Providing tools that insure excellent Cell Based Assays is a cornerstone of our business strategy. Lauren McGillicuddy and her team at Essen Bioscience have been using our E18 Primary Rat Cortical Neurons to develop NeuroTrakTM assays enabling kinetic quantification of neurite dynamics (initiation, branching, extension, retraction). NeuroTrack is one of several CellPlayerTM assays that can be run in IncuCyte ZoomTM.
1. The document discusses using principles from biological vision to improve computer vision systems.
2. It describes how computer vision has incorporated ideas from visual neuroscience, such as using oriented filters inspired by V1 simple cells and implementing normalization models.
3. The document argues that biology uses cascades of canonical operations like linear filtering, nonlinearities, and pooling in an optimized way for general-purpose vision, and following these principles can improve computer vision tasks like object recognition.
High-Performance Computing Needs Machine Learning... And Vice Versa (NIPS 201...npinto
This document discusses using high-performance computing for machine learning tasks like analyzing large convolutional neural networks for visual object recognition. It proposes running hundreds of thousands of large neural network models in parallel on GPUs to more efficiently search the parameter space, beyond what is normally possible with a single graduate student and model. This high-throughput screening approach aims to identify better performing network architectures through exploring a vast number of possible combinations in the available parameter space.
This document describes research on bio-inspired active vision systems. It discusses how biological vision differs from traditional computer vision in being active rather than passive. The researchers are developing active vision systems using an evolutionary robotics approach, involving neural networks and genetic algorithms. Previous related work is described, including obstacle avoidance by Mars rovers and koala robots. The document outlines plans to design an active vision system to recognize objects using a dataset of images under different conditions, and accelerate it with GPUs. Results showed the system learned to correctly classify objects over generations.
Neural stimulation has achieved successes like pacemakers and cochlear implants but faces challenges. Stimulation can train neural patterns and produce virtual perceptions like phosphenes in the visual field. Researchers are working to develop visual prostheses using microelectrodes implanted near the optic nerve. The project involves many universities, companies, and funding organizations collaborating to help patients with vision loss.
The document discusses the BioHDF project which aims to develop scalable data infrastructure for bioinformatics using HDF5. It notes that next generation DNA sequencing is producing vast amounts of complex data that is challenging to analyze and compare across samples due to lack of consistent data models and structured storage. The BioHDF project seeks to address this by developing HDF5 domain extensions and tools to organize, index, annotate and access sequencing data in a way that enables more efficient analysis, visualization and exploration of results within and between samples.
The document discusses using neurite growth as an endpoint for assessing developmental neurotoxicity in vitro. It describes how neurite outgrowth is critical for proper brain wiring and is sensitive to toxicants. A human neuronal cell line called LUHMES can be used to measure neurite growth in automated high-content assays allowing efficient screening of chemicals. Several known neurotoxicants were found to reduce neurite growth in the LUHMES cells. The document proposes applying omics technologies like transcriptomics and metabolomics to the LUHMES model to identify pathways of toxicity and classify chemicals. A proof-of-principle study on MPP+ is described where multi-omics analysis revealed perturbed genes, metabolites and pathways in MPP+-treated
Sogang University Machine Learning and Data Mining lab seminar, Neural Networks for newbies and Convolutional Neural Networks. This is prerequisite material to understand deep convolutional architecture.
1. The document discusses using principles from biological vision to improve computer vision systems.
2. It describes how computer vision has incorporated ideas from visual neuroscience, such as using oriented filters inspired by V1 simple cells and implementing normalization models.
3. The document argues that biology uses cascades of canonical operations like linear filtering, nonlinearities, and pooling in an optimized way for general-purpose vision, and following these principles can improve computer vision tasks like object recognition.
High-Performance Computing Needs Machine Learning... And Vice Versa (NIPS 201...npinto
This document discusses using high-performance computing for machine learning tasks like analyzing large convolutional neural networks for visual object recognition. It proposes running hundreds of thousands of large neural network models in parallel on GPUs to more efficiently search the parameter space, beyond what is normally possible with a single graduate student and model. This high-throughput screening approach aims to identify better performing network architectures through exploring a vast number of possible combinations in the available parameter space.
This document describes research on bio-inspired active vision systems. It discusses how biological vision differs from traditional computer vision in being active rather than passive. The researchers are developing active vision systems using an evolutionary robotics approach, involving neural networks and genetic algorithms. Previous related work is described, including obstacle avoidance by Mars rovers and koala robots. The document outlines plans to design an active vision system to recognize objects using a dataset of images under different conditions, and accelerate it with GPUs. Results showed the system learned to correctly classify objects over generations.
Neural stimulation has achieved successes like pacemakers and cochlear implants but faces challenges. Stimulation can train neural patterns and produce virtual perceptions like phosphenes in the visual field. Researchers are working to develop visual prostheses using microelectrodes implanted near the optic nerve. The project involves many universities, companies, and funding organizations collaborating to help patients with vision loss.
The document discusses the BioHDF project which aims to develop scalable data infrastructure for bioinformatics using HDF5. It notes that next generation DNA sequencing is producing vast amounts of complex data that is challenging to analyze and compare across samples due to lack of consistent data models and structured storage. The BioHDF project seeks to address this by developing HDF5 domain extensions and tools to organize, index, annotate and access sequencing data in a way that enables more efficient analysis, visualization and exploration of results within and between samples.
The document discusses using neurite growth as an endpoint for assessing developmental neurotoxicity in vitro. It describes how neurite outgrowth is critical for proper brain wiring and is sensitive to toxicants. A human neuronal cell line called LUHMES can be used to measure neurite growth in automated high-content assays allowing efficient screening of chemicals. Several known neurotoxicants were found to reduce neurite growth in the LUHMES cells. The document proposes applying omics technologies like transcriptomics and metabolomics to the LUHMES model to identify pathways of toxicity and classify chemicals. A proof-of-principle study on MPP+ is described where multi-omics analysis revealed perturbed genes, metabolites and pathways in MPP+-treated
Sogang University Machine Learning and Data Mining lab seminar, Neural Networks for newbies and Convolutional Neural Networks. This is prerequisite material to understand deep convolutional architecture.
