be measured. The size of the later depends of different natural factors: the age of the bee and the type of task performed by the bee. The HPG are the best developed at the age of 10 days by nursing bees. Regarding the data requirements of the new EFSA bee guidance document and the recently developed OECD larva test 237 and 239 a data GAP regarding residues of PSM in the produced Royal Jelly by pesticide exposed bees which might have an adverse impact on larva development from day 1 to day 3 is recognized. Method: The effects on the commonly and widely used varroacide coumaphos in hives and the fungicide folpet in agriculture are currently unknown. Here we measured the size of the acini of new emerged bees treated with field realistic and non-realistic doses of both substances dissolved in pollen patties fed ad libitum for nine days (N=3 cages with 20 bees in each group) and in small encaged colonies without queens. An untreated and acetone control were established. The effects of the pesticides on workers and residues in gelee royal were tested with and without brood to take into consideration variations according to the tasks performed by the bees due to labor division. . After staining HPG activity was measured as a proxy via acini size. The results will be discussed. Results: Our results may help to improve knowledge in the development and validation of methods to evaluate the risk of bees exposed to pesticides for plant protection product authorization in an appropriate and comparable way which could be consequently implemented in standardized ring-test.Background: Pesticides are increasingly suspected to be involved at a global scale in honey bee decline. Most studies focuses on acute effects on mortality, whereas sub-lethal effects are poorly understood. Hypophryngeal glands (HPG), producing royal jelly to feed brood, are established marker to assess sub-lethal effects of pesticides where for example the size of the acini can be measured. The size of the later depends of different natural factors: the age of the bee and the type of task performed by the bee. The HPG are the best developed at the age of 10 days by nursing bees. Regarding the data requirements of the new EFSA bee guidance document and the recently developed OECD larva test 237 and 239 a data GAP regarding residues of PSM in the produced Royal Jelly by pesticide exposed bees which might have an adverse impact on larva development from day 1 to day 3 is recognized. Method: The effects on the commonly and widely used varroacide coumaphos in hives and the fungicide folpet in agriculture are currently unknown. Here we measured the size of the acini of new emerged bees treated with field realistic and non-realistic doses of both substances dissolved in pollen patties fed ad libitum for nine days (N=3 cages with 20 bees in each group) and in small encaged colonies without queens. An untreated and acetone control were established. The effects of the pesticides on workers and residues in gelee royal were tested with and without brood to take into consideration variations according to the tasks performed by the bees due to labor division. . After staining HPG activity was measured as a proxy via acini size. The results will be discussed. Results: Our results may help to improve knowledge in the development and validation of methods to evaluate the risk of bees exposed to pesticides for plant protection product authorization in an appropriate and comparable way which could be consequently implemented in standardized ring-test

Software to convert terrestrial LiDAR scans of natural environments into photorealistic meshes

The introduction of 3D scanning has strongly influenced environmental sciences. If the resulting point clouds can be transformed into polygon meshes, a vast range of visualisation and analysis tools can be applied. But extracting accurate meshes from large point clouds gathered in natural environments is not trivial, requiring a suite of customisable processing steps. We present Habitat3D, an open source software tool to generate photorealistic meshes from registered point clouds of natural outdoor scenes. We demonstrate its capability by extracting meshes of different environments: 8,800 m2 grassland featuring several Eucalyptus trees (combining 9 scans and 41,989,885 data points); 1,018 m2 desert densely covered by vegetation (combining 56 scans and 192,223,621 data points); a well-structured garden; and a rough, volcanic surface. The resultant reconstructions accurately preserve all spatial features with millimetre accuracy whilst reducing the memory load by up to 98.5%. This enables rapid visualisation of the environments using off-the-shelf game engines and graphics hardware

Comparison of two 3D tracking paradigms for freely flying insects

In this paper, we discuss and compare state-of-the-art 3D tracking paradigms for flying insects such as Drosophila melanogaster. If two cameras are employed to estimate the trajectories of these identical appearing objects, calculating stereo and temporal correspondences leads to an NP-hard assignment problem. Currently, there are two different types of approaches discussed in the literature: probabilistic approaches and global correspondence selection approaches. Both have advantages and limitations in terms of accuracy and complexity. Here, we present algorithms for both paradigms. The probabilistic approach utilizes the Kalman filter for temporal tracking. The correspondence selection approach calculates the trajectories based on an overall cost function. Limitations of both approaches are addressed by integrating a third camera to verify consistency of the stereo pairings and to reduce the complexity of the global selection. Furthermore, a novel greedy optimization scheme is introduced for the correspondence selection approach. We compare both paradigms based on synthetic data with ground truth availability. Results show that the global selection is more accurate, while the previously proposed tracking-by-matching (probabilistic) approach is causal and feasible for longer tracking periods and very high target densities. We further demonstrate that our extended global selection scheme outperforms current correspondence selection approaches in tracking accuracy and tracking time

