Wednesday August 21, 2019

Researchers Develop a Tool That Attracts and Captures Female Mosquitoes Looking for Site to Lay Eggs

AGO traps are a novel chemical-free, effective approach to control Aedes aegypti (Yellow fever mosquito) populations

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Researchers, Tool, Mosquitoes
The study, published in the journal PLOS, shows that Autocidal Gravid Ovitrap (AGP trap) successfully protected people from getting infected with the chikungunya virus in Puerto Rico. Pixabay

Researchers have developed a tool that attracts and captures female mosquitoes looking for a site to lay eggs, which in the future may help curb the chikungunya virus.

The study, published in the journal PLOS, shows that Autocidal Gravid Ovitrap (AGP trap) successfully protected people from getting infected with the chikungunya virus in Puerto Rico.

“AGO traps are a novel chemical-free, effective approach to control Aedes aegypti (Yellow fever mosquito) populations and provide protection from infection with the pathogens that these mosquitoes transmit,” said researchers from the US Centers for Disease Control and Prevention (CDC).

“Further evaluations should determine if AGO traps are sustainable and effective in large scale community trials,” they said.

Researchers, Tool, Mosquitoes
Researchers have developed a tool that attracts and captures female mosquitoes looking for a site to lay eggs, which in the future may help curb the chikungunya virus. Pixabay

The lack of effective tools to control Aedes aegypti mosquito populations has resulted in the continued expansion of the dengue virus, zika virus and chikungunya virus.

For the study, the researchers randomly selected 290 households in Puerto Rican communities that had AGO trap interventions and 349 households in communities without AGO traps. From intervention communities, 175 household members were analysed and 152 from non-intervention communities.

Blood samples were collected from each participant to detect chikungunya virus infection and surveys recorded demographic information as well as data on mosquito repellent and bed net use and frequency of mosquito bites.

A total of 114 participants (34.9 per cent) were seropositive for the chikungunya virus. Among people who spent most of their daytime hours inside the community they lived in, 10.3 per cent were seropositive for chikungunya virus in communities with AGO traps whereas 48.7 per cent were positive for chikungunya virus in communities without traps.

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Among all participants, including those who did not spend as much daylight time within the community, 26.1 per cent were seropositive for the chikungunya virus in the intervention communities and 43.8 per cent were positive in communities without traps. (IANS)

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Researchers Develop AI-driven System to Curb ‘Deepfake’ Videos

Roy-Chowdhury, however, thinks we still have a long way to go before automated tools can detect “deepfake” videos in the wild

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Artificial Intelligence Bot
Artificial Intelligence Bot. Pixabay

At a time when “deepfake” videos become a new threat to users’ privacy, a team of Indian-origin researchers has developed Artificial Intelligence (AI)-driven deep neural network that can identify manipulated images at the pixel level with high precision.

Realistic videos that map the facial expressions of one person onto those of another — known as “deepfakes”, present a formidable political weapon in the hands of nation-state bad actors.

Led by Amit Roy-Chowdhury, professor of electrical and computer engineering at the University of California, Riverside, the team is currently working on still images but this can help them detect “deepfake” videos.

“We trained the system to distinguish between manipulated and nonmanipulated images and now if you give it a new image, it is able to provide a probability that that image is manipulated or not, and to localize the region of the image where the manipulation occurred,” said Roy-Chowdhury.

A deep neural network is what AI researchers call computer systems that have been trained to do specific tasks, in this case, recognize altered images.

These networks are organized in connected layers; “architecture” refers to the number of layers and structure of the connections between them.

While this might fool the naked eye, when examined pixel by pixel, the boundaries of the inserted object are different.

For example, they are often smoother than the natural objects.

artificial intelligence, nobel prize
“Artificial intelligence is now one of the fastest-growing areas in all of science and one of the most talked-about topics in society.” VOA

By detecting boundaries of inserted and removed objects, a computer should be able to identify altered images.

The researchers tested the neural network with a set of images it had never seen before, and it detected the altered ones most of the time. It even spotted the manipulated region.

“If you can understand the characteristics in a still image, in a video it’s basically just putting still images together one after another,” explained Roy-Chowdhury in a paper published in the journal IEEE Transactions on Image Processing.

“The more fundamental challenge is probably figuring out whether a frame in a video is manipulated or not”.

Also Read: TikTok Testing New Features Inspired by Instagram

Even a single manipulated frame would raise a red flag.

Roy-Chowdhury, however, thinks we still have a long way to go before automated tools can detect “deepfake” videos in the wild.

“This is kind of a cat and mouse game. This whole area of cybersecurity is in some ways trying to find better defense mechanisms, but then the attacker also finds better mechanisms.” (IANS)