Neural networks are computing systems inspired by biological neural networks in the brain. They are composed of interconnected artificial neurons that process information using a connectionist approach. Neural networks can be used for applications like pattern recognition, classification, prediction, and filtering. They have the ability to learn from and recognize patterns in data, allowing them to perform complex tasks. Some examples of neural network applications discussed include face recognition, handwritten digit recognition, fingerprint recognition, medical diagnosis, and more.
Deep Neural networks for Youtuberecommendationsystemnisha thapa
Deep neural networks can be used for YouTube recommendations by feeding the networks vast amounts of video data so they can learn to recognize patterns and provide personalized recommendations. Specifically, YouTube's algorithm ingests video data in real time, ranks videos based on signals like quality and user interests, and provides recommendations from the ranked candidates to improve the user experience.
This document provides an overview of deep learning techniques including neural networks, convolutional neural networks (CNNs), and long short-term memory (LSTM) algorithms. It defines key concepts like Bayesian inference, heuristics, perceptrons, and backpropagation. It also describes how to configure neural networks by specifying hyperparameters, hidden layers, normalization methods, and training parameters. CNN architectures are explained including convolution, pooling, and applications in computer vision tasks. Finally, predictive maintenance using deep learning to predict equipment failures from sensor data is briefly discussed.
Brain-Inspired Computation based on Spiking Neural Networks ...Jorge Pires
On this live, prof. Kasabov gives us a gentle overview of Spiking Neural Networks, and their current applications
Full live here, with discussion: https://www.youtube.com/watch?v=niAannUB3pc&t=232s
Have fun 😎😂😁😀
This document discusses the interpretation of optical coherence tomography (OCT) images of the retina. It begins by explaining how OCT images expand the retina in the axial direction and identifies key layers such as the inner and outer photoreceptor segments. It then examines pathologies affecting the outer, middle, and vitreo-retinal interface of the retina. New developments discussed include using anterior segment OCT to image the anterior chamber, and spectral domain techniques that provide higher resolution images to better visualize conditions like geographic atrophy. In closing, several OCT devices including Heidelberg Spectralis and Zeiss Cirrus are highlighted.
This document discusses neural networks from biological and artificial perspectives. Biologically, neurons are cells in the brain that transmit electrochemical signals between each other via connections called axons. Artificially, neural networks are modeled after biological neural connections and use units and weighted connections. The document also describes the STATISTICA Neural Networks program for creating and training neural networks. It allows designing networks, importing and analyzing data, choosing network types, setting activation functions, and creating applications using neural network APIs.
Wireless Recording Technologies for in vivo Electrophysiology in Conscious, F...InsideScientific
During this webinar, sponsored by Triangle BioSystems International (TBSI), scientists present experimental methods and scientific findings from applications of in vivo electrophysiology in conscious non-human primates using new head-mounted, wireless sensors.
Specifically, members ofThe Hatsopoulos Laboratory at the University of Chicago present research using a 64 channel wireless headstage on marmosets. The objective of this research is to investigate sensorimotor encoding across marmoset’s behavioral repertoire. The group discusses the platform they have developed for voluntary behavioural training and neural recording in a home cage environment, and share preliminary data they have obtained.
Following representatives from Dr. Ben Hayden’s lab, at the University of Minnesota, present a case study in which they have successfully implemented the 128-channel headstage in macaques while performing a center-out joystick task in a primate chair. They share methodology, experimental design, and discuss the promise their results show for future studies using untethered wireless recordings in freely moving and behaving animals.
İnterpretation of optic coherence tomography imagesSinan çalışkan
This document discusses the interpretation of optical coherence tomography (OCT) images of the retina and some new developments in OCT technology. It describes the layers of the normal retina that can be visualized on OCT and key pathologies that affect the outer, middle, and vitreo-retinal interface regions. Newer spectral domain OCT systems allow for improved visualization of retinal structures and layers. Additional applications of OCT now include imaging of the anterior segment as well as analysis of macular diseases like geographic atrophy.
Abhey Sharma's presentation discusses neural networks. It defines biological neural networks as networks of real neurons in the brain, and artificial neural networks (ANNs) as artificial systems composed of interconnected nodes modeled after biological neurons. ANNs are configured through learning to perform tasks like pattern recognition. The history of neural networks is reviewed, from early enthusiasm to a period of frustration before recent resurgence. Neural networks offer advantages like adaptive learning, self-organization, fault tolerance, and fast real-time operation, but disadvantages include their "black box" nature, high computational requirements, difficulty incorporating time, and inevitable errors of approximation.
Employing Electrophysiology and Optogenetics to Measure and Manipulate Neuron...InsideScientific
In this webinar, Dr. Tahl Holtzman, Founder of Cambridge NeuroTech, describes a new generation of silicon neural probes offering dozens of recording channels in precisely spaced, high-resolution arrays, built using sophisticated fabrication techniques borrowed from the electronics industry, along with simple-to-follow surgical implantation schemes for both acute and chronic animals.
Watch to learn how to take advantage of ultra-small chronic drives to open up scalability to span multiple brain areas in parallel and to achieve excellent chronic stability. In addition, Dr. Holtzman demonstrates integration of novel probes and drives offered by Cambridge NeuroTech with optogenetics that thereby enable your experiments to have the combined capability for measurement AND manipulation of neuronal activity in both acute and freely behaving settings.
This webinar will benefit both established electrophysiologists who wish to increase their data yield and experimental reach as well as those investigators whose expertise is centred in and around the animal behavioural, neuropharmacological, and optogenetics arenas. Viewers will learn what silicon neural probes are and how to use them in both acute and chronic experiments, best-practice techniques for surgical implantation in species ranging from mice to monkeys and how to integrate fibre optic cannulas with your probes to enable simultaneous opto-electrophysiology.
