Broad‐scale applications of the Raspberry Pi: A review and guide for biologists

A modular and flexible open source cell incubator system for mobile and stationary use

Culturing living cells in vitro requires the maintenance of physiological conditions for extended periods of time. Here, we introduce a versatile and affordable incubation system, addressing the limitations of traditional incubation systems. Conventionally, stationary cell incubators maintain constant temperature and gas levels for in vitro cell culturing. Combining such incubators with additional lab equipment proves challenging. The presented platform offers modularity and adaptability, enabling customization to diverse experimental needs. The system includes a main unit with a user-friendly interface as well as an interchangeable incubation chamber. We present two incubation chambers targeting two completely different use cases. The first chamber, named "inkugo" facilitates the transportation of cells for up to two hours without external power and for more than a day without an external CO2 source. The second chamber termed "inkubox" was designed to enable continuous electrophysiological recordings. Recordings from up to four neural cultures growing on high-density microelectrode arrays can be performed in parallel. The system's unique feature lies in its separability of control and incubation components, allowing one control unit to manage various custom chambers. The design's simplicity and the use of widely accessible components make the here proposed incubation system replicable for any laboratory. This platform fosters collaboration and experimentation in both decentralized and traditional laboratory settings, making it an invaluable addition to any cell culturing pipeline.

Development of a multisensor biologging collar and analytical techniques to describe high-resolution spatial behavior in free-ranging terrestrial mammals

Biologging has proven to be a powerful approach to investigate diverse questions related to movement ecology across a range of spatiotemporal scales and increasingly relies on multidisciplinary expertise. However, the variety of animal-borne equipment, coupled with little consensus regarding analytical approaches to interpret large, complex data sets presents challenges and makes comparison between studies and study species difficult. Here, we present a combined hardware and analytical approach for standardizing the collection, analysis, and interpretation of multisensor biologging data. Here, we present (i) a custom-designed integrated multisensor collar (IMSC), which was field tested on 71 free-ranging wild boar (Sus scrofa) over 2 years; (ii) a machine learning behavioral classifier capable of identifying six behaviors in free-roaming boar, validated across individuals equipped with differing collar designs; and (iii) laboratory and field-based calibration and accuracy assessments of animal magnetic heading measurements derived from raw magnetometer data. The IMSC capacity and durability exceeded expectations, with a 94% collar recovery rate and a 75% cumulative data recording success rate, with a maximum logging duration of 421 days. The behavioral classifier had an overall accuracy of 85% in identifying the six behavioral classes when tested on multiple collar designs and improved to 90% when tested on data exclusively from the IMSC. Both laboratory and field tests of magnetic compass headings were in precise agreement with expectations, with overall median magnetic headings deviating from ground truth observations by 1.7° and 0°, respectively. Although multisensor equipment and sophisticated analyses are now commonplace in biologging studies, the IMSC hardware and analytical framework presented here provide a valuable tool for biologging researchers and will facilitate standardization of biologging data across studies. In addition, we highlight the potential of additional analyses available using this framework that can be adapted for use in future studies on terrestrial mammals.

NIRis: A low-cost, versatile imaging system for near-infrared fluorescence detection of phototrophic cell colonies used in research and education

A variety of costly research-grade imaging devices are available for the detection of spectroscopic features. Here we present an affordable, open-source and versatile device, suitable for a range of applications. We provide the files to print the imaging chamber with commonly available 3D printers and instructions to assemble it with easily available hardware. The imager is suitable for rapid sample screening in research, as well as for educational purposes. We provide details and results for an already proven set-up which suits the needs of a research group and students interested in UV-induced near-infrared fluorescence detection of microbial colonies grown on Petri dishes. The fluorescence signal confirms the presence of bacteriochlorophyll a in aerobic anoxygenic phototrophic bacteria (AAPB). The imager allows for the rapid detection and subsequent isolation of AAPB colonies on Petri dishes with diverse environmental samples. To this date, 15 devices have been build and more than 7000 Petri dishes have been analyzed for AAPB, leading to over 1000 new AAPB isolates. Parts can be modified depending on needs and budget. The latest version with automated switches and double band pass filters costs around 350€ in materials and resolves bacterial colonies with diameters of 0.5 mm and larger. The low cost and modular build allow for the integration in high school classes to educate students on light properties, fluorescence and microbiology. Computer-aided design of 3D-printed parts and programming of the employed Raspberry Pi computer could be incorporated in computer sciences classes. Students have been also inspired to do agar art with microbes. The device is currently used in seven different high schools in Finland. Additionally, a science education network of Finnish universities has incorporated it in its program for high school students. Video guides have been produced to facilitate easy operation and accessibility of the device.

Bat2Web: A Framework for Real-Time Classification of Bat Species Echolocation Signals Using Audio Sensor Data

Bats play a pivotal role in maintaining ecological balance, and studying their behaviors offers vital insights into environmental health and aids in conservation efforts. Determining the presence of various bat species in an environment is essential for many bat studies. Specialized audio sensors can be used to record bat echolocation calls that can then be used to identify bat species. However, the complexity of bat calls presents a significant challenge, necessitating expert analysis and extensive time for accurate interpretation. Recent advances in neural networks can help identify bat species automatically from their echolocation calls. Such neural networks can be integrated into a complete end-to-end system that leverages recent internet of things (IoT) technologies with long-range, low-powered communication protocols to implement automated acoustical monitoring. This paper presents the design and implementation of such a system that uses a tiny neural network for interpreting sensor data derived from bat echolocation signals. A highly compact convolutional neural network (CNN) model was developed that demonstrated excellent performance in bat species identification, achieving an F1-score of 0.9578 and an accuracy rate of 97.5%. The neural network was deployed, and its performance was evaluated on various alternative edge devices, including the NVIDIA Jetson Nano and Google Coral.

