Some of you may remember that the ship’s computer on Star Trek: Voyager contained bioneural gel packs. Researchers have taken us one step closer to a biocomputing future with a study on the potential of ecological systems for computing.
Neural networks are a big deal in the world of machine learning, and it turns out that ecological dynamics exhibit many of the same properties. Reservoir Computing (RC) is a special type of Recurrent Neural Network (RNN) that feeds inputs into a fixed-dynamics reservoir black box with training only occurring on the outputs, drastically reducing the computational requirements of the system. With some research now embodying these reservoirs into physical objects like robot arms, the researchers wanted to see if biological systems could be used as computing resources.
Using both simulated and real bacterial populations (Tetrahymena thermophila) to respond to temperature stimuli, the researchers showed that ecological system dynamics has the “necessary conditions for computing (e.g. synchronized dynamics in response to the same input sequences) and can make near-future predictions of empirical time series.” Performance is currently lower than other forms of RC, but the researchers believe this will open up an exciting new area of research.
Researchers in Canada and the United States have used deep learning to derive an antibiotic that can attack a resistant microbe, acinetobacter baumannii, which can infect wounds and cause pneumonia. According to the BBC, a paper in Nature Chemical Biology describes how the researchers used training data that measured known drugs’ action on the tough bacteria. The learning algorithm then projected the effect of 6,680 compounds with no data on their effectiveness against the germ.
In an hour and a half, the program reduced the list to 240 promising candidates. Testing in the lab found that nine of these were effective and that one, now called abaucin, was extremely potent. While doing lab tests on 240 compounds sounds like a lot of work, it is better than testing nearly 6,700.
Interestingly, the new antibiotic seems only to be effective against the target microbe, which is a plus. It isn’t available for people yet and may not be for some time — drug testing being what it is. However, this is still a great example of how machine learning can augment human brainpower, letting scientists and others focus on what’s really important.
WHO identified acinetobacter baumannii as one of the major superbugs threatening the world, so a weapon against it would be very welcome. You can hope that this technique will drastically cut the time involved in developing new drugs. It also makes you wonder if there are other fields where AI techniques could cull out alternatives quickly, allowing humans to focus on the more promising candidates.
Want to catch up on machine learning algorithms? Google can help. Or dive into an even longer course.
Rapidly analyzing samples for the presence of bacteria and similar organic structures is generally quite a time-intensive process, with often the requirement of a cell culture being developed. Proposed by Fareeha Safir and colleagues in Nano Lettersis a method to use an acoustic droplet printer combined with Raman spectroscopy. Advantages of this method are a high throughput, which could make analysis of samples at sewage installations, hospitals and laboratories significantly faster.
Glowing animals are reasonably common ranging from fireflies to railroad worms. In the case of the French street lighting, Glowee is using a marine bacterium known as aliivibrio fischeri. A salt-water tube contains nutrients and when air is flowing through the tube, the bacteria glow with a cool turquoise light. The bacteria enter an anaerobic state and stop glowing if you shut off the air.
When you’ve got a diabetic in your life, there are few moments in any day that are free from thoughts about insulin. Insulin is literally the first coherent thought I have every morning, when I check my daughter’s blood glucose level while she’s still asleep, and the last thought as I turn out the lights, making sure she has enough in her insulin pump to get through the night. And in between, with the constant need to calculate dosing, adjust levels, add corrections for an unexpected snack, or just looking in the fridge and counting up the number of backup vials we have on hand, insulin is a frequent if often unwanted intruder on my thoughts.
And now, as my daughter gets older and seeks like any teenager to become more independent, new thoughts about insulin have started to crop up. Insulin is expensive, and while we have excellent insurance, that can always change in a heartbeat. But even if it does, the insulin must flow — she has no choice in the matter. And so I thought it would be instructional to take a look at how insulin is made on a commercial scale, in the context of a growing movement of biohackers who are looking to build a more distributed system of insulin production. Their goal is to make insulin affordable, and with a vested interest, I want to know if they’ve got any chance of making that goal a reality.
How exactly does salt help? The very fine salt coating deposited on the fibers of a mask’s filtration layer first dissolves on contact with airborne pathogens, then undergoes evaporation-induced recrystallization. Pathogens caught in the filter are therefore exposed to an increasingly-high concentration saline solution and are then physically damaged. There is a bit of a trick to getting the salt deposited evenly on the polypropylene filter fibers, since the synthetic fibers are naturally hydrophobic, but a wetting process takes care of that.
The salt coating on the fibers is very fine, doesn’t affect breathability of the mask, and has been shown to be effective even in harsh environments. The research paper states that “salt coatings retained the pathogen inactivation capability at harsh environmental conditions (37 °C and a relative humidity of 70%, 80% and 90%).”
These last few weeks we’ve all been reminded about the importance of washing our hands. It’s not complicated: you just need soap, water, and about 30 seconds worth of effort. In a pinch you can even use an alcohol-based hand sanitizer. But what if there was an even better way of killing bacteria and germs on our hands? One that’s easy, fast, and doesn’t even require you to touch anything. There might be, if you’ve got a high voltage generator laying around.
In his latest video, [Jay Bowles] proposes a novel concept: using the ozone generated by high-voltage corona discharge for rapid and complete hand sterilization. He explains that there’s plenty of research demonstrating the effectiveness of ozone gas a decontamination agent, and since it’s produced in abundance by coronal discharge, the high-voltage generators of the sort he experiments with could double as visually striking hand sanitizers.
Looking to test this theory, [Jay] sets up an experiment using agar plates. He inoculates half of the plates with swabs that he rubbed on his unwashed hands, and then repeats the process after passing his hands over the high-voltage generator for about 15 seconds. The plates were then stored at a relatively constant 23°C (75°F), thanks to the use of his microwave as a makeshift incubator. After 48 hours, the difference between the two sets of plates is pretty striking.
Despite what appears to be the nearly complete eradication of bacteria on his hands after exposing them to the ozone generator, [Jay] is quick to point out that he’s not trying to give out any medical advice with this video. This simple experiment doesn’t cover all forms of bacteria, and he doesn’t have the facilities to test the method against viruses. The safest thing you can do right now is follow the guidelines from agencies like the CDC and just wash your hands the old fashioned way; but the concept outlined here certainly looks worthy of further discussion and experimentation.