Introduction to Spiking Neural Networks: From a Computational Neuroscience pe...Jason Tsai
The document provides an introduction to spiking neural networks (SNNs) and neuromorphic computing. It discusses the characteristics and advantages of SNNs, including their spatio-temporal nature, asynchronous processing, sparsity, and energy efficiency. It also covers basic neuroscience concepts like neurons, action potentials, synaptic plasticity, and learning rules like STDP. Common SNN models and neural encoding schemes are described. Examples of SNN applications in visual processing and pattern generation are presented. Finally, neuromorphic hardware platforms like Intel's Loihi chip are introduced.
An Introduction to Artificial Neural NetworksCameron Vetter
Neural networks are loosely inspired by biological neural networks in animal brains. They can be used to analyze visual imagery through deep, feed-forward artificial neural networks. Popular open source frameworks like TensorFlow are leading the development of neural networks and machine learning. The presentation provided an overview of different types of neural networks including convolutional neural networks, self-organizing maps, recurrent neural networks, and autoencoders. Examples were given for using these networks for tasks like image recognition, text classification, drug discovery, time series prediction, speech recognition, and image compression/denoising.
This document summarizes image recognition using deep neural networks. It discusses how image recognition works, the different applications, and the key steps involved in building a convolutional neural network for image recognition. These steps include pre-processing the images, splitting the dataset, building the CNN with convolutional and pooling layers, and training it through forward and back propagation over many epochs to classify images.
This document provides an overview of three types of machine learning: supervised learning, reinforcement learning, and unsupervised learning. It then discusses supervised learning in more detail, explaining that each training case consists of an input and target output. Regression aims to predict a real number output, while classification predicts a class label. The learning process typically involves choosing a model and adjusting its parameters to reduce the discrepancy between the model's predicted output and the true target output on each training case.
This document provides an introduction to brain-computer interfaces (BCI). It defines BCI and its components. It describes how BCIs can allow communication using brain signals alone, without muscle control, to help paralyzed people communicate. It also discusses different types of biosignals measured, including EEG, and how non-invasive BCIs using scalp electrodes work. Applications mentioned include communication devices, operator monitoring, games and entertainment.
This project aimed to create an iOS game where the player navigates a maze using brain signals from an EEG device and neural network classification of the signals. The project consisted of three programs: NeuroSample for collecting training data, NeuroTrain for training an neural network classifier on the data, and NeuroMaze the game itself. Key challenges included inconsistent EEG readings from the device, difficulty maintaining brain states for data collection, noisy data from facial muscle artifacts, and a small training dataset, leading to low classification accuracy not suitable for game play. The author learned that data quality is crucial for neural network success, and gained practical understanding of network parameters and training from this project.
The document summarizes the goals and plans of a company called BrainBackups. It discusses assembling a team of neuroscientists, engineers, and advisors to develop the technology. The mission is to create a full backup of the human brain by mapping all neurons and synapses, which would require around 1 terabyte of storage. High resolution brain imaging technologies would be used to map and decode different areas of the brain. Initial funding is needed to continue research into decoding neuroanatomy and developing devices that could store brain backups.
The document discusses the syllabus for a course on Neural Networks. The mid-term syllabus covers introduction to neural networks, supervised learning including the perceptron and LMS algorithm. The end-term syllabus covers additional topics like backpropagation, unsupervised learning techniques and associative models including Hopfield networks. It also lists some references and applications of neural networks.
Artificial Neural Network and its Applicationsshritosh kumar
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
mathematical models involved are presented and
demonstrated.
Short overview featuring our 2d and 3d cell based assays, defined media and supplement liked FBS. In addition we are a distributor for IBA Life Science's cell selection & expansion, exosome isolation and protein production & assays products.
This document summarizes cell-based assay solutions from Neuromics and its partnership with UB Systems. It describes 3D cell-based assays including blood-brain barrier models and extracellular matrix hydrogels. It also provides details on various human brain cell types such as endothelial cells, astrocytes, and pericytes that can be used to build customized 3D brain models. Additionally, it mentions services offered like media filling and various cell culture reagents including fetal bovine serum in different grades.
Neural networks are computing systems inspired by biological neural networks in the brain. They are composed of interconnected artificial neurons that process information using a connectionist approach. Neural networks can be used for applications like pattern recognition, classification, prediction, and filtering. They have the ability to learn from and recognize patterns in data, allowing them to perform complex tasks. Some examples of neural network applications discussed include face recognition, handwritten digit recognition, fingerprint recognition, medical diagnosis, and more.
Deep Neural networks for Youtuberecommendationsystemnisha thapa
Deep neural networks can be used for YouTube recommendations by feeding the networks vast amounts of video data so they can learn to recognize patterns and provide personalized recommendations. Specifically, YouTube's algorithm ingests video data in real time, ranks videos based on signals like quality and user interests, and provides recommendations from the ranked candidates to improve the user experience.
This document provides an overview of deep learning techniques including neural networks, convolutional neural networks (CNNs), and long short-term memory (LSTM) algorithms. It defines key concepts like Bayesian inference, heuristics, perceptrons, and backpropagation. It also describes how to configure neural networks by specifying hyperparameters, hidden layers, normalization methods, and training parameters. CNN architectures are explained including convolution, pooling, and applications in computer vision tasks. Finally, predictive maintenance using deep learning to predict equipment failures from sensor data is briefly discussed.
Brain-Inspired Computation based on Spiking Neural Networks ...Jorge Pires
On this live, prof. Kasabov gives us a gentle overview of Spiking Neural Networks, and their current applications
Full live here, with discussion: https://www.youtube.com/watch?v=niAannUB3pc&t=232s
Have fun 😎😂😁😀
This document discusses the interpretation of optical coherence tomography (OCT) images of the retina. It begins by explaining how OCT images expand the retina in the axial direction and identifies key layers such as the inner and outer photoreceptor segments. It then examines pathologies affecting the outer, middle, and vitreo-retinal interface of the retina. New developments discussed include using anterior segment OCT to image the anterior chamber, and spectral domain techniques that provide higher resolution images to better visualize conditions like geographic atrophy. In closing, several OCT devices including Heidelberg Spectralis and Zeiss Cirrus are highlighted.
