A safer way to train detection dogs

K9 chemistry: A safer way to train detection dogs
A detection dog in training. Credit: Courtesy of Auburn University College of Veterinary Medicine

Trained dogs are incredible chemical sensors, far better at detecting explosives, narcotics and other substances than even the most advanced technological device. But one challenge is that dogs have to be trained, and training them with real hazardous substances can be inconvenient and dangerous.


NIST scientists have been working to solve this problem using a jello-like material called polydimethylsiloxane, or PDMS for short. PDMS absorbs odors and releases them slowly over time. Enclose it in a container with an explosive or narcotic for a few weeks until it absorbs the odors, and you can then use it to safely train dogs to detect the real thing.

But a few weeks is a long time, and now, NIST researchers have developed a faster way to infuse PDMS with vapors. In the journal Forensic Chemistry, they describe warming compounds found in explosives, causing them to release vapors more quickly, then capturing those vapors with PDMS that is maintained at a cooler temperature, which allows it to absorb vapors more readily. This two-temperature method cut the time it took to “charge” PDMS training aids from a few weeks to a few days.

“That time savings can be critical,” said NIST research chemist Bill MacCrehan. “If terrorists are using a new type of explosive, you don’t want to wait a month for the training aids to be ready.”

For this experiment, MacCrehan infused PDMS with vapors from dinitrotoluene (DNT), which is a low-level contaminant present in TNT explosives but the main odorant that dogs respond to when detecting TNT. He also infused PDMS with vapors from a small quantity of TNT. Co-authors at the Auburn University College of Veterinary Medicine then demonstrated that trained detection dogs responded to the DNT-infused PDMS training aids as if they were real TNT.

While this study focused on DNT as a proof of concept, MacCrehan says he believes the two-temperature method will also work with other explosives and with narcotics such as fentanyl. Some forms of fentanyl are so potent that inhaling a small amount can be harmful or fatal to humans and dogs. But by controlling how much vapor the PDMS absorbs, MacCrehan says, it should be possible to create safe training aids for fentanyl.

Other safe training aids already exist. Some are prepared by dissolving explosives and applying the solution to glass beads, for example. “But most have not been widely accepted in the canine detection community because their effectiveness has not been proven,” said Paul Waggoner, a co-author and co-director of Auburn’s Canine Performance Sciences Program. “If you put an explosive in a solvent, the dogs might actually be detecting the solvent, not the explosive.”

To test the two-temperature method, MacCrehan devised a PDMS “charging station” with a hot plate on one side and a cooling plate on the other (so the “hot stays hot and the cool stays cool,” as a 1980s commercial jingle put it). He prepared various samples

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Monash University and The Alfred to develop AI-based superbug detection system

Monash University and Alfred Hospital are developing an artificial intelligence-based system to improve the way superbugs are diagnosed, treated, and prevented. 
 
According to Monash University professor of digital health Christopher Bain, infections from superbugs kill 700,000 people every year and by 2050, the world could see 10 million deaths annually from previously treatable diseases.
  
Superbugs are created when microbes evolve to become immune from the effects of antimicrobials.
 
The project, which will be mainly based at The Alfred, has received AU$3.4 million from the federal government’s Medical Research Future fund.
 
According to the project’s lead researcher, Antony Peleg, the project will look to integrate genomics, electronic healthcare data, and AI technologies to address antimicrobial resistance in the healthcare system. Specifically, it will leverage tens of thousands of data points per patient and infecting pathogens to help predict treatment responses and patient outcomes.
 
“This project will push the boundaries of what can be achieved in healthcare and how new technologies can be applied to understand how superbugs infect humans and the way they are transmitted within a hospital system,” Peleg said.

See also: Monash University researchers develop AI aimed at improving suicide prevention

In addition to providing earlier detection of antimicrobial resistance, the two organisations are also hoping the system will be able to create personalised treatment for patients and prevent outbreaks. 
 
Elsewhere in Australia’s health sector, AustCyber has provided AU$500,000 in funding to cybersecurity startup Haventec to develop a new health consent system.
 
The system, called eConsent for Genomics, is aimed at improving how healthcare providers, service providers, and patients securely store and consent to personal health information.
 
The funding will come from the AustCyber Projects Fund, which is a three-year AU$15 million federal government initiative designed to help the Australian cybersecurity industry grow both locally and globally.

The system is expected to cost around AU$1 million to build, with Haventec and consortium partner 23Strands to provide the remaining AU$500,000. 
 
According to Haventec, the development of eConsent for Genomics comes at a critical time as current models for storing personal health information are consistently failing with the health sector regularly topping the list of notifiable data breaches.
 
Partnering with 23Strands, Haventec will also use the new consent system in a research project focused on COVID-19 patients. The research will look to correlate negative and positive health outcomes to specific DNA profiles, which it hopes will improve predictions regarding how individuals will react if they become infected with COVID-19.

Monash Uni publishes ethics analysis of agri-robots

Monash University on Monday also published a report focusing on the ethical and policy issues behind using robots in agriculture.
 
The report was created as the authors, Monash University Philosophy professor Robert Sparrow and philosophy research fellow Dr Mark Howard, said little attention has been paid to the ethical and policy challenges surrounding agriculture being increasingly automated. 

“People weren’t thinking about or talking about, such as unintended consequences, or what might happen in life and when things don’t work out perfectly,” Sparrow told ZDNet. 
 
Currently,

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