EPA eliminates research and development office, begins layoffs
The agency's Office of Research and Development has long provided the scientific underpinnings for EPA's mission to protect the environment and human health. The EPA said in May it would shift its scientific expertise and research efforts to program offices that focus on major issues like air and water.
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Gizmodo
27 minutes ago
- Gizmodo
Rocket Lab Has Some Genuine Competition for SpaceX, but It Can't Reach the Launchpad
California-based startup Rocket Lab is looking to compete with industry leader SpaceX with its upcoming launch vehicle, Neutron. But before it can debut its reusable rocket later this year, the company has to figure out a way to transport Neutron's components to the southern tip of Wallops Island in Virginia. Rocket Lab is awaiting approval to dredge a permanent channel to the Mid-Atlantic Regional Spaceport (MARS) on Wallops Island, a spaceport surrounded by shallow waters and scarce infrastructure, TechCrunch reported. The company is racing to meet its deadline for Neutron's inaugural liftoff in September, but the rocket still needs to go through final preparations on its launchpad before it can launch, and it has to make it onto the island first. Stuck in the mud, Rocket Lab is contemplating an old-timey sailing technique known as kedging. The Virginia Commercial Spaceflight Authority operates MARS, a commercial launch site, in partnership with NASA's Wallops Flight Facility. The site represents an ideal alternative to Cape Canaveral in Florida, which is experiencing increased congestion due to the steadily rising number of rocket launches. Rocket Lab began constructing its second launch site, called Launch Complex-2, for its Electron rocket in 2019, which has so far carried out four missions from its Virginia launchpad. In 2023, Rocket Lab began construction of a new launch site for its Neutron rocket, Launch Complex 3. Rocket Lab has already spent millions on Launch Complex 3, and it plans on spending another $5 million or so to dredge Sloop Gut, a channel in Accomack County, Virginia, that serves as a navigation route on Wallops Island. The company wants to remove sediment from the channel and dredge around 5,300 feet (1,615 meters), deepening it to 7 feet (2 meters) below the water to accommodate large barges carrying its rocket components to the island. Its request was approved by the Virginia Marine Resources Commission in May, but the company is still waiting for the green light from the Army Corps of Engineers, according to TechCrunch. In the meantime, Rocket Lab is asking federal regulators for permission to use a technique called kedging, which involves using a series of anchors to haul a ship across shallow water. The company hopes the method will help transport Neutron's rocket parts in time for a launch this year. In case its request isn't approved in time, Rocket Lab suggests it can use ramps and cranes to transport Neutron's hardware across the waters or use a boat ramp. The clock is ticking for Rocket Lab to launch its Neutron rocket on time, a medium-lift launch vehicle capable of launching 13 metric tons to low Earth orbit. Neutron is Rocket Lab's answer to SpaceX's Falcon 9 (which can launch up to 22 metric tons to low Earth orbit), hoping to provide the industry with an alternative to sending satellites to space.
Fox News
28 minutes ago
- Fox News
Two cancer drugs show promise in reversing Alzheimer's devastating effects
NEW You can now listen to Fox News articles! Two cancer drugs could potentially slow or even reverse the effects of Alzheimer's disease, a new study suggests. Researchers at the University of California San Francisco (UCSF) explored how the common dementia changes gene expression (which genes are turned on or off) in certain brain cells, according to a press release from the university. Next, they looked at which existing FDA-approved drugs might counteract, or reverse, those changes. ALZHEIMER'S RISK COULD RISE WITH SPECIFIC SLEEP PATTERN, EXPERTS WARN In analyzing millions of electronic medical records of adults over 65, the researchers identified two medications that appeared to reduce the likelihood of Alzheimer's in the patients who took them. The medications — letrozone and irinotecan — are both approved to treat cancer. Letrozole is a breast cancer medication and irinotecan treats colon and lung cancer. When the scientists tested a combination of both medications in mice, they noted a reversal of the gene expression changes that were initiated by Alzheimer's. They also discovered a reduction in tau protein clumps in the brain — a key marker of Alzheimer's — and an improvement in learning and memory. "Alzheimer's disease comes with complex changes to the brain, which has made it tough to study and treat, but our computational tools opened up the possibility of tackling the complexity directly," said co-senior author Marina Sirota, PhD, the interim director of the UCSF Bakar Computational Health Sciences Institute and professor of pediatrics, in the press release. EATING THESE COMMON FOODS COULD REDUCE ALZHEIMER'S RISK, EXPERTS SAY "We're excited that our computational approach led us to a potential combination therapy for Alzheimer's based on existing FDA-approved medications." The results of the study, which was funded in part by the National Institutes of Health (NIH) and the National Science Foundation, were published in the journal Cell on July 21. While the study's outcome was promising, the researchers acknowledged several limitations, including the fact that the database they used to identify possible drugs was built from cancer cells, not brain cells. They also noted that animal models were used. "Although necessary, validation in animal models may not fully recapitulate human biology," the researchers wrote. MEN FACE DOUBLE DEMENTIA RISK IF THEY HAVE A HIDDEN GENETIC MUTATION There was also a noticeable gender difference in response to the medications, with male mice responding better than females. "As a hormone modulator, letrozole might contribute to this sex difference," the team noted. "However, the analysis remains inconclusive due to the small number of male letrozole users." The electronic medical records could also present limitations, "as data tend to be sparse and are not collected with specific research in mind." "We're hopeful this can be swiftly translated into a real solution for millions of patients with Alzheimer's." More than seven million people in the U.S. are currently living with Alzheimer's, according to the Alzheimer's Association. This number is expected to approach 13 million by the year 2050. There are currently only two disease-modifying medications that have been FDA-approved to treat Alzheimer's, UCSF states. Lecanemab (Leqembi) and donanemab (Kisunla) are both monoclonal antibodies that are administered via IV infusions. CLICK HERE TO GET THE FOX NEWS APP They work by reducing the build-up of amyloid plaques in the brain, but they are only effective for those with early-stage Alzheimer's and have the potential for some serious side effects, according to experts. (Other Alzheimer's medications help with symptoms, but don't treat the underlying disease.) CLICK HERE TO SIGN UP FOR OUR HEALTH NEWSLETTER "Alzheimer's is likely the result of numerous alterations in many genes and proteins that, together, disrupt brain health," said co-senior study author Yadong Huang, M.D., PhD, professor of neurology and pathology at UCSF, in the release. "This makes it very challenging for drug development — which traditionally produces one drug for a single gene or protein that drives disease." Looking ahead, the researchers plan to start a clinical trial to explore the combined drugs' impact on human patients with Alzheimer's. "If completely independent data sources, such as single-cell expression data and clinical records, guide us to the same pathways and the same drugs, and then resolve Alzheimer's in a genetic model, then maybe we're onto something," Sirota said in the release. For more Health articles, visit "We're hopeful this can be swiftly translated into a real solution for millions of patients with Alzheimer's."
