Data/Software for "Presynaptic Mitochondria Volume and Abundance Increase During Development of a High-Fidelity Synapse"
Contributors: Connon I. Thomas, Christian Keine, Satoko Okayama, Rachel Satterfield, Morgan Musgrove, Debbie Guerrero-Given, Naomi Kamasawa, Samuel M. Young, Jr.
... Contains data and software from the publication: "Presynaptic Mitochondria Volume and Abundance Increase During Development of a High-Fidelity Synapse" published in the The Journal of Neuroscience (https://doi.org/10.1523/JNEUROSCI.0363-19.2019). The preprint to this data set has been published on bioRxiv (https://doi.org/10.1101/689653). In this study, we created a helper-dependent adenoviral vector (HdAd) to co-express cytoplasmic EGFP and a genetically encoded peroxidase marker (mito-APEX2) at the calyx of Held, an excellent model for deciphering regulatory mechanisms of presynaptic function. ABSTRACT: The calyx of Held, a large glutamatergic presynaptic terminal in the auditory brainstem undergoes developmental changes to support the high action-potential firing rates required for auditory information encoding. In addition, calyx terminals are morphologically diverse which impacts vesicle release properties and synaptic plasticity. Mitochondria influence synaptic plasticity through calcium buffering and are crucial for providing the energy required for synaptic transmission. Therefore, it has been postulated that mitochondrial levels increase during development and contribute to the morphological-functional diversity in the mature calyx. However, the developmental profile of mitochondrial volumes and subsynaptic distribution at the calyx of Held remains unclear. To provide insight on this, we developed a helper-dependent adenoviral vector (HdAd) that expresses the genetically encoded peroxidase marker for mitochondria, mito-APEX2, at the mouse calyx of Held. We developed protocols to detect labeled mitochondria for use with serial block face scanning electron microscopy to carry out semi-automated segmentation of mitochondria, high-throughput whole terminal reconstruction and presynaptic ultrastructure in mice of either sex. Subsequently, we measured mitochondrial volumes and subsynaptic distributions at the immature postnatal day 7 (P7) and the mature (P21) calyx. We found an increase of mitochondria volumes in terminals and axons from P7 to P21 but did not observe differences between stalk and swelling subcompartments in the mature calyx. Based on these findings, we propose that mitochondrial volumes and synaptic localization developmentally increase to support high firing rates required in the initial stages of auditory information processing. Data are sorted by the figures they appear in. Media (movies and 3D models) and custom-written software are located in separate folders.
Contributors: Massimo Salvi
... This repository contains the FAST algorithm graphical user interface and some sample image used in the following work: - Salvi M., Cerrato V., Buffo A., and Molinari F., "Automated Segmentation of Brain Cells for Clonal Analyses in Fluorescence Microscopy Images", J Neurosci Methods 2019 (DOI: 10.1016/j.jneumeth.2019.108348) ABSTRACT The understanding of how cell diversity within and across distinct brain regions is ontogenetically achieved is a pivotal topic in neuroscience. Clonal analyses based on multicolor cell labeling represent a powerful tool to tackle this issue and disclose lineage relationships, but produce enormous sets of fluorescence images, leading to time consuming analyses that may be biased by the operator’s subjectivity. Thus, time-efficient automated software are needed to analyze images easily, accurately and without subjective bias. In this paper, we present a fully automated method, named FAST (‘Fluorescent cell Analysis Segmentation Tool’), for the segmentation of neural cells labeled by multicolor combinations of fluorophores and for their classification into clones. The proposed method was tested on 77 high-magnification fluorescence images of adult mouse cerebellar tissues acquired using a confocal microscope. Automatic results were compared with manual annotations and two open-source software designed for cell detection in microscopic imaging. The algorithm showed very good performance in the cellular detection and in the assignment of the clonal identity. To the best of our knowledge, FAST is the first fully automated technique for the analysis of cellular clones based on combinatorial expression of fluorescent proteins. The proposed approach allows to perform clonal analyses easily, accurately and objectively, overcoming those biases and errors that may result from manual annotations. Moreover, it can be broadly applied to the quantification and colocalization within cells of fluorescent markers, therefore representing a versatile and powerful tool for automated quantitative analyses in fluorescence microscopy.
