The ozone layer has shown significant improvement, with the Antarctic ozone holes in 2024 being smaller than in prior years. New Report from the World Meteorological Organization (WMO).
This map depicts the size and shape of the Antarctic ozone hole on October 5th, 2022. Image credit: Earth Observatory by Joshua Stevens/NASA.
The depth of the Antarctic ozone hole in 2024 (which typically appears every spring) was below the average levels measured from 1990 to 2020, with the maximum ozone mass deficit recorded on September 29th at 46.1 million tons.
From 2020 to 2023, it remained smaller than a significantly larger hole.
Its development was relatively gradual, with ozone depletion slowing by September, followed by a quicker recovery after reaching the maximum deficit.
“This consistent progression is considered a strong indicator of early recovery in the Antarctic ozone holes,” stated WMO experts.
The alarm was initially sounded by scientists in 1975 when the WMO reported “changes in the ozone layer due to human activities and certain geophysical factors.”
If current policies remain in effect, the latest assessment for 2022 indicates that the ozone layer is projected to return to 1980 levels (prior to the appearance of ozone holes) around 2066, 2045 in the Arctic, and globally by 2045.
“Despite the significant success of the Montreal Protocol over the years, this effort remains ongoing, and continuous monitoring of stratospheric ozone and ozone-depleting substances is essential,” experts noted.
“WMO’s scientific research on the ozone layer spans decades,” remarked Celeste Sauro, WMO executive director.
“It relies on trust, international collaboration, and a commitment to free data exchange—fundamental principles of the world’s most successful environmental agreements.”
“To date, the Montreal Protocol has resulted in over 99% reduction in the production and consumption of controlled ozone-depleting substances used in refrigeration, air conditioning, fire foam, and even hairsprays.”
“Consequently, the ozone layer is on course to recover to 1980 levels by the middle of this century, significantly lowering the risk of ecosystem damage from skin cancer, cataracts, and UV overexposure.”
Medical professionals have created an artificial intelligence tool capable of predicting which men diagnosed with prostate cancer are likely to benefit from treatment, potentially lowering the risk of mortality.
Abiraterone is regarded as a revolutionary treatment for the condition, which is the most prevalent cancer among men in over 100 countries. It has already enabled countless individuals with advanced prostate cancer to enjoy extended lifespans.
Nonetheless, some nations, including the UK, have ceased offering this “remarkable” medication to men whose cancer has not metastasized.
Currently, teams from the US, UK, and Switzerland are developing AI assessments that determine which men are likely to gain from Abiraterone. This “promising” advancement enhances the healthcare system to allocate medications more effectively to suitable candidates while allowing others to avoid unnecessary treatments.
The AI test was unveiled in Chicago at the annual conference of the American Society of Clinical Oncology, the largest cancer conference globally.
Nick James, a professor specializing in prostate and bladder cancer research at the London Cancer Institute, serves as a consultant clinical oncologist at the Royal Marsden NHS Foundation Trust, where he leads the development team.
“Abiraterone has already greatly enhanced the prognosis for hundreds of thousands of men with advanced prostate cancer,” James stated. “We recognize that for many men whose cancer hasn’t spread yet, it can have significant implications.
“However, the treatment comes with side effects and necessitates additional monitoring for potential issues such as hypertension or liver abnormalities. It is extremely valuable to identify those most likely to truly benefit, as it may slightly elevate the risks of diabetes and heart complications.
“This research indicates that those who respond optimally to abiraterone, as well as those who fare well with standard treatments alone, can decide between hormone therapy and radiation therapy.”
The AI tool examines tumor images and identifies features that may not be discernible to the naked eye. Prostate Cancer UK, the Medical Research Council, and arterial funded teams analyzed biopsy images from over 1,000 men exhibiting high-risk prostate cancer that had not metastasized.
AI analysis pinpointed 25% of the men in the study who were most likely to gain from Abiraterone. For these individuals, the medication halved the risk of mortality.
In the study, patients received a score indicating a positive or negative biomarker. This was then compared with outcomes. Among those with biomarker-positive tumors, the risk of death was reduced from 17% to 9% after five years for one in four men.
For patients with biomarker-negative tumors, Abiraterone decreased the risk of death from 7% to 4%. The research team indicated this result was neither statistically nor clinically significant, meaning these men are better off with standard treatment alone and can avoid unnecessary therapies.
Professor Gert Attard, the research co-leader at UCL Cancer Institute, noted, “This study highlights that, within a sizable cohort of patients, new algorithms can be utilized to glean information from routinely available pathology slides to customize treatments to individual patients, thereby minimizing unnecessary interventions while maximizing the effectiveness of treatment.”
James mentioned that fewer men may require the medication than previously believed, suggesting that health services should contemplate administering it to men whose cancer hasn’t spread.
While it has been sanctioned for use within the NHS for advanced prostate cancer in England, it has yet to receive approval for newly diagnosed high-risk cases that have not metastasized. However, men with indications of high-risk cancer have had access to treatment in Scotland and Wales for two years.
“Abiraterone costs just £77 per pack compared to thousands for new treatments,” James remarked. “We sincerely hope this new research will clarify who truly benefits from this drug, especially given NHS England’s decision not to fund it for high-risk non-metastatic prostate cancer cases.”
