By CARLA K. JOHNSON, Associated Press

Women should have the opportunity to gather their very own test samples for cervical cancer screening, an influential health panel said on Tuesday.

Draft recommendations from the US Preventive Services Task Force aim to get more people screened and spread the word that girls can collect their very own vaginal samples for testing cancer-causing HPV.

Women of their 20s should still get a Pap test every three years. But after that – from ages 30 to 65 – women can get an HPV test every five years, the panel said.

And these HPV tests will be performed on samples collected either by a health care provider or by the patient themselves in a mobile clinic or doctor's office. Women ages 30 to 65 can still decide to have a Pap test by a health care provider every three years or a Pap test plus HPV test every five years.

“I am very confident that self-collection will help more women be screened and we can further reduce the burden of cervical cancer in women,” said Task Force Vice Chair Dr. John Wong of Tufts University School of Medicine.

Earlier this 12 months, US regulators expanded using two HPV test kits Include self-collection. Studies show that girls and doctors collect samples with similar accuracy.

Currently, the tests are only intended to be used in healthcare settings; Home testing may very well be on the horizon.

HPV or human papillomavirus could be very common and is transmitted through sex. Most HPV infections clear up on their very own, but persistent infection can result in cervical cancer. Most cervical cancers occur in women who’re inadequately screened, diagnosed or treated.

To collect a sample, a swab or brush from the kit is inserted into the vagina and rotated. It is placed in a tube and processed in a laboratory.

The HPV tests detect high-risk kinds of the virus. In contrast, a Pap test looks for abnormal cells within the cervix.

Australia, Denmark, the Netherlands and Sweden already use self-collection for early detection of cervical cancer.

Cervical cancer deaths have declined within the U.S. over the past decade, and there’s an HPV vaccine really useful for pre-teens Prevent cancer in men and women. Still, nearly 14,000 recent cases of cervical cancer were diagnosed within the U.S. this 12 months and there have been an estimated 4,360 deaths.

Black women, Native American women and Hispanic women still have higher mortality rates in comparison with white women. The mortality rate can also be above average for girls living in distant areas.

For many ladies, transgender men and non-binary people, collecting your personal sample could also be more comfortable than having a health care provider use stirrups and speculums.

The draft suggestion stays largely the identical as that of the group previous recommendations as of 2018. For example, women under 21 don’t have to be screened for cervical cancer. This also applies to women over 65 who’ve had regular check-ups with normal results. And women of any age who’ve had a complete hysterectomy don’t have to be examined.

The recommendations will be commented on until January thirteenth.

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Originally published: December 10, 2024 at 11:41 am PST

LOS ANGELES – Following the outbreak of recent wildfires in California, Google announced that it can invest $13 million next 12 months to enhance satellite imaging to assist track and detect wildfires.

FireSat, a constellation of greater than 50 satellites, will have the opportunity to detect wildfires the dimensions of a classroom (about 5 by 5 meters). The first satellite can be launched in early 2025. The media giant announced on MondayFire authorities currently depend on satellite imagery to detect wildfires, but only once they are in regards to the size of a football field or multiple hectare.

“We realized that combining satellites with machine learning and artificial intelligence provides the perfect platform to generate real-time operational information on fires,” said Christopher Van Arsdale, head of the Google Research Climate & Energy Group and chair of the Earth Fire Alliance, in a video announcement.

The initiative is led by the Earth Fire Alliance, a non-profit organization founded in May to develop FireSat and create wildfire data sets. The initiative is funded by Google and the Gordon and Betty Moore Foundation.

In satellite images, clouds or smokestacks could currently be mistaken for fires, Van Arsdale said. Using machine learning, a team at Google built a camera that optimized the technology for fire detection, and the team tested it over controlled fire areas.

The satellite constellation will have the opportunity to supply firefighters with updated satellite images every 20 minutes, helping to extinguish fires before they turn into large and devastating.

Authorities in California have already begun using AI to raised track wildfires. The California Department of Forestry and Fire Protection has partnered with UC San Diego to Development of a brand new AI pilot project with 1,039 high-resolution cameras on towers and mountaintops across the state to search for smoke and other anomalies and alert Cal Fire.

Jason Forthhofer, a mechanical engineer with the U.S. Forest Service, said the FireSat project may even help collect data to raised train firefighters to cope with wildfires and understand how they’re affected by changing conditions.

“We will be able to teach firefighters the impact that drought, wind or a change in fuel can have on a fire. Because we have seen so many firefighters die in fires because they did not anticipate the consequences of the fire,” he said.

Southern California is currently experiencing an especially energetic wildfire season. three fast-spreading fires burning greater than 40,000 hectares of land Thousands needed to be evacuated and dozens of buildings were destroyed.

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©2024 Los Angeles Times. Visit www.latimes.com. Distributed by Tribune Content Agency, LLC.

Originally published: September 17, 2024 at 10:40 am

Deepfakes – essentially putting words into another person's mouth in a really believable way – have gotten more sophisticated and harder to detect each day. Current examples of deepfakes include: Taylor Swift nude picturesa Audio recording of President Joe Biden telling New Hampshire residents to not vote, and a Video of Ukrainian President Volodymyr Zelensky called on his troops to put down their arms.

Although corporations have developed detectors to detect deepfakes, studies have shown this to be the case Distortions in the information The methods used to coach these tools may end in inappropriately targeting certain populations.

My team and I even have discovered recent methods that improve each the fairness and accuracy of deepfake detection algorithms.

To do that, we used a big dataset of facial fakes, which allows researchers like us to coach our deep learning approaches. Our work is predicated on the state-of-the-art Xception detection algorithm, a widespread foundation for deepfake detection systems and might detect deepfakes with an accuracy of 91.5%.

We have developed two separate deepfake detection methods is meant to advertise justice.

One focused on making the algorithm more sensitive to demographic diversity by labeling records by gender and race to attenuate errors in underrepresented groups.

The other aimed to enhance equity without counting on demographic labels, as a substitute specializing in characteristics invisible to the human eye.

It turned out that the primary method worked best. It increased accuracy rates from 91.5% to 94.17%, which was a bigger increase than our second method in addition to several others we tested. In addition, accuracy was increased while improving fairness, which was our important focus.

We consider that fairness and accuracy are critical if the general public is to just accept artificial intelligence technology. When large language models like ChatGPT “hallucinate,” they’ll maintain erroneous information. This affects public trust and safety.

Likewise, fake images and videos can undermine AI adoption if they can not be detected quickly and accurately. An essential aspect of that is improving the fairness of those detection algorithms in order that certain demographic groups aren’t disproportionately harmed.

Our research addresses the fairness of deepfake detection algorithms, not only attempting to balance the information. It offers a brand new approach to algorithm design that takes demographic fairness into consideration as a core aspect.