Contributors: Anders Thomsen, Morten Kristiansen, Ewa Kristiansen, Benny Endelt
... The data describes the measurement of a v-bend shape formed during multi-scan laser forming. The purpose of the measurements was to determine the dynamic response during laser forming of a v-bend. A measurement scanner was used to measure the height of a line perpendicular to the heating scan line of a laser during laser forming. In order to estimate a surface, 105 samples were made with identical settings with the measurement scanner moved along the heating scan line between samples. A total of 21 positions along the heating scan line were measured. Each position was measured using 5 samples. Due to a memory problem, the measurement scanner could only measure about 3.12 seconds at a time. The measurement scanner is started slightly before each heating scan line starts. Furthermore, each heating scan line is split into its own text file in the data set. This data set contains 21 folders, one for each position of the measurement scanner along the heating scan line. The folders are named as 'ymm', where y is the distance from the trailing edge of the heating scan line, '_' is used instead of decimals here. Each folder contains 30 text files, 6 for each of the samples used, structured as (x, y, z, t). Each file is named as 'sample_i_plate_j_scannumber_k.txt', where i is the sample number (1-5) at this position, j is the plate number (1 or 2), k is the scan number (1-6). Scan number 6 does not contain any heating, but is set as a final measurement of about 60 seconds after forming. Warning: The unzipped data fill 51.6 GB
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Contributors: Carlos Palacio
... Raw data for the internal Project 2016210 "Preparation and characterization of magnetic nanoparticles based in ferrites"--> UAN-UdeA.
Contributors: Prashant K. Jha, Robert Lipton
... We share the data used in publishing the article "Numerical convergence of finite difference approximations for state based peridynamic fracture models", see https://doi.org/10.1016/j.cma.2019.03.024. The data set comprises of raw data produced by computational code, post-processed files, and python script files. We consider finite difference approximation of a nonlinear state-based peridynamic model. We run simulation for two problems. In the first problem, we have a square domain with verticle pre-crack originating from the middle of the bottom edge. We apply a constant velocity boundary condition along the horizontal axis on the bottom layer. In response to the boundary conditions, the crack propagates vertically. The data correspond to three different horizons, 2mm, 4mm, and 8mm. For each horizon, we have three results, each corresponding to mesh size horizon/2, horizon/4, and horizon/8. From the approximate displacement fields, we compute the rate of convergence with respect to mesh size, for each fixed horizon. These are post-processed data and can be found in "postprocessing" folder of Example 1. In the second problem, we consider a rectangle domain which is supported at two regions (left and right) near the bottom edge. On the portion of the top edge, we apply a monotonically increasing in time force in the downward direction. We run simulation when the sample has just one vertical pre-crack originating from the middle of the bottom edge and when the sample has two vertical pre-cracks symmetrically located and originating from the bottom edge. We plot the damage at multiple times and show that the crack propagates upwards in response to applied load. All computations are carried out using an in-house developed code. In this data set, we have not shared the computational code. However, we plan on making the code public in the future. If you are interested in our code and if you have some collaborative ideas please feel free to get in touch.
Contributors: Talha Agcayazi
... This repository contains the supplement files for the HardwareX Journal titled: Multi-axis Stress Sensor Characterization and Testing Platform Authors: Talha Agcayazi*, Marc Foster, Hannah Kausche, Max Gordon, Alper Bozkurt Affiliations: Electric and Computer Engineering, North Carolina State University Contact email: [email@example.com] Abstract: Multi-Axis mechanical stress sensors have become an important tool in numerous fields. To characterize the quality and resolution of a new stress sensor, researchers use test platforms to apply controlled amounts of shear and normal stresses. Since commercially available multi-axis test platforms are expensive, in many cases researchers end up needing to build their own sensor characterization platforms. In this study, we describe a cost-effective multi-axis stress sensor characterization platform to reduce development time, enable accurate benchmarking and provide an open source standard for stress sensor characterization for other researchers. In our design, we combined a 3-axis translational stage and a 3-axis force and torque (F/T) sensor through custom hardware and software. Our platform is cost effective and can reach the limits of the F/T sensor (Fxy = ±65N and Fz = ±200N) without losing any accuracy to structural bending. We provide detailed construction and operation instructions as well as results of extensive static and cyclic load experiments. To further validate our system, we show characterization results from a custom stress sensor as a case study. The modular nature of our platform also enables other researchers to customize this characterization platform for their unique experimental requirements.
Contributors: Eduardo Fox, Li Chen, XiaoQing Wu
... The present dataset includes background information and files relative to the submitted paper " Venom Isosolenopsin A Delivers Rapid Knockdown of Fire Ant Competitors " currently under review in Toxicon. Currently it serves as a repository to files reviewers may wish to see. After publication it shall host all relevant files pertaining the final published version of the manuscript. It includes: - Original GC-MS chromatogram files obtained for natural venoms, calibration standards, synthetic alkaloids. - R script behind the plotted figures and data analyses; R data. - Supplementary videos and isolated images showing venom- and alkaloids- intoxicated ants.
