Movie Encoded In DNA Is The First Step Toward Datalogging With Living Cells

While DNA is a reasonably good storage medium, it’s not particularly fast, cheap, or convenient to read and write to.

What if living cells could simplify that by recording useful data into their own DNA for later analysis? At Harvard Medical School, scientists are working towards this goal by using CRISPR to encode and retrieve a short video in bacterial cells.

CRISPR is part of the immune system of many bacteria, and works by storing sequences of viral DNA in a specific location to identify and eliminate viral infections. As a tool for genetic engineering, it’s cheaper and has fewer drawbacks than previous techniques.

Besides generating living rickrolls and DMCA violations, what is this good for? Cheap, self-replicating sensors. [Seth Shipman], part of the team of scientists at Harvard, explains in an interview below a number of possible applications. His focus is engineering cells to act as a noninvasive data acquisition tool to study neurobiology, for example by using engineered neurons to record their developmental history.

It’s possible to see how this technique can be used more broadly and outside an academic context. Presently, biosensors generally use electric or fluorescent transducers to relay a detection event. By recording data over time in the DNA of living cells, biosensors could become much cheaper and contain intrinsic datalogging. Possible applications could include long-term metabolite (e.g. glucose) monitors, chemical detectors, and quality control.

It’s worth noting that this technique is only at the proof of concept stage. Data was recorded and retrieved manually by the scientists into the bacterial genome with 90% accuracy, demonstrating that if cells can be engineered to record data themselves, accuracy and capacity are high enough for practical applications.

That being said, if anyone is working on a MEncoder or ffmpeg command line option for this, let us know in the comments.

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Flying Defibrillators

It’s a sad reality that, by and large, cardiopulmonary resuscitation (CPR) doesn’t save lives. Despite all the “you could save a life” marketing aimed at getting people certified in CPR, the instances where even the prompt application of the correct technique results in a save are vanishingly rare, and limited mostly to witnessed arrests in a hospital. Speaking from personal experience, few things are sadder than arriving on-scene as a first responder to see CPR being performed by a husband on his wife and knowing that no matter what we do, it’s not going to end well.

The problem is one of time. Hearts only rarely just stop beating outright; usually some kind of arrhythmia first causes the heart to beat ineffectively, leading to hypoxia and loss of consciousness. From there it’s about a four-minute trip to brain death, but in that brief window chances are pretty good that the heart can be restarted. That’s why witnessed cardiac arrests in a hospital have better survival rates — the needed electric reboot of the heart with a defibrillator is only as far away as the nearest crash cart.

The advent of the automatic external defibrillator (AED) has increased the odds for survival of out-of-hospital cardiac arrest (OHCA), but the penetration of AEDs into public settings is far from complete enough to put one within a few minutes reach of everyone who might need one. So it’s only natural that thoughts would turn to delivering AEDs to cardiac incidents by drones. It seems like a great idea, but will it work? Continue reading “Flying Defibrillators”

Best Product Entry: A HSDK For Ultrasound Imaging

As an entry into this year’s Best Product portion of the Hackaday Prize, [kelu124] is developing a hardware and software development kit for ultrasound imaging.

Ultrasound is one of the primary tools used in modern diagnostic medicine. Head to the doctor with abdominal pain, and you can bet you’ll be seeing the business end of an ultrasound system. While Ultrasound systems have gotten cheaper, they aren’t something everyone has in the home yet.  [kelu124] is working to change that by building a hardware and software development kit which can be used to explore ultrasound systems. This isn’t [kleu124’s] first rodeo. HSDK builds upon and simplifies Murgen, his first open source ultrasound, and an entry in the 2016 Hackaday prize. [kelu124’s] goal is to “simplify everything, making it more robust and more user-friendly”.

The system is driven by a Raspberry Pi Zero W. A custom carrier board connects the Pi to the pulser block, which sends out the ultrasonic pings, and the analog front end, which receives the reflected signals. The receiver is called Goblin, and is a custom PCB designed [kelu124] designed himself. It uses a variable gain amplifier to bring reflected ultrasound signals up out of the noise.