FIM Imaging and FIMtrack: Two New Tools Allowing High-throughput and Cost Effective Locomotion Analysis

The analysis of neuronal network function requires a reliable measurement of behavioral traits. Since the behavior of freely moving animals is variable to a certain degree, many animals have to be analyzed, to obtain statistically significant data. This in turn requires a computer assisted automated quantification of locomotion patterns. To obtain high contrast images of almost translucent and small moving objects, a novel imaging technique based on frustrated total internal reflection called FIM was developed. In this setup, animals are only illuminated with infrared light at the very specific position of contact with the underlying crawling surface. This methodology results in very high contrast images. Subsequently, these high contrast images are processed using established contour tracking algorithms. Based on this, we developed the FIMTrack software, which serves to extract a number of features needed to quantitatively describe a large variety of locomotion characteristics. During the development of this software package, we focused our efforts on an open source architecture allowing the easy addition of further modules. The program operates platform independent and is accompanied by an intuitive GUI guiding the user through data analysis. All locomotion parameter values are given in form of csv files allowing further data analyses. In addition, a Results Viewer integrated into the tracking software provides the opportunity to interactively review and adjust the output, as might be needed during stimulus integration. The power of FIM and FIMTrack is demonstrated by studying the locomotion of Drosophila larvae

Deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks

Brain extraction in magnetic resonance imaging (MRI) data is an important segmentation step in many neuroimaging preprocessing pipelines. Image segmentation is one of the research fields in which deep learning had the biggest impact in recent years enabling high precision segmentation with minimal compute. Consequently, traditional brain extraction methods are now being replaced by deep learning-based methods. Here, we used a unique dataset comprising 568 T1-weighted (T1w) MR images from 191 different studies in combination with cutting edge deep learning methods to build a fast, high-precision brain extraction tool called deepbet. deepbet uses LinkNet, a modern UNet architecture, in a two stage prediction process. This increases its segmentation performance, setting a novel state-of-the-art performance during cross-validation with a median Dice score (DSC) of 99.0% on unseen datasets, outperforming current state of the art models (DSC = 97.8% and DSC = 97.9%). While current methods are more sensitive to outliers, resulting in Dice scores as low as 76.5%, deepbet manages to achieve a Dice score of > 96.9% for all samples. Finally, our model accelerates brain extraction by a factor of ~10 compared to current methods, enabling the processing of one image in ~2 seconds on low level hardware

FIMTrack: An open source tracking and locomotion analysis software for small animals

<div><p>Imaging and analyzing the locomotion behavior of small animals such as Drosophila larvae or C. elegans worms has become an integral subject of biological research. In the past we have introduced FIM, a novel imaging system feasible to extract high contrast images. This system in combination with the associated tracking software FIMTrack is already used by many groups all over the world. However, so far there has not been an in-depth discussion of the technical aspects. Here we elaborate on the implementation details of FIMTrack and give an in-depth explanation of the used algorithms. Among others, the software offers several tracking strategies to cover a wide range of different model organisms, locomotion types, and camera properties. Furthermore, the software facilitates stimuli-based analysis in combination with built-in manual tracking and correction functionalities. All features are integrated in an easy-to-use graphical user interface. To demonstrate the potential of FIMTrack we provide an evaluation of its accuracy using manually labeled data. The source code is available under the GNU GPLv3 at <a href="https://github.com/i-git/FIMTrack" target="_blank">https://github.com/i-git/FIMTrack</a> and pre-compiled binaries for Windows and Mac are available at <a href="http://fim.uni-muenster.de" target="_blank">http://fim.uni-muenster.de</a>.</p></div

Perspectives in machine learning for wildlife conservation

Data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices. These new technologies and the data they generate hold great potential for large-scale environmental monitoring and understanding, but are limited by current data processing approaches which are inefficient in how they ingest, digest, and distill data into relevant information. We argue that machine learning, and especially deep learning approaches, can meet this analytic challenge to enhance our understanding, monitoring capacity, and conservation of wildlife species. Incorporating machine learning into ecological workflows could improve inputs for population and behavior models and eventually lead to integrated hybrid modeling tools, with ecological models acting as constraints for machine learning models and the latter providing data-supported insights. In essence, by combining new machine learning approaches with ecological domain knowledge, animal ecologists can capitalize on the abundance of data generated by modern sensor technologies in order to reliably estimate population abundances, study animal behavior and mitigate human/wildlife conflicts. To succeed, this approach will require close collaboration and cross-disciplinary education between the computer science and animal ecology communities in order to ensure the quality of machine learning approaches and train a new generation of data scientists in ecology and conservation