A Behavioral Assay to Quantify Odor-Guided Thermotaxis with Anopheles gambiae under Semi-Field Conditions

The African malaria mosquito Anopheles gambiae is strongly attracted to human body odor and skin temperature. Quantitative behavioral assays suitable for use in semi-field environments with this nocturnal mosquito species are essential to gain improved insights into An. gambiae sensory biology, the mechanistic basis of mosquito attraction to humans, and host preference. In this protocol, we describe steps for engineering equipment for a novel behavioral assay for An. gambiae, which we have termed the odor-guided thermotaxis assay (OGTA). The OGTA uses infrared videography to quantify landings of female An. gambiae on an aluminum platform heated to human skin temperature that can be baited with volatile odorants such as carbon dioxide or human whole body odor. The OGTA facilitates high-content recordings of An. gambiae landing behavior during odor-guided thermotaxis under naturalistic semi-field conditions without the requirement for domestic power.

A primer on the use of computational modelling to investigate affective states, affective disorders and animal welfare in non-human animals

Objective measures of animal emotion-like and mood-like states are essential for preclinical studies of affective disorders and for assessing the welfare of laboratory and other animals. However, the development and validation of measures of these affective states poses a challenge partly because the relationships between affect and its behavioural, physiological and cognitive signatures are complex. Here, we suggest that the crisp characterisations offered by computational modelling of the underlying, but unobservable, processes that mediate these signatures should provide better insights. Although this computational psychiatry approach has been widely used in human research in both health and disease, translational computational psychiatry studies remain few and far between. We explain how building computational models with data from animal studies could play a pivotal role in furthering our understanding of the aetiology of affective disorders, associated affective states and the likely underlying cognitive processes involved. We end by outlining the basic steps involved in a simple computational analysis.

Edge computing in wildlife behavior and ecology

Modern sensor technologies increasingly enrich studies in wildlife behavior and ecology. However, constraints on weight, connectivity, energy and memory availability limit their implementation. With the advent of edge computing, there is increasing potential to mitigate these constraints, and drive major advancements in wildlife studies.

Formal Assessment of Agreement and Similarity between an Open-Source and a Reference Industrial Device with an Application to a Low-Cost pH Logger

Open-source devices are nowadays used in a vast number of research fields like medicine, education, agriculture, and sports, among others. In this work, an open-source, portable, low-cost pH logger, appropriate for in situ measurements, was designed and developed to assist in experiments on agricultural produce manufacturing. Τhe device was calibrated manually using pH buffers for values of 4.01 and 7.01. Then, it was tested by manually measuring the pH from the juice of citrus fruits. A waterproof temperature sensor was added to the device for temperature compensation when measuring the pH. A formal method comparison process between the open-source device and a Hanna HI9024 Waterproof pH Meter was designed to assess their agreement. We derived indices of agreement and graphical assessment tools using mixed-effects models. The advantages and disadvantages of interpreting agreement through the proposed procedure are discussed. In our illustration, the indices reported mediocre agreement and the subsequent similarity analysis revealed a fixed bias of 0.22 pH units. After recalibration, agreement between the devices improved to excellent levels. The process can be followed in general to avoid misleading or over-simplistic results of studies reporting solely correlation coefficients for formal comparison purposes.

Unravelling the origins of boldness behaviour: a common garden experiment with cavefish (Barbatula barbatula)

Many animals show an aversion to bright, open spaces, with significant variability seen across species, populations and individuals within populations. Although there is much interest in the underlying causes of this behaviour, few studies have been able to systematically isolate the role of heritable and environmental effects. Here, we addressed this gap using a common garden experiment with cavefish. Specifically, we bred and cross-bred cave loaches (Barbatula barbatula), Europe's only known cavefish, in the laboratory, raised the offspring in complete darkness or normal light conditions, and studied their light avoidance behaviour. Cavefish spent much more time in a light area and ventured further out, while surface fish spent considerable time in risk-assessment behaviour between the light and dark areas. Hybrids behaved most similarly to cavefish. Light treatment and eye quality and lens size only had a modest effect. Our results suggest light avoidance behaviour of cavefish has a heritable basis and is fundamentally linked to increased boldness rather than reduced vision, which is likely adaptive given the complete lack of macropredators in the cave environment. Our study provides novel experimental insights into the behavioural divergence of cavefish and contributes to our broader understanding of the evolution of boldness and behavioural adaptation.

Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals

Recombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and complex recombinant biopharmaceuticals on a large scale. Plant system has several advantages over other host systems such as humanized expression, glycosylation, scalability, reduced risk of human or animal pathogenic contaminants, rapid and cost-effective production. Despite many advantages, the expression of recombinant proteins in plant system is hindered by some factors such as non-human post-translational modifications, protein misfolding, conformation changes and instability. Artificial intelligence (AI) plays a vital role in various fields of biotechnology and in the aspect of plant molecular pharming, a significant increase in yield and stability can be achieved with the intervention of AI-based multi-approach to overcome the hindrance factors. Current limitations of plant-based recombinant biopharmaceutical production can be circumvented with the aid of synthetic biology tools and AI algorithms in plant-based glycan engineering for protein folding, stability, viability, catalytic activity and organelle targeting. The AI models, including but not limited to, neural network, support vector machines, linear regression, Gaussian process and regressor ensemble, work by predicting the training and experimental data sets to design and validate the protein structures thereby optimizing properties such as thermostability, catalytic activity, antibody affinity, and protein folding. This review focuses on, integrating systems engineering approaches and AI-based machine learning and deep learning algorithms in protein engineering and host engineering to augment protein production in plant systems to meet the ever-expanding therapeutics market.

