New sensors monitor brain activity and blood flow deeper in the brain and more accurately

https://ift.tt/2HFknRE

Magnetic calcium-responsive nanoparticles (dark centers are magnetic cores) respond within seconds to calcium ion changes by clustering (Ca+ ions, right) or expanding (Ca- ions, left), creating a magnetic contrast change that can be detected with MRI, indicating brain activation. (High levels of calcium outside the neurons correlate with low neuron activity; when calcium concentrations drop, it means neurons in that area are firing electrical impulses.) Blue: C2AB “molecular glue” (credit: The researchers)

Calcium-based MRI sensor enables high-sensitivity deep brain imaging

MIT neuroscientists have developed a new magnetic resonance imaging (MRI) sensor that allows them to monitor neural activity deep within the brain by tracking calcium ions.

Calcium ions are directly linked to neuronal firing at high resolution — unlike the changes in blood flow detected by functional MRI (fMRI), which provide only an indirect indication of neural activity. The new sensor is also better than fluorescent molecules used to label calcium in the brain and image it with traditional microscopy, which is limited to small areas of the brain.

A calcium-based MRI sensor could allow researchers to link specific brain functions directly to specific neuron activity, and to determine how distant brain regions communicate with each other during particular tasks. The research is described in a paper in the April 30 issue of Nature Nanotechnology. Source: MIT


New technique for measuring blood flow in the brain uses laser light shined into the head (“sample arm” path) through the skull. The return signal is boosted by a reference light beam and returned to a detector camera chip. (credit: Srinivasan lab, UC Davis)

Measuring deep-tissue blood flow at high speed

Biomedical engineers at the University of California, Davis, have developed a more-effective, lower-cost technique for measuring deep tissue blood flow in the brain with high speed. It could be especially useful for patients with stroke or traumatic brain injury.

The technique, called “interferometric diffusing wave spectroscopy” (iDWS), replaces about 20 photon-counting detectors in diffusing wave spectroscopy (DWS) devices (which cost a few thousand dollars each) with a single low-cost CMOS-based digital-camera chip.

The NIH-funded work is described in an open-access paper published April 26 in the journal Optica. Source: UC Davis

 

 

 

 

 

 

 

 

 

from Kurzweil https://ift.tt/2JFQcGx

New sensors monitor brain activity and blood flow deeper in the brain and more accurately

https://ift.tt/2rbDk3C

Magnetic calcium-responsive nanoparticles (dark centers are magnetic cores) respond within seconds to calcium ion changes by clustering (Ca+ ions, right) or expanding (Ca- ions, left), creating a magnetic contrast change that can be detected with MRI, indicating brain activation. (High levels of calcium outside the neurons correlate with low neuron activity; when calcium concentrations drop, it means neurons in that area are firing electrical impulses.) Blue: C2AB “molecular glue” (credit: The researchers)

Calcium-based MRI sensor enables high-sensitivity deep brain imaging

MIT neuroscientists have developed a new magnetic resonance imaging (MRI) sensor that allows them to monitor neural activity deep within the brain by tracking calcium ions.

Calcium ions are directly linked to neuronal firing at high resolution — unlike the changes in blood flow detected by functional MRI (fMRI), which provide only an indirect indication of neural activity. The new sensor is also better than fluorescent molecules used to label calcium in the brain and image it with traditional microscopy, which is limited to small areas of the brain.

A calcium-based MRI sensor could allow researchers to link specific brain functions directly to specific neuron activity, and to determine how distant brain regions communicate with each other during particular tasks. The research is described in a paper in the April 30 issue of Nature Nanotechnology. Source: MIT


New technique for measuring blood flow in the brain uses laser light shined into the head (“sample arm” path) through the skull. The return signal is boosted by a reference light beam and returned to a detector camera chip. (credit: Srinivasan lab, UC Davis)

Measuring deep-tissue blood flow at high speed

Biomedical engineers at the University of California, Davis, have developed a more-effective, lower-cost technique for measuring deep tissue blood flow in the brain with high speed. It could be especially useful for patients with stroke or traumatic brain injury.

The technique, called “interferometric diffusing wave spectroscopy” (iDWS), replaces about 20 photon-counting detectors in diffusing wave spectroscopy (DWS) devices (which cost a few thousand dollars each) with a single low-cost CMOS-based digital-camera chip.

The NIH-funded work is described in an open-access paper published April 26 in the journal Optica. Source: UC Davis

 

 

 

 

 

 

 

 

 

from Kurzweil » News https://ift.tt/2JFQcGx

Aspiring AI Programmer

https://ift.tt/1CNTXkp

I’m currently in school for CS with one of my concentrations being AI, but with summer coming up, I’d like to practice AI programming so that A) I don’t forget what I’ve learned over the past to semesters and B) prepare for future AI classes. What would be some good beginner-level AI projects that could help me practice?

submitted by /u/Atherutistgeekzombie
[link] [comments]

from Artificial Intelligence https://ift.tt/2KntNiq

Keras to extract quantitative information from images? (Physics research software)

I am currently working on an undergraduate research project designing software for a physics lab. The software currently takes microscopic images of particles obtained in the lab, and uses physics based algorithms to automatically extract qualitative data from the images. This data includes the particle diameter, and it’s refractive index. The downside of the software is that it is very slow, and computation heavy due to the physics algorithms in place. So I had an idea. I am under the impression that a CNN, via Keras, should be able to identify features of interest (localization) and analyze them (regression) in one shot, or at least in one clean cascade of similar components. I haven’t worked much with ML, and am wondering if someone more experience/knowledge might know if this sounds possible. If so, any leads on how to proceed, or any pointers relevant research papers, etc. would be appreciated.