This document discusses neural networks from biological and artificial perspectives. Biologically, neurons are cells in the brain that transmit electrochemical signals between each other via connections called axons. Artificially, neural networks are modeled after biological neural connections and use units and weighted connections. The document also describes the STATISTICA Neural Networks program for creating and training neural networks. It allows designing networks, importing and analyzing data, choosing network types, setting activation functions, and creating applications using neural network APIs.
Wireless Recording Technologies for in vivo Electrophysiology in Conscious, F...InsideScientific
During this webinar, sponsored by Triangle BioSystems International (TBSI), scientists present experimental methods and scientific findings from applications of in vivo electrophysiology in conscious non-human primates using new head-mounted, wireless sensors.
Specifically, members ofThe Hatsopoulos Laboratory at the University of Chicago present research using a 64 channel wireless headstage on marmosets. The objective of this research is to investigate sensorimotor encoding across marmoset’s behavioral repertoire. The group discusses the platform they have developed for voluntary behavioural training and neural recording in a home cage environment, and share preliminary data they have obtained.
Following representatives from Dr. Ben Hayden’s lab, at the University of Minnesota, present a case study in which they have successfully implemented the 128-channel headstage in macaques while performing a center-out joystick task in a primate chair. They share methodology, experimental design, and discuss the promise their results show for future studies using untethered wireless recordings in freely moving and behaving animals.
İnterpretation of optic coherence tomography imagesSinan çalışkan
This document discusses the interpretation of optical coherence tomography (OCT) images of the retina and some new developments in OCT technology. It describes the layers of the normal retina that can be visualized on OCT and key pathologies that affect the outer, middle, and vitreo-retinal interface regions. Newer spectral domain OCT systems allow for improved visualization of retinal structures and layers. Additional applications of OCT now include imaging of the anterior segment as well as analysis of macular diseases like geographic atrophy.
Abhey Sharma's presentation discusses neural networks. It defines biological neural networks as networks of real neurons in the brain, and artificial neural networks (ANNs) as artificial systems composed of interconnected nodes modeled after biological neurons. ANNs are configured through learning to perform tasks like pattern recognition. The history of neural networks is reviewed, from early enthusiasm to a period of frustration before recent resurgence. Neural networks offer advantages like adaptive learning, self-organization, fault tolerance, and fast real-time operation, but disadvantages include their "black box" nature, high computational requirements, difficulty incorporating time, and inevitable errors of approximation.
Employing Electrophysiology and Optogenetics to Measure and Manipulate Neuron...InsideScientific
In this webinar, Dr. Tahl Holtzman, Founder of Cambridge NeuroTech, describes a new generation of silicon neural probes offering dozens of recording channels in precisely spaced, high-resolution arrays, built using sophisticated fabrication techniques borrowed from the electronics industry, along with simple-to-follow surgical implantation schemes for both acute and chronic animals.
Watch to learn how to take advantage of ultra-small chronic drives to open up scalability to span multiple brain areas in parallel and to achieve excellent chronic stability. In addition, Dr. Holtzman demonstrates integration of novel probes and drives offered by Cambridge NeuroTech with optogenetics that thereby enable your experiments to have the combined capability for measurement AND manipulation of neuronal activity in both acute and freely behaving settings.
This webinar will benefit both established electrophysiologists who wish to increase their data yield and experimental reach as well as those investigators whose expertise is centred in and around the animal behavioural, neuropharmacological, and optogenetics arenas. Viewers will learn what silicon neural probes are and how to use them in both acute and chronic experiments, best-practice techniques for surgical implantation in species ranging from mice to monkeys and how to integrate fibre optic cannulas with your probes to enable simultaneous opto-electrophysiology.
Introduction to Spiking Neural Networks: From a Computational Neuroscience pe...Jason Tsai
The document provides an introduction to spiking neural networks (SNNs) and neuromorphic computing. It discusses the characteristics and advantages of SNNs, including their spatio-temporal nature, asynchronous processing, sparsity, and energy efficiency. It also covers basic neuroscience concepts like neurons, action potentials, synaptic plasticity, and learning rules like STDP. Common SNN models and neural encoding schemes are described. Examples of SNN applications in visual processing and pattern generation are presented. Finally, neuromorphic hardware platforms like Intel's Loihi chip are introduced.
An Introduction to Artificial Neural NetworksCameron Vetter
Neural networks are loosely inspired by biological neural networks in animal brains. They can be used to analyze visual imagery through deep, feed-forward artificial neural networks. Popular open source frameworks like TensorFlow are leading the development of neural networks and machine learning. The presentation provided an overview of different types of neural networks including convolutional neural networks, self-organizing maps, recurrent neural networks, and autoencoders. Examples were given for using these networks for tasks like image recognition, text classification, drug discovery, time series prediction, speech recognition, and image compression/denoising.
This document summarizes image recognition using deep neural networks. It discusses how image recognition works, the different applications, and the key steps involved in building a convolutional neural network for image recognition. These steps include pre-processing the images, splitting the dataset, building the CNN with convolutional and pooling layers, and training it through forward and back propagation over many epochs to classify images.
This document provides an overview of three types of machine learning: supervised learning, reinforcement learning, and unsupervised learning. It then discusses supervised learning in more detail, explaining that each training case consists of an input and target output. Regression aims to predict a real number output, while classification predicts a class label. The learning process typically involves choosing a model and adjusting its parameters to reduce the discrepancy between the model's predicted output and the true target output on each training case.