Testing and Evaluation of Low-Cost Sensors for Developing Open Smart Campus Systems Based on IoT

Urbanization has led to the need for the intelligent management of various urban challenges, from traffic to energy. In this context, smart campuses and buildings emerge as microcosms of smart cities, offering both opportunities and challenges in technology and communication integration. This study sets itself apart by prioritizing sustainable, adaptable, and reusable solutions through an open-source framework and open data protocols. We utilized the Internet of Things (IoT) and cost-effective sensors to capture real-time data for three different use cases: real-time monitoring of visitor counts, room and parking occupancy, and the collection of environment and climate data. Our analysis revealed that the implementation of the utilized hardware and software combination significantly improved the implementation of open smart campus systems, providing a usable visitor information system for students. Moreover, our focus on data privacy and technological versatility offers valuable insights into real-world applicability and limitations. This study contributes a novel framework that not only drives technological advancements but is also readily adaptable, improvable, and reusable across diverse settings, thereby showcasing the untapped potential of smart, sustainable systems.

Open Hardware for Microfluidics: Exploiting Raspberry Pi Singleboard Computer and Camera Systems for Customisable Laboratory Instrumentation

The integration of Raspberry Pi miniature computer systems with microfluidics has revolutionised the development of low-cost and customizable analytical systems in life science laboratories. This review explores the applications of Raspberry Pi in microfluidics, with a focus on imaging, including microscopy and automated image capture. By leveraging the low cost, flexibility and accessibility of Raspberry Pi components, high-resolution imaging and analysis have been achieved in direct mammalian and bacterial cellular imaging and a plethora of image-based biochemical and molecular assays, from immunoassays, through microbial growth, to nucleic acid methods such as real-time-qPCR. The control of image capture permitted by Raspberry Pi hardware can also be combined with onboard image analysis. Open-source hardware offers an opportunity to develop complex laboratory instrumentation systems at a fraction of the cost of commercial equipment and, importantly, offers an opportunity for complete customisation to meet the users' needs. However, these benefits come with a trade-off: challenges remain for those wishing to incorporate open-source hardware equipment in their own work, including requirements for construction and operator skill, the need for good documentation and the availability of rapid prototyping such as 3D printing plus other components. These advances in open-source hardware have the potential to improve the efficiency, accessibility, and cost-effectiveness of microfluidic-based experiments and applications.

Automated behavioral scoring: Do we even need humans?

The development of automated behavior scoring technology has been a tremendous boon to the study of social behavior. However, completely outsourcing behavioral analysis to a computer runs the risk of overlooking important nuances, and researchers risk distancing themselves from their very object of study. Here, I make the case that while automating analysis has been valuable, and overautomating analysis is risky, more effort should be spent automating the collection of behavioral data. Continuous automated behavioral observations conducted in situ have the promise to reduce confounding elements of social behavior research, such as handling stress, novel environments, one-time "snapshot" measures, and experimenter presence. Now that we have the capability to automatically process behavioral observations thanks to machine vision and machine learning, we would do well to leverage the same open-source ethos to increase the throughput of behavioral observation and collection. Fortunately, several such platforms have recently been developed. Repeated testing in the home environment will produce higher qualities and quantities of data, bringing us closer to realizing the ethological goals of studying animal behavior in a naturalistic context.

Zoobooth: A portable, open-source and affordable approach for repeated size measurements of live individual zooplankton

Repeated size measurements of individual animals are valuable data for many research questions, but it is often hard to obtain without causing stress or damage to the animal. We developed a video-based approach called Zoobooth to size individual zooplankton, which involves a low risk of handling accidents and stress. Here we describe the process of assembling the instrument we used to acquire video recordings of single zooplankton and the procedure to obtain size estimates from the recorded videos. Our setup produces accurate size estimates for Daphnia magna (correlation to manual measurements = 0.97), and was also tested with other zooplankton species. Zoobooth is especially advantageous when one needs size measurements of live, individual mesozooplankton. The device is small, portable, and comprised of very affordable and readily available components. It can easily be modified for other purposes, such as studies of coloration or behavior of micro-and macro-plankton. We share all the files to build and use Zoobooth.

A Novel OpenBCI Framework for EEG-Based Neurophysiological Experiments

An Open Brain-Computer Interface (OpenBCI) provides unparalleled freedom and flexibility through open-source hardware and firmware at a low-cost implementation. It exploits robust hardware platforms and powerful software development kits to create customized drivers with advanced capabilities. Still, several restrictions may significantly reduce the performance of OpenBCI. These limitations include the need for more effective communication between computers and peripheral devices and more flexibility for fast settings under specific protocols for neurophysiological data. This paper describes a flexible and scalable OpenBCI framework for electroencephalographic (EEG) data experiments using the Cyton acquisition board with updated drivers to maximize the hardware benefits of ADS1299 platforms. The framework handles distributed computing tasks and supports multiple sampling rates, communication protocols, free electrode placement, and single marker synchronization. As a result, the OpenBCI system delivers real-time feedback and controlled execution of EEG-based clinical protocols for implementing the steps of neural recording, decoding, stimulation, and real-time analysis. In addition, the system incorporates automatic background configuration and user-friendly widgets for stimuli delivery. Motor imagery tests the closed-loop BCI designed to enable real-time streaming within the required latency and jitter ranges. Therefore, the presented framework offers a promising solution for tailored neurophysiological data processing.