Fast Company
an hour ago
- Fast Company
Medical AI: fixing the bias problem
Tools powered by artificial intelligence (AI) promise to diagnose diseases faster, personalize treatments, and streamline hospital workflows. It's something that's been demonstrated in multiple studies: AI can outperform human doctors in specific tasks, including reading X-rays, predicting patient risk, and providing diagnostics assistance. Beneath the surface of this technological revolution, though, lies a critical flaw: bias. A groundbreaking new study from the Icahn School of Medicine at Mount Sinai tested most of the major LLM families and found that leading models exhibit clear bias based on race, sex, and income level, among other attributes. This is the latest example of bias in medicine. Before LLMs became mainstream, numerous clinical diagnostics systems were found to contain biased algorithms derived from skewed data or incorrect assumptions. These included algorithms for pain management, cardiovascular risk, and mortality in intensive care units, among other areas. To provide a comprehensive view of bias in common LLMs, the Icahn researchers conducted an analysis involving nine prominent LLMs, examining over 1.7 million model-generated outputs. They used a dataset of 1,000 emergency department cases, comprising 500 real and 500 synthetic scenarios. Each case was presented to the LLMs in 32 distinct variations: one control version with no sociodemographic identifiers, and 31 versions where labels indicating race, gender identity, income level, housing status, and sexual orientation were added, individually or in combination. The underlying clinical details of the patient presentation were kept identical across all 32 variations of a given case. The LLMs were asked to provide clinical recommendations, which were compared against baseline recommendations from human reviewers and the model's output in response to the control cases. The results were eye-opening. Patients of certain races were six or seven times more likely to be flagged for mental health evaluations than the control group. Patients labeled as lower income were less likely to receive recommendations for advanced care. Sometimes, the LLMs exhibited explicit bias by citing the sociodemographic tag as part of its recommendation. The researchers concluded that the magnitude and consistency of these observed differences strongly suggested model-driven bias inherited from the data used to train the LLMs. The findings held across both proprietary and open-source models underscore a systemic issue and highlight the critical need for robust bias evaluation and mitigation strategies specifically tailored for LLMs in healthcare. If biases like these continue to make their way into deployed AI systems, real-world medical care will be unequal and less effective. The Icahn researcher's findings are not surprising. LLMs, trained on vast swathes of internet text data, inherently encode and can amplify societal biases related to age, gender, race, disability, and other factors. Other studies show that information about patient gender and race is encoded within the internal layers of LLMs and can be manipulated to alter outputs such as clinical vignette generation or downstream predictions like depression risk. That awareness has spurred the development of specific datasets, like BiasMD and DiseaseMatcher, to reduce biases in health-related LLM outputs. Bias can also have legal consequences. Medical bias in technology in the United States, to name one country, was made illegal under the Affordable Care Act. However, bias likely remains common because it is still hard to detect. When bias is alleged, litigation often follows. Major U.S. health insurance providers have faced multiple lawsuits alleging biased algorithms prevented necessary care or discriminated against certain groups. Reputational risk from this litigation can be substantial. ESSENTIAL MITIGATION STRATEGIES Every AI system under consideration for medical care tasks must be tested for evidence of bias. Forward-thinking medical AI companies should be able to demonstrate that they have tested for bias or that they have a specific mechanism for customers to perform such tests on a regular basis. Ongoing testing even after deployment is important because bias can emerge in subsequent training or reinforcement learning. For effective implementation, companies must establish quantitative bias metrics specific to each clinical use case and create diverse validation datasets that deliberately include all patient populations with sufficient numbers to train LLMs. Also, they must develop clear procedures for handling cases where bias is detected, including model retraining protocols and emergency shutdown procedures when necessary. Advances in AI technology provide hope. But for now, empowering humans in the loop to apply their own wisdom and intuition remains mission-critical if we are going to have medical AI that treat us all fairly. The super-early-rate deadline for Fast Company's Most Innovative Companies Awards is this Friday, July 25, at 11:59 p.m. PT. Apply today.