This document provides an introduction to brain-computer interfaces (BCI). It defines BCI and its components. It describes how BCIs can allow communication using brain signals alone, without muscle control, to help paralyzed people communicate. It also discusses different types of biosignals measured, including EEG, and how non-invasive BCIs using scalp electrodes work. Applications mentioned include communication devices, operator monitoring, games and entertainment.
This project aimed to create an iOS game where the player navigates a maze using brain signals from an EEG device and neural network classification of the signals. The project consisted of three programs: NeuroSample for collecting training data, NeuroTrain for training an neural network classifier on the data, and NeuroMaze the game itself. Key challenges included inconsistent EEG readings from the device, difficulty maintaining brain states for data collection, noisy data from facial muscle artifacts, and a small training dataset, leading to low classification accuracy not suitable for game play. The author learned that data quality is crucial for neural network success, and gained practical understanding of network parameters and training from this project.
The document summarizes the goals and plans of a company called BrainBackups. It discusses assembling a team of neuroscientists, engineers, and advisors to develop the technology. The mission is to create a full backup of the human brain by mapping all neurons and synapses, which would require around 1 terabyte of storage. High resolution brain imaging technologies would be used to map and decode different areas of the brain. Initial funding is needed to continue research into decoding neuroanatomy and developing devices that could store brain backups.
The document discusses the syllabus for a course on Neural Networks. The mid-term syllabus covers introduction to neural networks, supervised learning including the perceptron and LMS algorithm. The end-term syllabus covers additional topics like backpropagation, unsupervised learning techniques and associative models including Hopfield networks. It also lists some references and applications of neural networks.
Artificial Neural Network and its Applicationsshritosh kumar
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
mathematical models involved are presented and
demonstrated.
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Short overview featuring our 2d and 3d cell based assays, defined media and supplement liked FBS. In addition we are a distributor for IBA Life Science's cell selection & expansion, exosome isolation and protein production & assays products.
This document summarizes cell-based assay solutions from Neuromics and its partnership with UB Systems. It describes 3D cell-based assays including blood-brain barrier models and extracellular matrix hydrogels. It also provides details on various human brain cell types such as endothelial cells, astrocytes, and pericytes that can be used to build customized 3D brain models. Additionally, it mentions services offered like media filling and various cell culture reagents including fetal bovine serum in different grades.
Neuromics Bases 2 and 3 Cell Based AssaysPete Shuster
This document provides information on cell-based assay solutions from Neuromics for drug discovery applications. It describes 3D blood-brain barrier models, extracellular matrixes, coating solutions, and published data from human BBB models. Details are given on human brain astrocytes and endothelial cells, as well as retinal cells. An example is shown of researchers using these cell types to create an in vitro BBB model. The document also discusses FBS and other serum options and provides contact information for Neuromics.
Neuromics and UBC have a business partnership to provide 3D cell-based assay solutions and manufacture and fill media. Neuromics offers proven human cell types like brain astrocytes and endothelial cells for building blood brain barrier models. They also provide fetal bovine serum in different grades based on endotoxin levels as well as other animal sera. Customers have successfully used Neuromics' cells to create static 3D blood brain barrier models.
Neuromics base presentation 2020 with Virus Transport MediaPete Shuster
Neuromics' is a leader in providing Biopharmas, Academic and Government with CFR compliant 2 and 3-D human primary cell assays, media and supplements for discovery. We also provide antibodies, proteins/growth factors, apoptosis kits and genetic engineering/manipulation tools. We now have FDA registered Virus Transport Media (VTM).
Neuromics' is a leader in providing Biopharmas, Academic and Government with CFR compliant 2 and 3-D human primary cell assays, media and supplements for discovery. We also provide antibodies, proteins/growth factors, apoptosis kits and genetic engineering/manipulation tools.
Neuromics' is a recognized leader in providing Large Pharma, Biotech, and Academic/Government Labs 2 and 3-D cell-based assays. They are excellent for use in Drug Discover and Toxicology Studies.
Details on Neuromics' 3-D Blood-Brain Barrier Model. This model is designed for drug discovery and tox assays. The goal is to provide users with in vivo like results.
Proteomics Modules designed to bring clinically relevant data, at any point, into the Drug Discovery Process. 1000s of proteins are plated from primary cells and are used to trap autoantibodies from diseased patients' blood sera. Results put a spotlight on highest probability targets.
Available Cell Based Assays and Expertise-Proven to work for drug discovery, tox studies, expansion, migration and differentiation. Results guaranteed,
Regenerative Medicine Industry Outlook 2014Pete Shuster
The document summarizes the regenerative medicine field based on a report from the Alliance for Regenerative Medicine, including an overview of major industry players and subsectors, clinical trials and products in development, financial performance and investments in 2013, and insights from a panel discussion on key areas of focus. It analyzes trends in cell and gene therapies, areas attracting pharmaceutical investment, and challenges facing the commercialization of regenerative medicine technologies and therapies.
This document provides a technical and business overview of Vitro Biopharma. It summarizes their stem cell products focusing on mesenchymal stem cells, introduces their management team and partners, describes their proprietary MSC growth medium MSCGro which supports faster growth than competitors' media, and outlines their research in areas like osteoporosis drug discovery using cell-based assays. It also reviews their intellectual property strategy and regenerative medicine market opportunities.
The dance between Glia and Neurons is critical to the development and maintenance to CNS. Did you know: • up to 90% of the cells in the vertebrate nervous system are “not neurons” • make > 50% of the brain volume. This is a great overview to anyone that wants to learn more.
This document describes an optimized protocol that enables the co-detection of cluster of differentiation (CD) surface antigens on fixed, permeabilized neural cell populations through combined flow cytometric analysis of both surface and intracellular antigens. This reveals surface molecule markers of human neuropoiesis.
