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Burst analysis of recording sessions across three species. a) Burst of extensor spikes in the Mantis Shrimp. Burst duration is time difference between first and last spikes. To show temporal change in spiking, we calculate the number of spikes in the first vs second half of each burst, in this example 9 and 17 respectively. b) Characteristic EMG bursts from each organism. Individual spike times are highlighted by stars. c) Waveform summary of spikes for each organism.
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Mantis shrimp are aggressive, burrowing crustaceans that hunt using one the fastest movements in the natural world. These stomatopods can crack the calcified shells of prey or spear down unsuspecting fish with lighting speed. Their strike makes use of power-amplification mechanisms to move their limbs much faster than is possible by muscles alone....
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... firing rate of spikes within the EMG traces changed over the course of each EMG burst. Figure 4 compares burst lengths (time between first spike and last spike) and spike counts within the first and last half of the burst across 3 different animals (Figure 4a). Power amplifying animals such as mantis shrimp and crickets had shorter average burst durations than cockroaches. ...
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... firing rate of spikes within the EMG traces changed over the course of each EMG burst. Figure 4 compares burst lengths (time between first spike and last spike) and spike counts within the first and last half of the burst across 3 different animals (Figure 4a). Power amplifying animals such as mantis shrimp and crickets had shorter average burst durations than cockroaches. ...
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... post-hoc test showed that though there was a significant difference between mantis shrimp and cockroaches, there was no significant difference between cockroaches and crickets, and there was a significant difference between mantis shrimp and crickets (Kruskal-Wallis test: χ2(2)=16.85, p=0.0002, (Figure 4b). The variance in data between mantis shrimp and cockroaches differed across organisms as well (Levene's test for equal variance: F(2,43)=9.72, ...
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... salient feature of power-amplifying EMG bursts in our data is the tendency to increase in firing rate from the beginning to the end of the recording (Figure 4c). This increasing trend held across 87% of all recordings of poweramplifying animals, and exhibits a stark difference from cockroaches, in which only 25% of the recordings have a net increase in spikes between the beginning and end of the burst. ...
Citations
... The structure of the system using EMG to monitor shrimp behavior is shown in Figure 5. The signal collected by EMG is converted and transmitted to computers, and the signals can be analyzed to detect physiological abnormalities, activation level, recruitment order, and the biomechanics of crustacean movement [129,130]. Therefore, these electrical signals can be used to design automated growth monitoring systems and develop intelligent decision-making and control systems for aquaculture. ...
... It is reliable for monitoring feeding behavior and estimating the intensity of behavior based on chemical and biological EMG methods to gain a deeper understanding of crustacean feeding status, and the obtained data can be used to establish accurate growth models. The principle of studying lobster movement patterns is similar to monitoring eating behavior; the difference is that chronic electrodes are implanted on the shrimp's legs instead of on the claws [129]. More importantly, the EMG pattern can be analyzed to determine whether the behavior is reflexive or spontaneous [130,135], which solves the problem that machine vision and acoustics cannot monitor some behaviors that are not obvious. ...
Crustacean farming is a fast-growing sector and has contributed to improving incomes. Many studies have focused on how to improve crustacean production. Information about crustacean behavior is important in this respect. Manual methods of detecting crustacean behavior are usually infectible, time-consuming, and imprecise. Therefore, automatic growth situation monitoring according to changes in behavior has gained more attention, including acoustic technology, machine vision, and sensors. This article reviews the development of these automatic behavior monitoring methods over the past three decades and summarizes their domains of application, as well as their advantages and disadvantages. Furthermore, the challenges of individual sensitivity and aquaculture environment for future research on the behavior of crustaceans are also highlighted. Studies show that feeding behavior, movement rhythms, and reproduction behavior are the three most important behaviors of crustaceans, and the applications of information technology such as advanced machine vision technology have great significance to accelerate the development of new means and techniques for more effective automatic monitoring. However, the accuracy and intelligence still need to be improved to meet intensive aquaculture requirements. Our purpose is to provide researchers and practitioners with a better understanding of the state of the art of automatic monitoring of crustacean behaviors, pursuant of supporting the implementation of smart crustacean farming applications.