Closed-loop Control Systems for Pumps used in Portable Analytical Systems

The demand for accurate control of the flowrate/pressure in chemical analytical systems has given rise to the adoption of mechatronic approaches in analytical instruments. A mechatronic device is a synergistic system which combines mechanical, electronic, computer and control components. In the development of portable analytical devices, considering the instrument as a mechatronic system can be useful to mitigate compromises made to decrease space, weight, or power consumption. Fluid handling is important for reliability, however, commonly utilized platforms such as syringe and peristaltic pumps are typically characterized by flow/pressure fluctuations and slow responses. Closed loop control systems have been used effectively to decrease the difference between desired and realized fluidic output. This review discusses the way control systems have been implemented for enhanced fluidic control, categorized by pump type. Advanced control strategies used to enhance the transient and the steady state responses are discussed, along with examples of their implementation in portable analytical systems. The review is concluded with the outlook that the challenge in adequately expressing the complexity and dynamics of the fluidic network as a mathematical model has yielded a trend towards the adoption of experimentally informed models and machine learning approaches.

A method of cotton root segmentation based on edge devices

The root is an important organ for plants to absorb water and nutrients. In situ root research method is an intuitive method to explore root phenotype and its change dynamics. At present, in situ root research, roots can be accurately extracted from in situ root images, but there are still problems such as low analysis efficiency, high acquisition cost, and difficult deployment of image acquisition devices outdoors. Therefore, this study designed a precise extraction method of in situ roots based on semantic segmentation model and edge device deployment. It initially proposes two data expansion methods, pixel by pixel and equal proportion, expand 100 original images to 1600 and 53193 respectively. It then presents an improved DeeplabV3+ root segmentation model based on CBAM and ASPP in series is designed, and the segmentation accuracy is 93.01%. The root phenotype parameters were verified through the Rhizo Vision Explorers platform, and the root length error was 0.669%, and the root diameter error was 1.003%. It afterwards designs a time-saving Fast prediction strategy. Compared with the Normal prediction strategy, the time consumption is reduced by 22.71% on GPU and 36.85% in raspberry pie. It ultimately deploys the model to Raspberry Pie, realizing the low-cost and portable root image acquisition and segmentation, which is conducive to outdoor deployment. In addition, the cost accounting is only $247. It takes 8 hours to perform image acquisition and segmentation tasks, and the power consumption is as low as 0.051kWh. In conclusion, the method proposed in this study has good performance in model accuracy, economic cost, energy consumption, etc. This paper realizes low-cost and high-precision segmentation of in-situ root based on edge equipment, which provides new insights for high-throughput field research and application of in-situ root.

Artificial intelligence techniques for retinal prostheses: a comprehensive review and future direction

Objective. Retinal prostheses are promising devices to restore vision for patients with severe age-related macular degeneration or retinitis pigmentosa disease. The visual processing mechanism embodied in retinal prostheses play an important role in the restoration effect. Its performance depends on our understanding of the retina's working mechanism and the evolvement of computer vision models. Recently, remarkable progress has been made in the field of processing algorithm for retinal prostheses where the new discovery of the retina's working principle and state-of-the-arts computer vision models are combined together.Approach. We investigated the related research on artificial intelligence techniques for retinal prostheses. The processing algorithm in these studies could be attributed to three types: computer vision-related methods, biophysical models, and deep learning models.Main results. In this review, we first illustrate the structure and function of the normal and degenerated retina, then demonstrate the vision rehabilitation mechanism of three representative retinal prostheses. It is necessary to summarize the computational frameworks abstracted from the normal retina. In addition, the development and feature of three types of different processing algorithms are summarized. Finally, we analyze the bottleneck in existing algorithms and propose our prospect about the future directions to improve the restoration effect.Significance. This review systematically summarizes existing processing models for predicting the response of the retina to external stimuli. What's more, the suggestions for future direction may inspire researchers in this field to design better algorithms for retinal prostheses.

Leveraging big data to uncover the eco-evolutionary factors shaping behavioural development

Mapping the eco-evolutionary factors shaping the development of animals' behavioural phenotypes remains a great challenge. Recent advances in 'big behavioural data' research-the high-resolution tracking of individuals and the harnessing of that data with powerful analytical tools-have vastly improved our ability to measure and model developing behavioural phenotypes. Applied to the study of behavioural ontogeny, the unfolding of whole behavioural repertoires can be mapped in unprecedented detail with relative ease. This overcomes long-standing experimental bottlenecks and heralds a surge of studies that more finely define and explore behavioural-experiential trajectories across development. In this review, we first provide a brief guide to state-of-the-art approaches that allow the collection and analysis of high-resolution behavioural data across development. We then outline how such approaches can be used to address key issues regarding the ecological and evolutionary factors shaping behavioural development: developmental feedbacks between behaviour and underlying states, early life effects and behavioural transitions, and information integration across development.

