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Subcutaneous nanosensor

07.11.2013

Nitric oxide (NO) is one of the most important molecules in living cells. It is responsible for the delivery of signals within and between cells, and coordinates the work of the immune system. Many cancer cells have abnormal levels of nitric oxide, but scientists don't know exactly how this gas works. So, according to them, the role of nitric oxide in the progression of cancer is highly controversial, and scientists need new tools to understand the process.

Researchers at the Massachusetts Institute of Technology have developed a new tool to measure the body's nitric oxide levels in real time. The sensor designed by engineers can be implanted in the body (under the skin) for more than a year and control the process of inflammation - the process during which NO is produced.

This work is the first proof that nanosensors can be used directly in the body for an extended period of time. Sensors made of carbon nanotubes are expected to be used, among other things, to detect other molecules, such as glucose. Already, a team of researchers is working on a sensor for diabetics that will monitor sugar and insulin levels without the need to take blood samples.

Carbon nanotubes one nanometer thick, scientists consider promising for the manufacture of sensors. Researchers at the Massachusetts Institute of Technology have recently developed carbon tube sensors for various molecules, including hydrogen peroxide and sarin. Such sensors use the fluorescence of carbon nanotubes: when a nanotube binds to a particular molecule, it glows brighter or dimmer.

In the new work, the researchers modified the nanotube to create two different types of sensors: one for insertion into the bloodstream for short-term monitoring, and the other for long-term implantation under the skin.

To improve sensor performance, scientists used a biocompatible polymer (polyethylene glycol), which prevents particles from sticking together in the bloodstream. Experiments on mice have shown that in this case, the particles can pass through the lungs and heart without causing any damage. Most particles accumulate in the liver, where they are used to monitor NO levels. At the same time, the researchers note that so far they have studied only the liver, but now they see that the particles remain in the blood. This means that it is possible to study different areas of the body with the help of nanoparticles.

The sensor for long-term residence in the body consists of nanotubes embedded in an alginate gel (a polymer derived from algae). After implantation under the skin of mice, the gel remained in place and functioned for 400 days. But the researchers suggest that the life of the sensor can be extended. In the future, such a sensor would be useful for monitoring cancer or other inflammatory diseases, and for detecting immune responses in patients with artificial hips or other implanted devices.

Scientists are now working on adapting this technology for glucose detection. It is assumed that nanotubes will replace electrochemical sensors for measuring blood sugar levels, which do not work for long, and also increase the risk of infection due to the penetration of the electrode under the skin. But the new sensor will determine the level of sugar in real time, and the insulin pump connected to it will supply the hormone in the right amount.

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Robots have been taught to be curious 08.06.2017

For several years, developers of computer programs for robots have tried to create a "curiosity algorithm", but it was quite difficult to replicate human curiosity. According to them, most methods cannot assess the "gaps" in the knowledge of the robot and understand what it will be interested in.

However, Todd Hester of Google DeepMind in the UK and Peter Stone of the University of Texas in the US have managed to create an algorithm that allows a computer program to "be curious" by learning about its environment on its own. The new approach will allow robots to learn even faster than they do now. The researchers named the algorithm TEXPLORE-VENIR, which is based on a "reinforcement learning" technique.

This technique works according to the following principle: when the program approaches a solution to a problem (for example, exiting a maze), it receives some reward. It is assumed that, having received an award, in the future she will again try to achieve some goal. The researchers slightly modified the "reinforcement learning" technique so that the program is rewarded simply for new knowledge, even if it does not help achieve the goal. For example, TEXPLORE-VENIR earns bonuses when he explores the world, looks for distant places on the map, or masters cooking recipes.

Hester and Stone tested their algorithm in two scenarios. The first of these was a virtual maze of 4 rooms connected by locked doors. The bot - just a computer program - had to explore the area, find the key, take it and open the door. For each open door, he earned 10 points. To achieve the highest score, he was given 3000 "steps".

When using the TEXPLORE-VENIR algorithm, the bot explored the area (having spent 1000 "steps" on this), it earned 55 points. Moreover, if he used other algorithms, he received from 0 to 35 points. When the program had to explore the area and unlock the doors at the same time, it scored 70 points with TEXPLORE-VENIR, and less than 5 in other cases.

For the second experiment, the researchers connected the humanoid robot Nao. With the help of the TEXPLORE-VENIR algorithm, he had to complete three tasks: hit a musical cymbal, find and bring a pink ribbon to his eyes, and press a button located on his leg. For each task, Nao received 200 "steps", and another 400 "steps" were given to him for "training" (learning subjects). After 13 attempts, he pressed the button 7 times, hit the cymbal 1 out of 5 times, and eventually found the pink ribbon faster.

The TEXPLORE-VENIR algorithm showed good results, however, excessive curiosity can reduce the productivity of the robot, third-party researchers say. It may happen that the robot's intrinsic motivation to receive a learning reward exceeds its extrinsic motivation to complete the task. Therefore, it is important to find a balance between the two types of motivation.

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