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This document discusses potential cell-based assays for drug discovery related to musculoskeletal disorders using human mesenchymal stem cells (MSCs). Specifically, it describes MSCs derived from umbilical cord blood that are phenotypically stable and can differentiate into osteoblasts, chondrocytes, and fibroblasts. Potent media are also described that allow expansion of these cells in a manner suitable for high-throughput screening. Images demonstrate differentiation of MSCs and growth of fibroblasts over multiple passages. The document argues that these cells and media provide a cost-effective alternative to induced pluripotent stem cells for developing cell-based assays.
Better cell cultures lower your research costs!-A common Neuromics' theme is harnessing the power of cells. The raw cost of the cells are often the biggest consideration. We encourage our customers to focus on true costs. These include the # number of cells (how many times can they be passaged?), culture viability (how long do the cells live) and bioactivity (how closely do cultures mimic in vivo behavior?). I would like to present a publication and presentation confirms our competitiveadvantage when analyzing true costs.
Cell based assays presentation v3_03_2012Pete Shuster
Update presentation on "increasing returns in drug discovery by harnessing the power of cells". Includes images/data/pubs of differentiating human sensory and dopaminergic neurons from hNP1 neural progenitors + osteoblasts and chondrocytes from human mesenchymal stem cells. Our platforms are ideal for high throughput screening and other drug discovery processes.
Cell based assays presentation V2_03_2012Pete Shuster
This document provides an executive overview of a company called Neuromics/Vitro Biopharma that develops pre-clinical tools for drug discovery using human stem cell-derived cell systems. The company offers physiologically relevant human cells and cellular systems to enable better in vitro screening and target identification, reducing animal models and shortening development timelines. Key offerings include human stem cell-derived neuronal cells, glial cells, immune cells and other cell types, specialized media, transfection reagents, markers and labeling technologies. The company aims to improve early drug discovery through more predictive human cell-based assays.
1) ArunA Biomedical produces three neural cell products from different sources - STEMEZTM hNP1TM from human embryonic stem cells, STEMEZTM hN2TM from human embryonic stem cells, and STEMEZTM iPS-NP1TM from human induced pluripotent stem cells - for use in high-throughput screening and high-content analysis assays.
2) The document demonstrates that all three neural cell products can be used to measure ATP levels in a high-throughput screening assay and evaluate neurite outgrowth in a high-content analysis assay to test neurotoxicity and neuronal differentiation.
3) Both hNP1TM and hN2TM neural cells differentiated effectively in
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Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
CAKE: Sharing Slices of Confidential Data on BlockchainClaudio Di Ciccio
Presented at the CAiSE 2024 Forum, Intelligent Information Systems, June 6th, Limassol, Cyprus.
Synopsis: Cooperative information systems typically involve various entities in a collaborative process within a distributed environment. Blockchain technology offers a mechanism for automating such processes, even when only partial trust exists among participants. The data stored on the blockchain is replicated across all nodes in the network, ensuring accessibility to all participants. While this aspect facilitates traceability, integrity, and persistence, it poses challenges for adopting public blockchains in enterprise settings due to confidentiality issues. In this paper, we present a software tool named Control Access via Key Encryption (CAKE), designed to ensure data confidentiality in scenarios involving public blockchains. After outlining its core components and functionalities, we showcase the application of CAKE in the context of a real-world cyber-security project within the logistics domain.
Paper: https://doi.org/10.1007/978-3-031-61000-4_16
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Essen bioscience neuromics 9_17_12
1. CellPlayer NeuroTrack ™ Assay
Assay Design Software
Demonstrated compatibility Integrated ,User-friendly
with primary neurons, IncuCyteTM ZOOM
neuronal cell lines, and Primary Neutons from
neurons derived from Neuromics
iPSCs.
Quantitative
Neurite
Dynamics
Advantages
• Label-free
• Quantitative, kinetic data
• 10x or 20x objectives
• Flexible algorithm
• High-definition images
• Time-lapse movies
• Internal Expertise
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2. Tracking Neurite Dynamics
Neurite Outgrowth
Fukata et al., Neuroscience Research, Vol. 43, Issue 4, August 2002, Pages 305–315
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3. Tracking Neurite Dynamics
Fundamental Role In Neurite Outgrowth
• Embryonic development
• Neuronal differentiation
• Nervous system function
• Neuropathological
disorders
• Neuronal injury and
regeneration
• Neurotoxicity
Fukata et al., Neuroscience Research, Vol. 43, Issue 4, August 2002, Pages 305–315
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4. Neurite Outgrowth Analysis:
High Content Imaging Approach
Fix cells
- Single time point
- Paraformaldehyde fixation
-risk loss of fine neurites
Antibody Labeling (immunofluorescence)
- Protocols require a few hours at minimum
- Multiple antibodies
Image Acquisition and Analysis
- High Content Imager and software
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5. Neurite Outgrowth Analysis:
High Content Imaging Approach
Fix cells
- Single time point
- Paraformaldehyde fixation
-risk loss of fine neurites
Antibody Labeling (immunofluorescence)
- Protocols require a few hours at minimum
- Multiple antibodies
Image Acquisition and Analysis
- High Content Imager and software
Labor intensive, complex, results in data from a single time point.
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6. NeuroTrack ™ Assay Protocol
Cortical Neurons Hippocampal Neurons Neuro-2a Cells iCell Neurons®
Select Cells:
We recommend Techno Plastic Products (TPP)
Plate Cells: tissue culture plates for optimal clarity.
Change media 18-24hrs post cell plating. Apply test
Media Change:
agents (compounds, growth factors).
Place vessels in IncuCyte Zoom and image at user
Image cells: defined intervals.