The Application of Nanotechnology for Quantification of Circulating Tumour DNA in Liquid Biopsies: A Systematic Review

Technologies for quantifying circulating tumour DNA (ctDNA) in liquid biopsies could enable real-time measurements of cancer progression, profoundly impacting patient care. Sequencing methods can be too complex and time-consuming for regular point-of-care monitoring, but nanotechnology offers an alternative, harnessing the unique properties of objects tens to hundreds of nanometres in size. This systematic review was performed to identify all examples of nanotechnology-based ctDNA detection and assess their potential for clinical use. Google Scholar, PubMed, Web of Science, Google Patents, Espacenet and Embase/MEDLINE were searched up to 23rd March 2021. The review identified nanotechnology-based methods for ctDNA detection for which quantitative measures (e.g., limit of detection, LOD) were reported and biologically relevant samples were used. The pre-defined inclusion criteria were met by 66 records. LODs ranged from 10 zM to 50nM. 25 records presented an LOD of 10fM or below. Nanotechnology-based approaches could provide the basis for the next wave of advances in ctDNA diagnostics, enabling analysis at the point-of-care, but none are currently used clinically. Further work is needed in development and validation; trade-offs are expected between different performance measures e.g., number of sequences detected and time to result.

Exploiting animal personality to reduce chronic stress in captive fish populations

Chronic stress is a major source of welfare problems in many captive populations, including fishes. While we have long known that chronic stress effects arise from maladaptive expression of acute stress response pathways, predicting where and when problems will arise is difficult. Here we highlight how insights from animal personality research could be useful in this regard. Since behavior is the first line of organismal defense when challenged by a stressor, assays of shy-bold type personality variation can provide information about individual stress response that is expected to predict susceptibility to chronic stress. Moreover, recent demonstrations that among-individual differences in stress-related physiology and behaviors are underpinned by genetic factors means that selection on behavioral biomarkers could offer a route to genetic improvement of welfare outcomes in captive fish stocks. Here we review the evidence in support of this proposition, identify remaining empirical gaps in our understanding, and set out appropriate criteria to guide development of biomarkers. The article is largely prospective: fundamental research into fish personality shows how behavioral biomarkers could be used to achieve welfare gains in captive fish populations. However, translating potential to actual gains will require an interdisciplinary approach that integrates the expertise and viewpoints of researchers working across animal behavior, genetics, and welfare science.

Long-term imaging reveals behavioral plasticity during C. elegans dauer exit

BACKGROUND

During their lifetime, animals must adapt their behavior to survive in changing environments. This ability requires the nervous system to undergo adjustments at distinct temporal scales, from short-term dynamic changes in expression of neurotransmitters and receptors to longer-term growth, spatial and connectivity reorganization, while integrating external stimuli. The nematode Caenorhabditis elegans provides a model of nervous system plasticity, in particular its dauer exit decision. Under unfavorable conditions, larvae will enter the non-feeding and non-reproductive stress-resistant dauer stage and adapt their behavior to cope with the harsh new environment, with active reversal under improved conditions leading to resumption of reproductive development. However, how different environmental stimuli regulate the exit decision mechanism and thereby drive the larva's behavioral change is unknown. To fill this gap and provide insights on behavioral changes over extended periods of time, we developed a new open hardware method for long-term imaging (12h) of C. elegans larvae.

RESULTS

Our WormObserver platform comprises open hardware and software components for video acquisition, automated processing of large image data (> 80k images/experiment) and data analysis. We identified dauer-specific behavioral motifs and characterized the behavioral trajectory of dauer exit in different environments and genetic backgrounds to identify key decision points and stimuli promoting dauer exit. Combining long-term behavioral imaging with transcriptomics data, we find that bacterial ingestion triggers a change in neuropeptide gene expression to establish post-dauer behavior.

CONCLUSIONS

Taken together, we show how a developing nervous system can robustly integrate environmental changes activate a developmental switch and adapt the organism's behavior to a new environment. WormObserver is generally applicable to other research questions within and beyond the C. elegans field, having a modular and customizable character and allowing assessment of behavioral plasticity over longer periods.

Towards the fully automated monitoring of ecological communities

High-resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real-time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components-for example, individual behaviours and traits, and species abundance and distribution-is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high-throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high-resolution, multidimensional and standardised data across complex ecologies.

IoT cloud laboratory: Internet of Things architecture for cellular biology

The Internet of Things (IoT) provides a simple framework to control online devices easily. IoT is now a commonplace tool used by technology companies but is rarely used in biology experiments. IoT can benefit cloud biology research through alarm notifications, automation, and the real-time monitoring of experiments. We developed an IoT architecture to control biological devices and implemented it in lab experiments. Lab devices for electrophysiology, microscopy, and microfluidics were created from the ground up to be part of a unified IoT architecture. The system allows each device to be monitored and controlled from an online web tool. We present our IoT architecture so other labs can replicate it for their own experiments.

The emergence and development of behavioral individuality in clonal fish

Behavioral individuality is a ubiquitous phenomenon in animal populations, yet the origins and developmental trajectories of individuality, especially very early in life, are still a black box. Using a high-resolution tracking system, we mapped the behavioral trajectories of genetically identical fish (Poecilia formosa), separated immediately after birth into identical environments, over the first 10 weeks of their life at 3 s resolution. We find that (i) strong behavioral individuality is present at the very first day after birth, (ii) behavioral differences at day 1 of life predict behavior up to at least 10 weeks later, and (iii) patterns of individuality strengthen gradually over developmental time. Our results establish a null model for how behavioral individuality can develop in the absence of genetic and environmental variation and provide experimental evidence that later-in-life individuality can be strongly shaped by factors pre-dating birth like maternal provisioning, epigenetics and pre-birth developmental stochasticity.

