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Detection and analysis of spontaneous synaptic events is an extremely common task in many neuroscience research labs. Various algorithms and tools have been developed over the years to improve the sensitivity of detecting synaptic events. However, the final stages of most procedures for detecting synaptic events still involve the manual selection of candidate events. This step in the analysis is laborious and requires care and attention to maintain consistency of event selection across the whole dataset. Manual selection can introduce bias and subjective selection criteria that cannot be shared with other labs in reporting methods. To address this, we have created Eventer, a standalone application for the detection of spontaneous synaptic events acquired by electrophysiology or imaging. This open-source application uses the freely available MATLAB Runtime and is deployed on Mac, Windows, and Linux systems. The principle of the Eventer application is to learn the user's “expert” strategy for classifying a set of detected event candidates from a small subset of the data and then automatically apply the same criterion to the remaining dataset. Eventer first uses a suitable model template to pull out event candidates using fast Fourier transform (FFT)-based deconvolution with a low threshold. Random forests are then created and trained to associate various features of the events with manual labeling. The stored model file can be reloaded and used to analyse large datasets with greater consistency. The availability of the source code and its user interface provide a framework with the scope to further tune the existing Random Forest implementation, or add additional, artificial intelligence classification methods. The Eventer website (https://eventerneuro.netlify.app/) includes a repository where researchers can upload and share their machine learning model files and thereby provide greater opportunities for enhancing reproducibility when analyzing datasets of spontaneous synaptic activity. In summary, Eventer, and the associated repository, could allow researchers studying synaptic transmission to increase throughput of their data analysis and address the increasing concerns of reproducibility in neuroscience research.

Free and open-source hardware, 3D printing, and the use of locally sourced materials can be valuable tools for local problem solving, as proven by the production of more than 400 reusable face shields and masks in a Nigerian community to bypass PPE shortages during the COVID-19 pandemic.

With the current rapid spread of COVID-19, global health systems are increasingly overburdened by the sheer number of people that need diagnosis, isolation and treatment. Shortcomings are evident across the board, from staffing, facilities for rapid and reliable testing to availability of hospital beds and key medical-grade equipment. The scale and breadth of the problem calls for an equally substantive response not only from frontline workers such as medical staff and scientists, but from skilled members of the public who have the time, facilities and knowledge to meaningfully contribute to a consolidated global response. Here, we summarise community-driven approaches based on Free and Open Source scientific and medical Hardware (FOSH) as well as personal protective equipment (PPE) currently being developed and deployed to support the global response for COVID-19 prevention, patient treatment and diagnostics.

The first edition of the Aspects of Neuroscience Brainhack took place at the Department of Physics at University of Warsaw, Poland between November 17th and 19th 2017. This hackathon was one of the satellite events of the Aspects of Neuroscience conference, it was organized by the Brainhack organization to promote interaction between researchers, encouraging open (neuro)science and collaborations on projects related to the study of the nervous system. The event had a total of nine projects on many different topics including functional connectivity research, white matter tractography, classification of brain-ageing biomarkers through machine learning, presentation of a portable one channel EEG registration device and a do it yourself 3D-printed neurobiology lab. The latter is highlighted in this paper.

Small, genetically tractable species such as larval zebrafish, Drosophila, or Caenorhabditis elegans have become key model organisms in modern neuroscience. In addition to their low maintenance costs and easy sharing of strains across labs, one key appeal is the possibility to monitor single or groups of animals in a behavioural arena while controlling the activity of select neurons using optogenetic or thermogenetic tools. However, the purchase of a commercial solution for these types of experiments, including an appropriate camera system as well as a controlled behavioural arena, can be costly. Here, we present a low-cost and modular open-source alternative called 'FlyPi'. Our design is based on a 3D-printed mainframe, a Raspberry Pi computer, and high-definition camera system as well as Arduino-based optical and thermal control circuits. Depending on the configuration, FlyPi can be assembled for well under €100 and features optional modules for light-emitting diode (LED)-based fluorescence microscopy and optogenetic stimulation as well as a Peltier-based temperature stimulator for thermogenetics. The complete version with all modules costs approximately €200 or substantially less if the user is prepared to 'shop around'. All functions of FlyPi can be controlled through a custom-written graphical user interface. To demonstrate FlyPi's capabilities, we present its use in a series of state-of-the-art neurogenetics experiments. In addition, we demonstrate FlyPi's utility as a medical diagnostic tool as well as a teaching aid at Neurogenetics courses held at several African universities. Taken together, the low cost and modular nature as well as fully open design of FlyPi make it a highly versatile tool in a range of applications, including the classroom, diagnostic centres, and research labs.

[This corrects the article DOI: 10.1371/journal.pbio.1002086.].

The introduction of affordable, consumer-oriented 3-D printers is a milestone in the current "maker movement," which has been heralded as the next industrial revolution. Combined with free and open sharing of detailed design blueprints and accessible development tools, rapid prototypes of complex products can now be assembled in one's own garage--a game-changer reminiscent of the early days of personal computing. At the same time, 3-D printing has also allowed the scientific and engineering community to build the "little things" that help a lab get up and running much faster and easier than ever before.