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7. Measuring Neurite Dynamics with
NeuroTrack ™
Non-labeled E18 rat cortical neurons plated on poly-D-lysine
HD Phase
Segmentation
T=24hrs T=72hrs T=120hrs
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10. Measuring Neurite Dynamics with
NeuroTrack ™
Time lapse series of images and masks
Quantitative data Time lapse movies
Neurite Length
150
16k cells/well
Neurite length (mm/mm2 )
Neurite Length/Cell Body Cluster
Neurite length (mm/cell body cluster)
12k cells/well
0.8
Branch Points 100 8k cells/well
4k cells/well 4k cells/well
0.6 4000
8k cells/well 16k cells/well
Branch Points (1/mm2 )
12k cells/well 50 12k cells/well
0.4 3000
16k cells/well 8k cells/well
4k cells/well
0.2 2000 0
0 24 48 72 96 120 144
Time post plating (hours)
0.0 1000
0 24 48 72 96 120 144
Time post plating (hours)
0
0 24 48 72 96 120 144
Time post plating (hours)
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11. Assay Validation:
NeuroTrack ™ vs. Endpoint Assay
A
B
• Data points represent mean ± SD, n=30
NeuroTrack quantitation of living neurites in HD phase is
comparable to the quantitation of fixed and stained A) NeuroTrack phase image with mask
neurites in a high content imager. B) β-tubulin staining in fixed cells
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12. Low Intra Assay Variability
Non-labeled E18 rat cortical neurons plated on poly-D-lysine
96-well Plate View
• Data points represent mean ± SD, n=96
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13. E18 Rat Cortical Neurons:
NeuroTrack ™ Assay Optimization
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14. Cytochalasin D treatment of E18 cortical neurons
highlights the importance of a kinetic read-out
• Cytochalasin D depolymerizes the actin cytoskeleton. Cytochalasin D Treatment:
Neurite Length
•
Neurite length (mm/cell body cluster)
It has been shown that treatment of neurons with high
0.25 Cytochalasin D
concentrations of Cytochalasin D results in the rapid concentration
development of multiple axon-like structures. * 0.20 1 µM
0.3 µM
• However, a NeuroTrack time course reveals that these 0.15 Vehicle
structures are transient due to neurite disintegration and cell 0.10
0.1 µM
death.
0.05
• In contrast, low concentrations of Cytochalasin D inhibit overall mean ± SD, n=6, 9 images /well
0.00
neurite outgrowth.
0 24 48 72 96 120 144
Time post plating (hours)
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15. Cytochalasin D treatment of E18 cortical neurons
highlights the importance of a kinetic read-out
• Cytochalasin D depolymerizes the actin cytoskeleton. Cytochalasin D Treatment:
Neurite Length
Neurite length (mm/cell body cluster)
• It has been shown that treatment of neurons with high
0.25 Cytochalasin D
concentrations of Cytochalasin D results in the rapid concentration
development of multiple axon-like structures. * 0.20 1 µM
0.3 µM
• However, a NeuroTrack time course reveals that these 0.15 Vehicle
structures are transient due to neurite disintegration and cell 0.10
0.1 µM
death.
0.05
• In contrast, low concentrations of Cytochalasin D inhibit overall mean ± SD, n=6, 9 images /well
0.00
neurite outgrowth.
0 24 48 72 96 120 144
Time post plating (hours)
A B
A: Cortical neurons treated
with vehicle (T=66hrs)
B: Cortical neurons
treated with1 µM Cyto D
(T=66hrs)
*Bradke and Dotti, Science, 1999 • Data points represent mean ± SD, n=6
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16. Cytochalasin D treatment of E18 cortical neurons
highlights the importance of a kinetic read-out
• Cytochalasin D depolymerizes the actin cytoskeleton. Cytochalasin D Treatment:
Neurite Length
Neurite length (mm/cell body cluster)
• It has been shown that treatment of neurons with high
0.25 Cytochalasin D
concentrations of Cytochalasin D results in the rapid concentration
development of multiple axon-like structures. * 0.20 1 µM
0.3 µM
• However, a NeuroTrack time course reveals that these 0.15 Vehicle
structures are transient due to neurite disintegration and cell 0.10
0.1 µM
death.
0.05
• In contrast, low concentrations of Cytochalasin D inhibit overall mean ± SD, n=6, 9 images /well
0.00
neurite outgrowth.
0 24 48 72 96 120 144
A B Time post plating (hours)
A B
B
A: Cortical neurons treated
with vehicle (T=66hrs)
B: Cortical neurons
treated with1 µM Cyto D
(T=66hrs)
*Bradke and Dotti, Science, 1999 • Data points represent mean ± SD, n=6
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17. Measure Neurite Dynamics and
Cytotoxicity
• Measure cytotoxicity and neurite outgrowth kinetically in the
same well.
YoPro-3® (Life Technologies) is a cell impermeant cyanine dimer
nucleic acid stain that binds dsDNA. Apoptosis and necrosis result in
a loss of membrane integrity. YoPro-3 ® stains cell nuclei only when
cells have lost membrane integrity, viable cells remain unstained. YoPro-3®
+ cytotoxic compound
We have optimized the use of YoPro-3 ® for use in monitoring
cytotoxicity kinetically in primary cortical neurons.
Control 0.1 µM R0-31-8220 1 µM R0-31-8220
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18. Measure Neurite Dynamics and
Cytotoxicity
• Measure cytotoxicity and neurite outgrowth kinetically in the
same well.
YoPro-3® (Life Technologies) is a cell impermeant cyanine dimer
nucleic acid stain that binds dsDNA. Apoptosis and necrosis result in
a loss of membrane integrity. YoPro-3 ® stains cell nuclei only when
cells have lost membrane integrity, viable cells remain unstained. YoPro-3®
+ cytotoxic compound
We have optimized the use of YoPro-3 ® for use in monitoring
cytotoxicity kinetically in primary cortical neurons.
Control 0.1 µM R0-31-8220 1 µM R0-31-8220
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19. Neurite Outgrowth + Cytotoxicity
Measuring cytotoxicity and neurite length in
response to PKC inhibition:
24 hours post plating, E18 rat cortical neurons were
treated with different concentrations of the PKC inhibitor,
Ro-31-8220, in the presence of Yo-Pro3 ®.