The following stackexchange discussion explains how to get a number (such as particle diameter) from a network created with Keras: (I’ve been referencing this) https://stats.stackexchange.com/questions/243578/how-to-get-continuous-output-with-convolutional-network-keras

submitted by /u/curmudgeono
[link] [comments]

from Artificial Intelligence https://ift.tt/2HF7Y08

How to start research in Artificial Intelligence?

https://ift.tt/1CNTXkp

Hi,

I am undergrad student and I really like to start to learn about artificial intelligence and publish paper, what do you think is the starting point to learn about different topics in Artificial Intelligence?

submitted by /u/JulieMarlin
[link] [comments]

from Artificial Intelligence https://ift.tt/2vZ9eWC

Developing an advanced AI in the right way? let’s talk about it.

What is the best way to develop a solid working AI able to talk to us like we do with our friends? should we follow the path of seq2seq RNN using QAs from movies and forums or should we teach our AI how to grammatically formulate questions, answers,etc(with all the rules and verbs involved)?

They both are limited imo, for this reason I can’t decide the right path. RNN with LSTM trained with QAs datasets are limited to their datasets, you can never ask to the AI if it likes "Deadpool 2" for example, this kind of training is limited cause it has no clue of the future, therefore you can only ask for already happened stuff.

On the other hand, an AI with all the grammar rules in it would not be so evolved, it has no empathy, it has no past experiences that could help it to formulate the right phrases. But the good thing here is that everything it says is created by it, it’s not taken from a dataset, it’s kinda unique. you’re actually talking to an "empty" human brain just able to reply. Again, here you cannot ask for current time or date and other basic and always changing infos.

But another path is interesting here, chatbots. should we start from chatbots and mix ’em with RNNs +LSTM + seq2seq and feed them with QAs from movies, from forums etc?

I think the right mix between a chatbot, a RNN, a grammar-based algorithm (like tokenization), a sentiment analysis algorithm and some AI output interface like a webcam could help the program to identify gestures, facial and body expressions, objects and animals and so on, giving it a base, a starting point to learn and understand how to reply to the correct way to us.

This is what I would like to develop, but I still have to figure out what to do first and how to mix the things at a programming level, I do know how to create NNs and how to train them but how do I implement the trained model into my python chatbot? My idea is to wait for the user input, analyze it and then choose with a simple IF statement to feed the input in the chatbot algorithm or in the NN algorithm and getting the output.

Any idea and opinion is well accepted guys, let me know.. and if you have something to help me out with this whole thing just write it down here!

submitted by /u/fr1d4y_
[link] [comments]

from Artificial Intelligence https://ift.tt/2JFQIV0

European Commission Unveils €1.5 Billion AI Funding

Artificial intelligence.

The European Commission is investing another 1.5 billion in artificial intelligence research and development through 2020.

Credit: healthcareitnews.com

The European Commission (EC) has announced it will be investing an extra €1.5 billion in artificial intelligence (AI) for the period 2018-20 under the Horizon 2020 research and innovation program.

The funding initiative aims to spur an additional €2.5 billion from existing public-private partnerships.

The funding will support the development of AI in key sectors and strengthen AI research centers across Europe. As part of the effort, the EC also urged member states to modernize their education and training systems and support labor market transitions.

To ensure an adequate supply of AI-capable workers, the EC said it would support business-education partnerships and establish dedicated training programs.

The EC says it will release ethical guidelines on AI development by the end of the year.

EC vice president Andrus Ansip said, “We need to invest at least €20 billion by the end of 2020. The European Commission is playing its part: today, we are giving a boost to researchers so they can develop the next generation of AI technologies and applications, and to companies, so they can embrace and incorporate them.”

From TechTarget
View Full Article

 

Abstracts Copyright © 2018 Information Inc., Bethesda, Maryland, USA


AI to Find Optimal Electric Car Recharge Point Locations

https://ift.tt/2Kqigij

Charging an electric vehicle.

A new artificial intelligence tool can identify the best locations in a city for electric vehicle charging stations.

Credit: Jon Parker Lee

A new artificial intelligence (AI) tool developed at the Universitat Politècnica de València in Spain makes it possible to determine the best locations for electric car charging stations.

The Movindeci tool allows users to analyze the general state of transport and mobility in a city in order to make strategic decisions about those areas.

The tool’s AI algorithm automatically evaluates the possible locations for recharging stations and determines which are the best based on factors that can be specified by the user, such as the population density of the area, urban mobility, and an estimate of the amount of time vehicles spend in any given place.

The researchers already have applied the tool to the city of Valencia, Spain, and they are currently working on another version for Lima, Peru.

From RUVID
View Full Article

 

Abstracts Copyright © 2018 Information Inc., Bethesda, Maryland, USA