PiSpy: An affordable, accessible, and flexible imaging platform for the automated observation of organismal biology and behavior

A great deal of understanding can be gleaned from direct observation of organismal growth, development, and behavior. However, direct observation can be time consuming and influence the organism through unintentional stimuli. Additionally, video capturing equipment can often be prohibitively expensive, difficult to modify to one's specific needs, and may come with unnecessary features. Here, we describe PiSpy, a low-cost, automated video acquisition platform that uses a Raspberry Pi computer and camera to record video or images at specified time intervals or when externally triggered. All settings and controls, such as programmable light cycling, are accessible to users with no programming experience through an easy-to-use graphical user interface. Importantly, the entire PiSpy system can be assembled for less than $100 using laser-cut and 3D-printed components. We demonstrate the broad applications and flexibility of PiSpy across a range of model and non-model organisms. Designs, instructions, and code can be accessed through an online repository, where a global community of PiSpy users can also contribute their own unique customizations and help grow the community of open-source research solutions.

Advances in non-invasive tracking of wave-type electric fish in natural and laboratory settings

Recent technological advances greatly improved the possibility to study freely behaving animals in natural conditions. However, many systems still rely on animal-mounted devices, which can already bias behavioral observations. Alternatively, animal behaviors can be detected and tracked in recordings of stationary sensors, e.g., video cameras. While these approaches circumvent the influence of animal-mounted devices, identification of individuals is much more challenging. We take advantage of the individual-specific electric fields electric fish generate by discharging their electric organ (EOD) to record and track their movement and communication behaviors without interfering with the animals themselves. EODs of complete groups of fish can be recorded with electrode arrays submerged in the water and then be tracked for individual fish. Here, we present an improved algorithm for tracking electric signals of wave-type electric fish. Our algorithm benefits from combining and refining previous approaches of tracking individual specific EOD frequencies and spatial electric field properties. In this process, the similarity of signal pairs in extended data windows determines their tracking order, making the algorithm more robust against detection losses and intersections. We quantify the performance of the algorithm and show its application for a data set recorded with an array of 64 electrodes distributed over a 12 m² section of a stream in the Llanos, Colombia, where we managed, for the first time, to track Apteronotus leptorhynchus over many days. These technological advances make electric fish a unique model system for a detailed analysis of social and communication behaviors, with strong implications for our research on sensory coding.

Frontiers in quantifying wildlife behavioural responses to chemical pollution

Animal behaviour is remarkably sensitive to disruption by chemical pollution, with widespread implications for ecological and evolutionary processes in contaminated wildlife populations. However, conventional approaches applied to study the impacts of chemical pollutants on wildlife behaviour seldom address the complexity of natural environments in which contamination occurs. The aim of this review is to guide the rapidly developing field of behavioural ecotoxicology towards increased environmental realism, ecological complexity, and mechanistic understanding. We identify research areas in ecology that to date have been largely overlooked within behavioural ecotoxicology but which promise to yield valuable insights, including within- and among-individual variation, social networks and collective behaviour, and multi-stressor interactions. Further, we feature methodological and technological innovations that enable the collection of data on pollutant-induced behavioural changes at an unprecedented resolution and scale in the laboratory and the field. In an era of rapid environmental change, there is an urgent need to advance our understanding of the real-world impacts of chemical pollution on wildlife behaviour. This review therefore provides a roadmap of the major outstanding questions in behavioural ecotoxicology and highlights the need for increased cross-talk with other disciplines in order to find the answers.

LightFD: Real-Time Fault Diagnosis with Edge Intelligence for Power Transformers

Power fault monitoring based on acoustic waves has gained a great deal of attention in industry. Existing methods for fault diagnosis typically collect sound signals on site and transmit them to a back-end server for analysis, which may fail to provide a real-time response due to transmission packet loss and latency. However, the limited computing power of edge devices and the existing methods for feature extraction pose a significant challenge to performing diagnosis on the edge. In this paper, we propose a fast Lightweight Fault Diagnosis method for power transformers, referred to as LightFD, which integrates several technical components. Firstly, before feature extraction, we design an asymmetric Hamming-cosine window function to reduce signal spectrum leakage and ensure data integrity. Secondly, we design a multidimensional spatio-temporal feature extraction method to extract acoustic features. Finally, we design a parallel dual-layer, dual-channel lightweight neural network to realize the classification of different fault types on edge devices with limited computing power. Extensive simulation and experimental results show that the diagnostic precision and recall of LightFD reach 94.64% and 95.33%, which represent an improvement of 4% and 1.6% over the traditional SVM method, respectively.

CyberSco.Py an open-source software for event-based, conditional microscopy

Timelapse fluorescence microscopy imaging is routinely used in quantitative cell biology. However, microscopes could become much more powerful investigation systems if they were endowed with simple unsupervised decision-making algorithms to transform them into fully responsive and automated measurement devices. Here, we report CyberSco.Py, Python software for advanced automated timelapse experiments. We provide proof-of-principle of a user-friendly framework that increases the tunability and flexibility when setting up and running fluorescence timelapse microscopy experiments. Importantly, CyberSco.Py combines real-time image analysis with automation capability, which allows users to create conditional, event-based experiments in which the imaging acquisition parameters and the status of various devices can be changed automatically based on the image analysis. We exemplify the relevance of CyberSco.Py to cell biology using several use case experiments with budding yeast. We anticipate that CyberSco.Py could be used to address the growing need for smart microscopy systems to implement more informative quantitative cell biology experiments.