Data for: A piezoelectric spring pendulum oscillator used for multi-directional and ultra-low frequency vibration energy harvesting
Contributors: Yipeng WU
... Please see the file name or the txt file called "readme".
Experimental results for the color segregation multi-robot task implemented on real and simulated Kilobots using P colonies and Finite State Machines
Contributors: Andrei George Florea, Catalin Buiu
... In this record we present an example use of a symbolic robot control model designed to control a swarm of real and simulated robots (in a distributed manner) using the free and open-source Lulu P colony simulator (https://github.com/andrei91ro/lulu_pcol_sim). The robots used in this experiment are Kilobots. This color segregation example considers a group of leader and follower robots and three posible target states: RED, GREEN, BLUE. The leaders always emit a single type of message (color) and color themselves using a single color. The followers receive messages and if the received color corresponds to their current color (or they are in the initial state), the followers hold their position, keep the RGB LED set to that color and emit the corresponding message. If the followers receive two different types of messages, they will move at random, until they receive messages from of a single type. The second control model, that is included here for comparison purposes, uses the concept of State-full Event-driven Finite State Machine (FSM) and the structure of this model is presented in 'fsm_diagram.png''. Details regarding the functioning of the P colony simulator (Lulu) and of the associated robot controller (Lulu_kilobot) can be found in: A. G. Florea, C. Buiu. Membrane Computing for Distributed Control of Robotic Swarms: Emerging Research and Opportunities, IGI Global, USA, ISBN13: 9781522522805, DOI: 10.4018/978-1-5225-2280-5, 2017 Included in this dataset are: * video files, sets of 10 experiments, grouped by the following six categories: a) using 16 real kilobots controlled using a P colony based model (video_kilobot_lulu_) b) using 16 real kilobots controlled using a FSM based model (video_kilobot_fsm_) c) using 16 simulated kilobots controlled using a P colony based model (video_kilombo_lulu_) d) using 16 simulated kilobots controlled using a FSM based model (video_kilombo_fsm_) e) using 50 simulated kilobots controlled using a P colony based model (video_kilombo_lulu_50_robots) f) using 50 simulated kilobots controlled using a FSM based model (video_kilombo_fsm_50_robots) * experiment data, for each experiment, for each of the previous six categories, grouped in a single ZIP archive (csv_data_and_R_script.zip) * The GNU R source file that can be used to process the experiment data, inside csv_data_and_R_script.zip
Contributors: Catalin Buiu, Andrei Florea
... In this record we present two distinct robot control models designed to control a swarm of robots (in a distributed manner) with the objective of moving the robots in a random direction as long as they have neighbors nearby. Once there are no more neighbor robots nearby, each robot is programmed to stop and color itself in white. The first control model uses P colonies and is described in detail in Florea, A. G., & Buiu, C. (2016). Development of a software simulator for P colonies. Applications in robotics. International Journal of Unconventional Computing, 12(2-3), 189–205. The second control model uses the concept of State-less Event-driven Finite State Machine and the structure of this model is presented in diagram.png. In the following sections, we present a small description for each of the attached videos. 1_clone_10_circle This video demonstrates the use of the robot cloning function of the vrep_bridge script in order to create 9 distinct copies of the source robot and distribute them on a circle around the source robot. The copies are so positioned by a distribution function that can be adapted to other forms. This cloning function allows one to generate large swarms of robots with ease. 2_one_pcolony_for_three_kilobots In this video, we simulate a simple P minus colony using Lulu_Kilobot, on three different robots. At each subtraction, the robots move one step forward. At the beginning of the clip one can see the robot - P colony association table, where each robot has a distinct copy of the original P colony. 3_pswarm_5_robots_3_colonies This video demonstrates the flexibility offered by the config file of Lulu_Kilobot. From the config file we explicitly specify that the first two robots should use the go straight P colony. For the other colonies, we specify the number of robots that should be assigned, go left = 1 and go right = 2. From the robot - P colony association table, one can see that the first robot that is assigned a P colony uses the original P colony while the others use an independent copy of the P colony. 4_pcolony_10_robots_disperse This video demonstrates dispersion, which is a typical deployment scenario in swarm robotics. The robots should position themselves away from one another, so that each robot is at least at a minimum distance from each of its neighbours. All decisions are taken by the command module, on the basis of the received input data from msg_distance. A new direction of motion (and corresponding color) is randomly chosen if there are other robots closer than a pre-set threshold distance and otherwise the robots stop and set their color to white. 5_fsm_10_robots_disperse In this video we present the same dispersion algorithm as presented in the previous video but implemented using the Event-driven Finite State Machine control model.