The work reported here has focused on addressing two issues: which species will puncture intact fruit skin and the morphological evolution of the ovipositor. Future research, carried out in a comparative context, may lead to more insights on both the agricultural and evolutionary implications of our findings. A thorough analysis of any interspecific differences in both adult and larval survivability, for example, and whether larval development in the fruit pulp varies depending on whether the egg was laid in an exposed area or through a skin puncture, will aid in the assessment of the agricultural threat, including the risk of secondary invasions . Behavioural studies of egg-laying attempts will help to determine the extent to which the fly’s propensity to oviposit in intact skin has coevolved with ovipositor morphology. In our own preliminary observations of the flies’ behaviour when trying to oviposit, we have observed that flies stab repeatedly at fruit skin with their ovipositors, apparently attempting to lay an egg, but sometimes only succeeding after numerous failed attempts. Detailed analyses of both successes and failures may help to resolve the issue of whether the failure to puncture the skin is owing to morphological limitations of the ovipositor or an aversion to the skin. One model of adaptive evolution, dating back to George Gaylord Simpson, posits that a key innovation can provide a novel ecological opportunity leading to diversification. However, we have knowledge of only one other closely related species, Drosphila pulchrella, blueberry pot size with putatively enlarged thorn bristles . Therefore, there is little evidence in this case of an ‘adaptive radiation’ of species following the innovation, as the classic model would posit.
It would appear to be surprising for an innovation that confers such a strong adaptive advantage to be rare. The pattern in Drosophilidae makes for an instructive contrast with another lineage, the fly superfamily Tephritoidea, where the evolution of an ovipositor optimized for piercing tough plant surfaces spawned a radiation of thousands of species. Ovipositor modification has also played a key role in the natural history of other insect taxa, including the aculeate Hymeonoptera, where the organ has evolved into a sting . The species we have analysed here, however, are much more closely related to Drosophila melanogasterthan any other cases where the ecological importance of ovipositor evolution has been demonstrated, facilitating the transfer of the vast array of resources developed for themodel fruitfly. Analysis of the developmental genetics of ovipositor ontogeny, and interspecific comparisons of this genetic circuitry, along with new genomic resources , may shed light on how the structure can be evolutionarily co-opted. It is possible that this system could become a promising model for evolutionary developmental biology. We have reported here an assessment of the potential of a Drosophila species other than D. suzukii to puncture fruit skin. We have found that while the capabilities of D. subpulchrella differ from those of D. suzukii, it nonetheless has the ability— in apparent contrast to more distant relatives of D. suzukii—to penetrate the exterior of ripening raspberries and cherries. As little is known of the ecology of this species, it is difficult to know whether it has invasive potential. Vast resources have been devoted to the study of D. suzukii in recent years, with no comparable resources being given to D. subpulchrella research.
Our results suggest that applying an evolutionary framework in pest management is both prudent and practical.The COVID-19 pandemic has changed the work landscape throughout the world. Wherever possible jobs have transitioned to a remote format in compliance with lockdown regulations. Bench scientists were unequally affected by this pandemic, experiencing a substantial reduction in their ability to work compared to computational scientists. This situation will likely have long-lasting effects on science careers, particularly for junior investigators. A silver lining may be the development of new approaches that allow experimental scientists to work remotely. These are likely to have lasting benefit long after the pandemic. We describe one such approach here. In addition to allowing a greater quantity of work to be done, remote experimentation and the automation required to implement it can increase the quality of work coming out of a lab. There is a “crisis of reproducibility” that exists within biology. Scientists and technicians following prescribed protocols are often unable to replicate each other’s results. For example, in cell culture experiments, differences in how often controlled temperature and controlled gas incubators are opened or how much time a sample spends out of the incubator for routine manipulation can cause varying amounts of stress on the culture, affecting the metabolism and the experimental results. This leads to unaccounted for experimental variability. Increasing automation in lab experiments has been proposed as a way to address this issue. Remotely operated experimentation entails such increased automation. Techniques for remote operation exist on a wide spectrum of cost and complexity, from fully automated labs utilizing expensive robotic systems, to DIY 3D printed microscopes with basic Internet access. The further development of low cost solutions for remote lab control will bring more options within the reach of institutions with limited resources, allowing even labs in underprivileged environments to enjoy many of the benefits of the “lab of the future”.