Movement is a key feature of animal systems, yet its embryonic origins are not fully understood. Here, we investigate the genetic basis underlying the embryonic onset of movement in Drosophila focusing on the role played by small non-coding RNAs (microRNAs, miRNAs). To this end, we first develop a quantitative behavioural pipeline capable of tracking embryonic movement in large populations of fly embryos, and using this system, discover that the Drosophila miRNA miR-2b-1 plays a role in the emergence of movement. Through the combination of spectral analysis of embryonic motor patterns, cell sorting and RNA in situs, genetic reconstitution tests, and neural optical imaging we define that miR-2b-1 influences the emergence of embryonic movement by exerting actions in the developing nervous system. Furthermore, through the combination of bioinformatics coupled to genetic manipulation of miRNA expression and phenocopy tests we identify a previously uncharacterised (but evolutionarily conserved) chloride channel encoding gene – which we term Movement Modulator (Motor) – as a genetic target that mechanistically links miR-2b-1 to the onset of movement. Cell-specific genetic reconstitution of miR-2b-1 expression in a null miRNA mutant background, followed by behavioural assays and target gene analyses, suggest that miR-2b-1 affects the emergence of movement through effects in sensory elements of the embryonic circuitry, rather than in the motor domain. Our work thus reports the first miRNA system capable of regulating embryonic movement, suggesting that other miRNAs are likely to play a role in this key developmental process in Drosophila as well as in other species.

The field of 3D bioengineering proposes to effectively contribute to the manufacture of artificial multicellular organ/tissues and the understanding of complex cellular mechanisms. In this regard, 3D cell cultures comprise a promising bioengineering possibility for the alternative treatment of organ function loss, potentially improving patient life expectancies. Patients with end-stage disease, for example, could benefit from treatment until organ transplantation or even undergo organ function restoration. Currently, 3D bioprinters can produce tissues such as trachea cartilage or artificial skin. Most low-cost 3D bioprinters are built from fused deposition modeling 3D printer frames modified for the deposition of biologically compatible material, ranging between $13.000,00 and $300.000,00. Furthermore, the cost of consumables should also be considered as they, can range from $3,85 and $100.000,00 per gram, making biomaterials expensive, hindering bioprinting access. In this context, our report describes the first prototype of a significantly low-cost 3D bioprinter built from recycled scrap metal and off-the-shelf electronics. We demonstrate the functionalized process and methodology proof of concept and aim to test it in different biological tissue scaffolds in the future, using affordable materials and open-source methodologies, thus democratizing the state of the art of this technology.

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.

Visual neuroscientists require accurate control of visual stimulation. However, few stimulator solutions simultaneously offer high spatio-temporal resolution and free control over the spectra of the light sources, because they rely on off-the-shelf technology developed for human trichromatic vision. Importantly, consumer displays fail to drive UV-shifted short wavelength-sensitive photoreceptors, which strongly contribute to visual behaviour in many animals, including mice, zebrafish and fruit flies. Moreover, many non-mammalian species feature more than three spectral photoreceptor types. Here, we present a flexible, spatial visual stimulator with up to six arbitrary spectrum chromatic channels. It combines a standard digital light processing engine with open source hard- and software that can be easily adapted to the experimentalist’s needs. We demonstrate the capability of this general visual stimulator experimentally in the in vitro mouse retinal whole-mount and the in vivo zebrafish. With this work, we intend to start a community effort of sharing and developing a common stimulator design for vision research.

Many efforts are making science more open and accessible; they are mostly concentrated on issues that appear before and after experiments are performed: open access journals, open databases, and many other tools to increase reproducibility of science and access to information. However, these initiatives do not promote access to scientific equipment necessary for experiments. Mostly due to monetary constraints, equipment availability has always been uneven around the globe, affecting predominantly low-income countries and institutions. Here, a case is made for the use of free open source hardware in research and education, including countries and institutions where funds were never the biggest problem.

Generalized linear models (GLMs) represent a popular choice for the probabilistic characterization of neural spike responses. While GLMs are attractive for their computational tractability, they also impose strong assumptions and thus only allow for a limited range of stimulus-response relationships to be discovered. Alternative approaches exist that make only very weak assumptions but scale poorly to high-dimensional stimulus spaces. Here we seek an approach which can gracefully interpolate between the two extremes. We extend two frequently used special cases of the GLM-a linear and a quadratic model-by assuming that the spike-triggered and non-spike-triggered distributions can be adequately represented using Gaussian mixtures. Because we derive the model from a generative perspective, its components are easy to interpret as they correspond to, for example, the spike-triggered distribution and the interspike interval distribution. The model is able to capture complex dependencies on high-dimensional stimuli with far fewer parameters than other approaches such as histogram-based methods. The added flexibility comes at the cost of a non-concave log-likelihood. We show that in practice this does not have to be an issue and the mixture-based model is able to outperform generalized linear and quadratic models.

α-Synuclein (αSYN) is genetically and neuropathologically linked to a spectrum of neurodegenerative diseases including Parkinson's disease, dementia with Lewy bodies, and related disorders. Cognitive impairment is recapitulated in several αSYN transgenic mouse lines. However, the mechanisms of dysfunction in affected neurons are largely unknown. Here we measured neuronal activity induced gene products in the limbic system of αSYN transgenic mice upon fear conditioning (FC). Induction of the synaptic plasticity marker c-Fos was significantly reduced in the amygdala and hippocampus of (Thy1)-h[A30P]αSYN transgenic mice in an age-dependent manner. Similarly, the neuronal activity inducible polo-like kinase 2 (Plk2) that can phosphorylate αSYN at the pathological site serine-129 was up-regulated in both brain regions upon FC. Plk2 inductions were also significantly impaired in aged (Thy1)-h[A30P]αSYN transgenic mice, both in the amygdala and hippocampus. Plk2 inductions in the amygdala after FC were paralleled by a small but significant increase in the number of neuronal cell bodies immunopositive for serine-129 phosphorylated αSYN in young but not aged (Thy1)-h[A30P]αSYN transgenic mice. In addition, we observed in the aged hippocampus a distinct type of apparently unmodified transgenic αSYN profiles resembling synaptic accumulations of αSYN. Thus, the cognitive decline observed in aged αSYN transgenic mice might be due to impairment of neurotransmission and synaptic plasticity in the limbic system by distinct αSYN species.