... Murdock. This issue is representative of the scholarship our readers expect from JUNE and features articles with new innovative resources and methods for neuroscience lab courses using invertebrates (Pollak et al., 2019), mouse models (Quinan et al., 2019) and undergraduates (Segawa, 2019) as well as a perspective article on approaches to teach principles of neurophysiology (Crisp, 2019) and a case study article on the history of neuroanatomy (Mitrano, 2019). The title to this editorial is reflective of several articles in this issue that relate to neuroscience outreach targeting diverse student populations as well as two articles that remind us about the diversity of students in our classrooms and teaching labs. ...
Citizen Science or community science has been around for a long time. The scope of community involvement in Citizen Science initiatives ranges from short-term data collection to intensive engagement to delve into a research topic together with scientists and/or other volunteers. Although many volunteer researchers have academic training, it is not a prerequisite for participation in research projects. It is important to adhere to scientific standards, which include, above all, transparency with regard to the methodology of data collection and public discussion of the results, and open educational resources (OER). Hereby, Citizen Science is closely linked to Open Science. In our contribution, we will introduce two projects, both developed within the Wikimedia Fellowship Freies Wissen.
The top-down approach: ERGo! An Entomology Research Tool to raise awareness of biodiversity protection.
Inclusion in academia and pressing social problems such as climate change are fundamentally social justice issues. To facilitate early participation in the scientific process on the part of people holding underrepresented identities in science, we develop a Citizen Science initiative based on a low-cost open-source platform (ERGo!) to perform a technique for electrical recordings from insect eyes known as electroretinograms (ERGs) while presenting visual stimuli. Pasadena Unified School District High School students pilot ERG experiments to test the feasibility of this technique as a large-scale Citizen Science initiative. With ERGo!, future Citizen Scientists contribute data to cutting-edge research that monitors insect biodiversity, adaptation, and health in rapidly changing environments caused by monocultures, pesticides, and climate change.
The bottom-up approach: Open cultural data collection. A Citizen Science initiative for regional knowledge curation.
We catalogued the 18th century German magazine ‘Die Gartenlaube’ (in Wikisource) with bibliographic metadata in Wikidata in a project called ‘Die Datenlaube’. We develop collaborative approaches for linked open data methods to produce data sets about historical knowledge. The concept of ‘Open Citizen Science’ offers a methodological baseline for Open Science practises in fields of digital humanities. Scanned documents and structured open metadata revealed open access to historic collections. Through the Wikimedia platforms 'Die Datenlaube' creates possibilities to edit entries, to design own investigations, and to contribute to OER.
Based on the elaboration of the two rather different projects (natural and social sciences, involvement of pupils vs citizens, top-down vs bottom-up), we will discuss similarities and hence the challenges and lessons learned for using and developing Open Science elements in Citizen Science and mutual learning. Furthermore, we will conclude by focusing on the opportunities resulting from the integration of societal expectations in science and vice versa.
Biological data sets are increasingly becoming information-dense, making it effective to use a computer science-based analysis. We used convolution neural networks (CNN) and the specific CNN architecture Unet to study sponge behavior over time. We analyzed a large time series of hourly high-resolution still images of a marine sponge, Suberites concinnus (Demospongiae, Suberitidae) captured between 2012 and 2015 using the NEPTUNE seafloor cabled observatory, off the west coast of Vancouver Island, Canada. We applied semantic segmentation with the Unet architecture with some modifications, including adapting parts of the architecture to be more applicable to three-channel images (RGB). Some alterations that made this model successful were the use of a dice-loss coefficient, Adam optimizer and a dropout function after each convolutional layer which provided losses, accuracies and dice scores of up to 0.03, 0.98 and 0.97, respectively. The model was tested with five-fold cross-validation. This study is a first step towards analyzing trends in the behavior of a demosponge in an environment that experiences severe seasonal and inter-annual changes in climate. The end objective is to correlate changes in sponge size (activity) over seasons and years with environmental variables collected from the same observatory platform. Our work provides a roadmap for others who seek to cross the interdisciplinary boundaries between biology and computer science.
