VoDEx: a Python library for time annotation and management of volumetric functional imaging data.

SUMMARY: In functional imaging studies, accurately synchronizing the time course of experimental manipulations and stimulus presentations with resulting imaging data is crucial for analysis. Current software tools lack such functionality, requiring manual processing of the experimental and imaging data, which is error-prone and potentially non-reproducible. We present VoDEx, an open-source Python library that streamlines the data management and analysis of functional imaging data. VoDEx synchronizes the experimental timeline and events (e.g. presented stimuli, recorded behavior) with imaging data. VoDEx provides tools for logging and storing the timeline annotation, and enables retrieval of imaging data based on specific time-based and manipulation-based experimental conditions. AVAILABILITY AND IMPLEMENTATION: VoDEx is an open-source Python library and can be installed via the "pip install" command. It is released under a BSD license, and its source code is publicly accessible on GitHub (https://github.com/LemonJust/vodex). A graphical interface is available as a napari-vodex plugin, which can be installed through the napari plugins menu or using "pip install." The source code for the napari plugin is available on GitHub (https://github.com/LemonJust/napari-vodex). The software version at the time of submission is archived at Zenodo (version v1.0.18, https://zenodo.org/record/8061531).

VoDEx: a Python library for time annotation and management of volumetric functional imaging data.

In functional imaging studies, accurately synchronizing the time course of experimental manipulations and stimulus presentations with resulting imaging data is crucial for analysis. Current software tools lack such functionality, requiring manual processing of the experimental and imaging data, which is error-prone and potentially non-reproducible. We present VoDEx, an open-source Python library that streamlines the data management and analysis of functional imaging data. VoDEx synchronizes the experimental timeline and events (eg. presented stimuli, recorded behavior) with imaging data. VoDEx provides tools for logging and storing the timeline annotation, and enables retrieval of imaging data based on specific time-based and manipulation-based experimental conditions. Availability and Implementation: VoDEx is an open-source Python library and can be installed via the "pip install" command. It is released under a BSD license, and its source code is publicly accessible on GitHub https://github.com/LemonJust/vodex. A graphical interface is available as a napari-vodex plugin, which can be installed through the napari plugins menu or using "pip install." The source code for the napari plugin is available on GitHub https://github.com/LemonJust/napari-vodex.

Quantity as a Fish Views It: Behavior and Neurobiology.

An ability to estimate quantities, such as the number of conspecifics or the size of a predator, has been reported in vertebrates. Fish, in particular zebrafish, may be instrumental in advancing the understanding of magnitude cognition. We review here the behavioral studies that have described the ecological relevance of quantity estimation in fish and the current status of the research aimed at investigating the neurobiological bases of these abilities. By combining behavioral methods with molecular genetics and calcium imaging, the involvement of the retina and the optic tectum has been documented for the estimation of continuous quantities in the larval and adult zebrafish brain, and the contributions of the thalamus and the dorsal-central pallium for discrete magnitude estimation in the adult zebrafish brain. Evidence for basic circuitry can now be complemented and extended to research that make use of transgenic lines to deepen our understanding of quantity cognition at genetic and molecular levels.

Regional synapse gain and loss accompany memory formation in larval zebrafish.

Defining the structural and functional changes in the nervous system underlying learning and memory represents a major challenge for modern neuroscience. Although changes in neuronal activity following memory formation have been studied [B. F. Grewe et al., Nature 543, 670-675 (2017); M. T. Rogan, U. V. Stäubli, J. E. LeDoux, Nature 390, 604-607 (1997)], the underlying structural changes at the synapse level remain poorly understood. Here, we capture synaptic changes in the midlarval zebrafish brain that occur during associative memory formation by imaging excitatory synapses labeled with recombinant probes using selective plane illumination microscopy. Imaging the same subjects before and after classical conditioning at single-synapse resolution provides an unbiased mapping of synaptic changes accompanying memory formation. In control animals and animals that failed to learn the task, there were no significant changes in the spatial patterns of synapses in the pallium, which contains the equivalent of the mammalian amygdala and is essential for associative learning in teleost fish [M. Portavella, J. P. Vargas, B. Torres, C. Salas, Brain Res. Bull 57, 397-399 (2002)]. In zebrafish that formed memories, we saw a dramatic increase in the number of synapses in the ventrolateral pallium, which contains neurons active during memory formation and retrieval. Concurrently, synapse loss predominated in the dorsomedial pallium. Surprisingly, we did not observe significant changes in the intensity of synaptic labeling, a proxy for synaptic strength, with memory formation in any region of the pallium. Our results suggest that memory formation due to classical conditioning is associated with reciprocal changes in synapse numbers in the pallium.

Single-objective selective-volume illumination microscopy enables high-contrast light-field imaging.

The performance of light-field microscopy is improved by selectively illuminating the relevant subvolume of the specimen with a second objective lens. Here we advance this approach to a single-objective geometry, using an oblique one-photon illumination path or two-photon illumination to accomplish selective-volume excitation. The elimination of the second orthogonally oriented objective to selectively excite the volume of interest simplifies specimen mounting; yet, this single-objective approach still reduces the out-of-volume background, resulting in improvements in image contrast, effective resolution, and volume reconstruction quality. We validate our new, to the best of our knowledge, approach through imaging live developing zebrafish, demonstrating the technology's ability to capture imaging data from large volumes synchronously with high contrast while remaining compatible with standard microscope sample mounting.

Charged particle separation by an electrically tunable nanoporous membrane.

We study the applicability of an electrically tunable nanoporous semiconductor membrane for the separation of nanoparticles by charge. We show that this type of membrane can overcome one of the major shortcomings of nanoporous membrane applications for particle separation: the compromise between membrane selectivity and permeability. The computational model that we have developed describes the electrostatic potential distribution within the system and tracks the movement of the filtered particle using Brownian dynamics while taking into consideration effects from dielectrophoresis, fluid flow, and electric potentials. We found that for our specific pore geometry, the dielectrophoresis plays a negligible role in the particle dynamics. By comparing the results for charged and uncharged particles, we show that for the optimal combination of applied electrolyte and membrane biases the same membrane can effectively separate same-sized particles based on charge with a difference of up to 3 times in membrane permeability.

Filtering of nanoparticles with tunable semiconductor membranes.

Translocation dynamics of nanoparticles permeating through the nanopore in an n-Si semiconductor membrane is studied. With the use of Brownian Dynamics to describe the motion of the charged nanoparticles in the self-consistent membrane-electrolyte electrostatic potential, we asses the possibility of using our voltage controlled membrane for the macroscopic filtering of the charged nanoparticles. The results indicate that the tunable local electric field inside the membrane can effectively control interaction of a nanoparticle with the nanopore by either blocking its passage or increasing the translocation rate. The effect is particularly strong for larger nanoparticles due to their stronger interaction with the membrane while in the nanopore. By extracting the membrane permeability from our microsopic simulations, we compute the macroscopic sieving factors and show that the size selectivity of the membrane can be tuned by the applied voltage.