A Low-Cost, Open-Source Peer-to-Peer Energy Trading System for a Remote Community Using the Internet-of-Things, Blockchain, and Hypertext Transfer Protocol

A low-cost, open-source peer-to-peer (P2P) energy trading system for a remote community is presented in this paper. As a result of its geographic location, this community has never been able to access electricity and other modern amenities. This study aims to design and implement a P2P energy trading system for this remote community that allows residents to take advantage of distributed energy resources. A Raspberry Pi 4 Model B (Pi4B) hosts the main server of the trading system that includes the user interface and a local Ethereum blockchain server. The Ethereum blockchain is used to deploy smart contracts. The Internet-of-Things (IoT) servers run on ESP32 microcontrollers. Sensors and actuators connected to the ESP32 are field instrumentation devices that facilitate acquiring, monitoring, and transferring energy data in real-time. To perform trading activities, React.JS open-source library was used to develop the blockchain-enabled user interface. An immutable blockchain network keeps track of all transactions. The proposed system runs on a local Wi-Fi network with restricted authorization for system security. Other security measures such as login credentials, private key, firewall, and secret recovery phrases are also considered for information security and data integrity. A Hypertext Transfer Protocol is implemented for communication between the servers and the client. This explains the overall system design, implementation, testing, and results.

Manipulating actions: a selective two‐option device for cognitive experiments in wild animals

Advances in biologging technologies have significantly improved our ability to track individual animals' behaviour in their natural environment. Beyond observations, automation of data collection has revolutionized cognitive experiments in the wild. For example, radio-frequency identification (RFID) antennae embedded in 'puzzle box' devices have allowed for large-scale cognitive experiments where individuals tagged with passive integrated transponder (PIT) tags interact with puzzle boxes to gain a food reward, with devices logging both the identity and solving action of visitors. Here, we extended the scope of wild cognitive experiments by developing a fully automated selective two-option foraging device to specifically control which actions lead to a food reward and which remain unrewarded. Selective devices were based on a sliding-door foraging puzzle, and built using commercially available low-cost electronics. We tested it on two free-ranging PIT-tagged subpopulations of great tits Parus major as a proof of concept. We conducted a diffusion experiment where birds learned from trained demonstrators to get a food reward by sliding the door either to the left or right. We then restricted access of knowledgeable birds to their less preferred side and calculated the latency until birds produced solutions as a measure of behavioural flexibility. A total of 22 of 23 knowledgeable birds produced at least one solution on their less preferred side after being restricted, with higher-frequency solvers being faster at doing so. In addition, 18 of the 23 birds reached their solving rate from prior to the restriction on their less preferred side, with birds with stronger prior side preference taking longer to do so. We therefore introduce and successfully test a new selective two-option puzzle box, providing detailed instructions and freely available software that allows reproducibility. It extends the functionality of existing systems by allowing fine-scale manipulations of individuals' actions and opens a large range of possibilities to study cognitive processes in wild animal populations.

Monitoring Bivalve Behavior and Physiology in the Laboratory and Field Using Open Source Tools

Bivalve molluscs have been the focus of behavioral and physiological studies for over a century, due in part to the relative ease with which their traits can be observed. I review historical methods for monitoring behavior and physiology in bivalves and how modern methods with electronic sensors can allow for a number of parameters to be measured in a variety of conditions using low cost components and open source tools. Open source hardware and software tools can allow researchers to design and build custom monitoring systems to sample organismal processes and the environment, systems that can be tailored to the particular needs of a research program. The ability to leverage shared hardware and software can streamline the development process, providing greater flexibility to researchers looking to expand the number of traits they can measure, the frequency and duration of sampling, and the number of replicate devices they can afford to deploy.

Open Hardware in Science: The Benefits of Open Electronics

Openly shared low-cost electronic hardware applications, known as open electronics, have sparked a new open-source movement, with much untapped potential to advance scientific research. Initially designed to appeal to electronic hobbyists, open electronics have formed a global “maker” community and are increasingly used in science and industry. In this perspective article, we review the current costs and benefits of open electronics for use in scientific research ranging from the experimental to the theoretical sciences. We discuss how user-made electronic applications can help (I) individual researchers, by increasing the customization, efficiency, and scalability of experiments, while improving data quantity and quality; (II) scientific institutions, by improving access to customizable high-end technologies, sustainability, visibility, and interdisciplinary collaboration potential; and (III) the scientific community, by improving transparency and reproducibility, helping decouple research capacity from funding, increasing innovation, and improving collaboration potential among researchers and the public. We further discuss how current barriers like poor awareness, knowledge access, and time investments can be resolved by increased documentation and collaboration, and provide guidelines for academics to enter this emerging field. We highlight that open electronics are a promising and powerful tool to help scientific research to become more innovative and reproducible and offer a key practical solution to improve democratic access to science.

Computational bioacoustics with deep learning: a review and roadmap

Animal vocalisations and natural soundscapes are fascinating objects of study, and contain valuable evidence about animal behaviours, populations and ecosystems. They are studied in bioacoustics and ecoacoustics, with signal processing and analysis an important component. Computational bioacoustics has accelerated in recent decades due to the growth of affordable digital sound recording devices, and to huge progress in informatics such as big data, signal processing and machine learning. Methods are inherited from the wider field of deep learning, including speech and image processing. However, the tasks, demands and data characteristics are often different from those addressed in speech or music analysis. There remain unsolved problems, and tasks for which evidence is surely present in many acoustic signals, but not yet realised. In this paper I perform a review of the state of the art in deep learning for computational bioacoustics, aiming to clarify key concepts and identify and analyse knowledge gaps. Based on this, I offer a subjective but principled roadmap for computational bioacoustics with deep learning: topics that the community should aim to address, in order to make the most of future developments in AI and informatics, and to use audio data in answering zoological and ecological questions.