Many cost reductions have been made possible by the numerous innovations in the Internet of Things space, ranging from frameworks to low cost network capable devices. There is an active community of DIY enthusiasts creating designs to manufacture lab equipment using consumer accessible tools . Several open source microscope designs have been proposed using 3D printing and low cost computing platforms like the Raspberry Pi. Starting as low as 5$ for a fully featured computer capable of running a desktop version of Linux, the Raspberry Pi allows scientists to use dedicated clusters of computers in virtually any application. Beyond research applications, remote and simulated lab systems have found use in educational environments . Simulated labs have been used as a replacement for, or supplement to, traditional educational lab experience with the aim of introducing students without access to the necessary experimental equipment and environment to the experience of the scientific process. However, simulations can never provide students with the experience of actually discovering something new. Remote lab experimentation allows students to manipulate live experiments running on real lab equipment from their classroom and home computers, or from their mobile phones. This removes the stale predictability of fully simulated experimentation, plant raspberry in container giving students a chance to experience the actual scientific process of discovery. Remote microscopy is an important aspect of many of these remote lab experiments. We recently described a device for simultaneous longitudinal imaging which we call the “Picroscope”. Here we describe the software and network architecture developed to run the device as well as its integration into an IoT system on the cloud. This system enables adjustment of imaging parameters without disturbing the samples. It also allows researchers to monitor their experiment remotely enabling a variety of remote biology applications. The Picroscope system is comprised of a cluster of network connected devices. A pipeline was developed to facilitate remote operation and to control communication between modules in the system. The result is a web based interface that allows users to control a longitudinal imaging experiment and view results in near real time. This brings high throughput parallel remote microscopy to a price point affordable in many sectors that could not previously access such systems. The 3D z-stack image data captured by the system allows it to image both 2D monolayer cell cultures and 3D samples. Our data pipeline is capable of feeding these z-stacks into software that generates Extended Depth of Field composite images to simplify the end user’s visual analysis of longitudinal changes in a 3D sample. In this paper we demonstrate the system’s functionality with frog embryos, zebrafish, and human cerebral cortex organoids.The Picroscope contains several custom boards and 3D printed pieces. These are shown in figure 1, the main pieces include the 24 well plate holder, the elevator stage camera array, and the LED illumination boards .
The camera array consists of a 6×4 grid of sensors with m12 threaded objective lenses attached. Two stepper motors are used to raise and lower the camera stage in order to move the focal plane. More detail on the hardware design of the picroscope can be found in [29].The basic workflow for this system is illustrated in figure 2). Experiments are triggered through our web based control console . In the console, the user sets the following parameters: experiment id, number of pictures in z-stack, distance between layers, initial offset distance, light type , and any additional camera control parameters allowed through the raspistill library. These parameters are passed to the Picroscope through a cloud based messaging service using theMQTT protocol. Experiment parameters can also be changed on the fly during the course of an experiment through the same console.At the conclusion of each captured event, the Picroscope uploads the results to an S3 Object Store on a server where the pictures become accessible through our image viewer website . Even though all 24 cameras share a single z-stack adjustment to reduce the cost of the device, the image viewer interface functionally provides users with 24 independent virtual microscopes. This allows users individual control with near real time views of the contents of each well at different z-stack focal layers. This is a big cost savings over having a classroom setup with 24 independent microscopes, for example. Students have the experience of controlling their own independent microscope from their mobile phone. This illustrates a unique advantage of computer interfaced remote experimentation.The flow of messages and data in our pipeline is represented in Figure 5. The control console webpage communicates with our system using the MQTT message protocol. MQTT is a publish/subscribe based protocolin which a message “broker” transfers any messages published on a given topic to all the subscribers of that topic. To pass messages from the webpage to the picroscope, we use a cloud based MQTT broker provided by Amazon IoT. Every picroscope has a unique id and “device shadow” on the Amazon IoT platform. The control console website has access to the device list through Amazon’s IoT API. The console displays a list of all active systems with buttons to control each one . When a command is sent from the console it’s published with the topic being the id of the picroscope we wish to control. The targeted picroscope receives the start command along with the desired experiment parameters. Parameters can be adjusted on the fly from the control console website. Each picroscope then uses it’s own locally hosted MQTT broker as a message bus to pass commands to the 24 raspberry pi zero Ws that each control a camera. Each hub also connects through USB to an Arduino Uno which is responsible for controlling the motors and lights as well as temperature safety monitoring and emergency shutoff. Commands to take a picture can be sent to individual cameras or all of them at once. When a camera finishes taking a picture, it sends a message back to the hub with its camera id, allowing the hub to know when all cameras have finished. Unused wells can be disabled by the hub, allowing a higher maximum throughput for the other enabled cameras.When a z-stack capture concludes, the pi zeros need to send their data to their assigned hub pi. To accomplish this, we use a custom queuing protocol that initiates file transfers individually on each pi zero W and continues to the next pi when the current transfer finishes or a timeout condition is reached. The protocol is detailed in figure 6. This queuing system results in higher throughput than simply starting all transfers in parallel and the queue is not disrupted in the case of non-responsive pi zeros. When the data transfer completes, the result is uploaded to an s3 object store on cloud hardware run on the Pacific Research Platform. The time required for the transfer to complete is the primary limiting factor in determining the maximum data capture frequency we can achieve. Transfer time is primarily determined by z-stack size and number of active cameras.