Neurite Length/Cell Body Cluster
Neurite length (mm/cell body cluster)
0.5
Vehicle
0.004 µM Ro-31-8220
0.4
0.02 µM Ro-31-8220
0.3 0.1 µM Ro-31-8220
0.5 µM Ro-31-8220
0.2 1 µM Ro-31-8220
0.1
0.0
0 24 48 72 96 120 144
Time post plating (hours)
• Data points represent mean ± SD, n=6, 9 images/well
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20. Neurite Outgrowth + Cytotoxicity
Measuring cytotoxicity and neurite length in
response to PKC inhibition:
24 hours post plating, E18 rat cortical neurons were
treated with different concentrations of the PKC inhibitor,
Ro-31-8220, in the presence of Yo-Pro3 ®.
Neurite Length/Cell Body Cluster Cell Death
Neurite length (mm/cell body cluster)
YoPro-3 Red Object Count/mm2
0.5 300
Vehicle
0.004 µM Ro-31-8220
0.4 1 µM Ro-31-8220
0.02 µM Ro-31-8220 0.5 µM Ro-31-8220
200
0.3 0.1 µM Ro-31-8220 0.1 µM Ro-31-8220
0.5 µM Ro-31-8220 0.02 µM Ro-31-8220
0.2 1 µM Ro-31-8220 100 0.004 µM Ro-31-8220
Vehicle
0.1
0.0 0
0 24 48 72 96 120 144 0 24 48 72 96 120 144
Time post plating (hours) Time post plating (hours)
• Data points represent mean ± SD, n=6, 9 images/well
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21. CellPlayer NeuroTrack™ Assay
• The HD Phase optics, integrated software algorithm and live-cell
Label-free: imaging obviates the need to fix and label cells.
• Time lapse measurement of neurite dynamics under physiological
Kinetic: conditions. Images can be assembled into time-lapse movies.
Compatible with multiple • Validated with rodent primary neurons, iPSC derived neurons and
neuronal cell types: Neuro-2A cells.
• Automated data acquisition and integrated metric calculations
Easy to use software: provide convenient access to complex data.
Multiplex: • Monitor a fluorescent label as well as neurite dynamics.
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Editor's Notes
An Overview of the assay:-compatible in every neuronal cell type we tested-run exclusively on ZOOM-as in all incucyte assays images are captured at user defined intervals-from the phase images, a mask is applied, from the mask, quantitation is derived-advantages (Flexible algorithm means compatible with multiple cell types)
What are neurite dynamics?A term that includes neurite outgrowth, in which a neuron extends processes to create neural networksHere you see the stages of outgrowth, in which axons and dendrites formNeurite is a non-specific term including both axons and dendritesIn addition to outgrowth, loss of neurite length is included in neurite dynamics, and is also important to studies of development and degenerative diseasesNote: NeuroTrack does not distinguish between axons/dendrites
Neurite dynamics have an important role in….
Generally paraformaldehyde used—not a user friendly chemical.Long time required for incubation with antibodies
POINT: Labor intensive process, complex, plenty of troubleshooting involved, and all for data that comes from only a single timepoint!Essen combined the capabilities of ZOOM and the talent of the programming engineers to improve the system for measuring neurite dynamics.
This is the alternative protocol NeuroTrack (NT) provides scientists.Step 1: Select cells. Very important fact that NT is compatible with such a wide range of models, as they have very different morphology.
These are non labeled E18 rat cortical neurons plated on poly-D-lysine at 3 different timepoints post-plating1. Really there is a minimal amount of user involvement needed to create a very good mask like the one you see here. There are just enough parameters to customize to the user’s cell type, but it’s not an overwhelming process. HCI systems in comparison have much more complex, time consuming processes. A good processing definition on NeuroTrack can be made in under 10 minutes.
NT masks these very fine primary neurites with the same precision as large, thick neurites.
This is a slide showing the NeuroTrack process– From the phase images, it generates the user-customized mask, and from this mask quantitative data is produced in real-time.
This slide summarizes the flow of information in a NeuroTrack assay—the phase images of the cells can be made into time lapse movies, and the masks automatically produce data of the metrics just described.
Scientists at Essen performed assay validation in an experiment where they took plates of cells at three different timepoints, imaged in ZOOM, and immediately fixed, immunostained with Beta-tubulin, the standard for labeling neurites in fluorescence. These plates they took to the University of Michigan to image in their Image Xpress Micro. They utilized their corresponding software to measure neurite length and compared the results with the ZOOM analysis. In these graphs of three different timepoints you see that “NeuroTrack quantitation…..in a high content imager.”
The intra-assay variability was quantified using “non….on polydlysine” plated at 8k cells/wellError bars represent standard deviationCoefficient of variation remains well under 10%.96 well plate view demonstrates low variability.A corresponding power analysis demonstrated that N=6 with 9 images per well is suggested. (for an assay of 80% power to show a 10% change in neurite length)
Movie at optimal density.
POINT: shows the importance of a kinetic readout, how it completes the story of cytochalasin D’s effectsThese results were replicated 2 more times at Essen and agree with reports from the literature.
Below you see the pictures, longer structures in B. (~25% longer)
Masked.
Another important application of a fluorescent marker. Images show control cells and two concentrations of a PKC inhibitor and the resulting increase in red fluorescent objects.
Here is a close up of the red blended image—we chose to use the lowest concentration of YoPro that the Basic Analyzer was capable of masking, so concentration of YoPro-3, and thus the fluorescence is very low. This concentration of YoPro-3 (and concentrations even higher) have been shown to produce no effect on cell viability or neurite outgrowth.
Example of how to use this reagent. We used Ro-31-8220 which is a known PKC inhibitor. Protein kinase C is upstream of neurite outgrowth and survival signal transduction pathway, and adding a PKC inhibitor caused the predicted effect of decreased neurite length per cell body cluster with increasing concentration of inhibitor. It’s very useful to know if and when cell death is occurring during this assay.
So this data was produced using a Basic Analyzer processing definition to show that the higher concentrations of Ro-31-8220 have higher cell death, and this cell death is the cause of the loss of neurite length.