A Portable Device for the Measurement of Venous Pulse Wave Velocity

Pulse wave velocity in veins (vPWV) has recently been reconsidered as a potential index of vascular filling, which may be valuable in the clinic for fluid therapy. The measurement requires that an exogenous pressure pulse is generated in the venous blood stream by external pneumatic compression. To obtain optimal measure repeatability, the compression is delivered synchronously with the heart and respiratory activity. We present a portable prototype for the assessment of vPWV based on the PC board Raspberry Pi and equipped with an A/D board. It acquires respiratory and ECG signals, and the Doppler shift from the ultrasound monitoring of blood velocity from the relevant vein, drives the pneumatic cuff inflation, and returns multiple measurements of vPWV. The device was tested on four healthy volunteers (2 males, 2 females, age 33±13 years), subjected to the passive leg raising (PLR) manoeuvre simulating a transient increase in blood volume. Measurement of vPWV in the basilic vein exhibited a low coefficient of variation (3.6±1.1%), a significant increase during PLR in all subjects, which is consistent with previous findings. This device allows for carrying out investigations in hospital wards on different patient populations as necessary to assess the actual clinical potential of vPWV.

The Potential for Physiological Performance Curves to Shape Environmental Effects on Social Behavior

As individual animals are exposed to varying environmental conditions, phenotypic plasticity will occur in a vast array of physiological traits. For example, shifts in factors such as temperature and oxygen availability can affect the energy demand, cardiovascular system, and neuromuscular function of animals that in turn impact individual behavior. Here, we argue that nonlinear changes in the physiological traits and performance of animals across environmental gradients—known as physiological performance curves—may have wide-ranging effects on the behavior of individual social group members and the functioning of animal social groups as a whole. Previous work has demonstrated how variation between individuals can have profound implications for socially living animals, as well as how environmental conditions affect social behavior. However, the importance of variation between individuals in how they respond to changing environmental conditions has so far been largely overlooked in the context of animal social behavior. First, we consider the broad effects that individual variation in performance curves may have on the behavior of socially living animals, including: (1) changes in the rank order of performance capacity among group mates across environments; (2) environment-dependent changes in the amount of among- and within-individual variation, and (3) differences among group members in terms of the environmental optima, the critical environmental limits, and the peak capacity and breadth of performance. We then consider the ecological implications of these effects for a range of socially mediated phenomena, including within-group conflict, within- and among group assortment, collective movement, social foraging, predator-prey interactions and disease and parasite transfer. We end by outlining the type of empirical work required to test the implications for physiological performance curves in social behavior.

Raspberry Pi nest cameras: An affordable tool for remote behavioral and conservation monitoring of bird nests

Bespoke (custom-built) Raspberry Pi cameras are increasingly popular research tools in the fields of behavioral ecology and conservation, because of their comparative flexibility in programmable settings, ability to be paired with other sensors, and because they are typically cheaper than commercially built models.Here, we describe a novel, Raspberry Pi-based camera system that is fully portable and yet weatherproof-especially to humidity and salt spray. The camera was paired with a passive infrared sensor, to create a movement-triggered camera capable of recording videos over a 24-hr period. We describe an example deployment involving "retro-fitting" these cameras into artificial nest boxes on Praia Islet, Azores archipelago, Portugal, to monitor the behaviors and interspecific interactions of two sympatric species of storm-petrel (Monteiro's storm-petrel Hydrobates monteiroi and Madeiran storm-petrel Hydrobates castro) during their respective breeding seasons.Of the 138 deployments, 70% of all deployments were deemed to be "Successful" (Successful was defined as continuous footage being recorded for more than one hour without an interruption), which equated to 87% of the individual 30-s videos. The bespoke cameras proved to be easily portable between 54 different nests and reasonably weatherproof (~14% of deployments classed as "Partial" or "Failure" deployments were specifically due to the weather/humidity), and we make further trouble-shooting suggestions to mitigate additional weather-related failures.Here, we have shown that this system is fully portable and capable of coping with salt spray and humidity, and consequently, the camera-build methods and scripts could be applied easily to many different species that also utilize cavities, burrows, and artificial nests, and can potentially be adapted for other wildlife monitoring situations to provide novel insights into species-specific daily cycles of behaviors and interspecies interactions.

A Custom Sensor Network for Autonomous Water Quality Assessment in Fish Farms

The control of water quality is crucial to ensure the survival of fish in aquaculture production facilities. Today, the combination of sensors with communication technologies permits to monitor these crucial parameters in real-time, allowing to take fast management decisions. However, out-of-the-box solutions are expensive, due to the small market and the industrial nature of sensors, besides being little customizable. To solve this, the present work describes a low-cost hardware and software architecture developed to achieve the autonomous water quality assessment and management on a remote facility for fish conservation aquaculture within the framework of the Smart Comunidad Rural Digital (smartCRD) project. The developed sensor network has been working uninterruptedly since its installation (20 April 2021). It is based on open source technology and includes a central gateway for on-site data monitoring of water quality nodes as well as an online management platform for data visualization and sensor network configuration. Likewise, the system can detect autonomously water quality parameters outside configurable thresholds and deliver management alarms. The described architecture, besides low-cost, is highly customizable, compatible with other sensor network projects, machine-learning applications, and is capable of edge computing. Thus, it contributes to making open sensorization more accessible to real-world applications.