A system like this would be a boon both to hackers and medical professionals working in the field. Ultrasonics can do more than just imaging. You can decrease healing time with ultrasonics, or even levitate things!

Using A Thermal Camera To Spot A Broken Wrist

Chemist and Biochemist [Thunderf00t] has shown us a really interesting video in which you can spot the wrist he broke 10 years ago using a thermal camera.

He was on an exercise bike while filming himself on a high-resolution thermal camera, As his body started to heat up he noticed that one hand was not dumping as much heat as the other. In fact one was dumping very little heat. Being a man of science he knew there must be some explanation for this. He eventually came to the conclusion that during a nasty wrist breaking incident about 10 years ago it must have affected the blood-flow to that hand, Which would go on to produce these type of results on a thermal camera while exercising.

Using thermal camera’s to spot fractures in the extremities is nothing new as it has the benefit of eliminating radiation exposure for patients, But it’s not as detailed as an X-ray or as cool as fluoroscopy and is only useful for bones near the surface of the skin.  It’s still great that you can visualize this for yourself and even after 10 years still notice a significant difference.

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Using Machine Learning To Cut Down Surgical Videos

Recording video of medical surgeries is a great way to both educate doctors in training and identify process improvements. However, surgeries can be very time consuming, and it can be a gargantuan task to sort through endless hours of video searching for relevant points where the action happens. To tackle this issue, researchers at MIT have used machine learning techniques to analyse videos of surgical procedures.

There’s some fairly serious mathematics behind this sort of videographic analysis.

The machine learning algorithm needed to be trained to identify the relevant parts of surgical videos. To do this, the laparoscopic surgeries being investigated were split up into distinct stages, each relating to a different part of the surgical process. Researchers would then watch recordings of prior surgeries and mark the start of each stage. This data was used to train the model which was then used to sift through other recordings to capture the key moments of each surgery.

The time-saving advantages of such technology could be applied to a great many fields – such an algorithm could be put to great use to sort through hours of uneventful security footage looking for anomalies, or rapidly cut together holiday footage so you only have to see the good parts. We’d love to see the researchers release footage showing the algorithm’s work – thus far, all we have to go off is the project paper.

If you’re thirsty for more machine learning knowledge, read up on the state of working with neural networks in 2017.

Listen To Your Body

[John Miller] has the perfect response next time he complains about an ache or pain and one of his friends says, “You should listen to your body!” As you can see in the video below, he already does. Using two 9V batteries and some instrumentation amplifiers, [John] built an electromyography (EMG) rig.

If you haven’t heard of EMG, think of EEG or EKG, but for muscles instead of your brain or your heart. The LT1167 amplifier is well-suited for this application and even has a data sheet showing how to create an EMG circuit. [John] also used some more garden-variety op amps and the ubiquitous LM386N chip for audio amplification.

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Getting Data Off Proprietary Glucometers Gets A Little Easier

Glucometers (which measure glucose levels in blood) are medical devices familiar to diabetics, and notorious for being proprietary. Gentoo Linux developer [Flameeyes] has some good news about his open source tool to read and export data from a growing variety of glucometers. For [Flameeyes], the process started four years ago when he needed to send his glucometer readings to his doctor and ended up writing his own tool. Previously it was for Linux only, but now has Windows support.

Glucometers use a variety of different data interfaces, and even similar glucometers from the same manufacturer can use different protocols. Getting the data is one thing, but more is needed. [Flameeyes] admits that the tool is still crude in many ways, lacking useful features such as HTML output. Visualization and analysis are missing as well. If you’re interested in seeing if you can help, head over to the GitHub repository for glucomerutils. Also needed are details on protocols used by different devices; [Flameeyes] has only been able to reverse-engineer the protocols of meters he owns.

Speaking of glucometers, there is a project for a Universal Glucometer which aims to be able to use test strips from any manufacturer without needing to purchase a different meter.

Thanks for the tip, [Stuart]!