Dr. Matthew Hobbs, research director at Prostate Cancer UK, termed the AI test as “promising.” He further elaborated:
The UK government is in the process of developing a predictive programme aimed at identifying potential murderers by utilizing personal data from individuals known to law enforcement authorities.
Researchers are utilizing algorithms to analyze data from thousands of individuals, including crime victims.
Originally named the “Murder Prediction Project,” the initiative has been renamed to “Share data to improve risk assessment” by the Ministry of Justice. While officials hope the project will enhance public safety, critics have labeled it as “chilling and dystopian.”
The existence of the project was brought to light by the advocacy group Statewatch, with details of its operations available through a Freedom of Information request.
Statewatch alleges that data from individuals without criminal convictions will be utilized in the project, including sensitive details related to self-harm and domestic abuse. Authorities vehemently deny this, stating they only collect data on individuals with at least one criminal conviction.
While the government maintains the project is solely for research purposes at this stage, detractors argue that the data used could introduce biases in predictions, particularly affecting ethnic minorities and low-income populations.
The project, commissioned during Rishi Snack’s tenure at the Prime Minister’s Office, analyzes crime data from various official sources, including the probation service and Greater Manchester Police prior to 2015.
Information processed includes names, dates of birth, gender, ethnicity, and unique identifiers on the police national database.
Statewatch’s claim regarding the inclusion of data from innocent individuals and those seeking police assistance is based on a data sharing agreement between the Ministry of Justice and Greater Manchester Police.
The shared data encompasses a range of personal information, including criminal convictions and details such as age at first reporting domestic violence or seeking police intervention.
Moreover, sensitive information categorized as “Special Categories of Personal Data” includes health indicators deemed predictive, mental health, addiction, and vulnerability data.
Responding to criticisms, a Ministry of Justice spokesperson stated: “This project is strictly for research purposes. It utilizes existing data from prison, probation, and police records of convicted offenders to enhance understanding of probationer risks.”
Current risk assessment tools used by correctional services will be supplemented with additional data sources to gauge effectiveness.
In summary, the Ministry of Justice asserts that the project aims to enhance risk assessment for serious crimes and ultimately contribute to public protection through improved analysis.
Pre-Lamp Disease is a potentially serious complication of pregnancy
Half Point Image/Getty Image
Pre-lamp syndrome can lead to many pregnancy complications, including death, but can be difficult to detect in the early stages of pregnancy. New blood tests can help doctors identify the risk of developing a pregnant individual’s condition before symptoms begin.
“We can narrow it down to four really high-risk pregnancies. That’s a big step.” Maneesh Jain at Mirvie, a California-based health startup.
Pre-salping syndrome is a type of hypertensive disorder (HDP) during pregnancy, which occurs when scientists are not sure exactly – occurs during placenta development. This can lead to high blood pressure and lead to cardiovascular disease, organ damage, seizures and even death. It can also cause harm to the developing fetus.
However, catching pre-lammosis and other HDP is difficult. This is because symptoms usually do not appear for at least 20 weeks after pregnancy. Sometimes, no signs are detected until work. It is difficult to monitor placenta development. This is because taking tissue samples from organs is very invasive.
New blood tests are relatively non-invasive and use RNA markers to predict whether someone may develop HDP. Specifically, this test focuses on specific genes PAPPA2 and CD163its overexpression was previously linked to HDP. The researchers wanted to see if they could detect this overexpression of blood samples.
Their validation studies of over 9,000 pregnant people suggest that they can: Jain says the test can be predicted with accuracy of over 99%, whether people without existing risk factors overexpress the gene and therefore are at a higher risk of EC presymptom or another HDP. Almost a quarter of participants without existing HDP risk factors overexpressed the gene.
People with a certain demographic (for example, those with a family history of preexisting hypertension or pre-sexual pre-lampsia) are known to be at a moderate risk of developing the condition, he says. Morten Rasmussen At Mirvie. But for many, it comes from the blue at first glance.
Once someone knows that they are at high risk of pre-lamps, they can take action to prevent this. Common interventions include taking medications like aspirin, switching to a Mediterranean diet, and monitoring your daily blood pressure.
However, the new test only looked at people between 17.5 and 22 weeks after pregnancy. “Ideally, you should start aspirin 16 weeks in advance.” Kathryn Gray At Washington University in Seattle. “So by the time most people get the results of this test, they’ve already missed that window.”
Mirvie plans to sell blood tests on the market soon. Once it’s on the market, the team hopes other scientists will use it to develop drugs that specifically target the expression of genes such as PAPPA2. Such molecular pinpoints “give a much better opportunity for treatment to be effective,” says Rasmussen.
Gray also hopes researchers will use Mirvie’s RNA bank data to further identify the genes behind the risk of prelammosis in certain people. She says narrowing down your search profile could reduce the cost of testing and make it affordable for more people.
The article was revised on April 8, 2025
This article has been revised to reflect the risks posed by pre-lammosis during pregnancy
Entomologists at Washington State University predict that the US Honeybee colony may decline by up to 70% by 2025.
The university revealed in a News Release that over the last decade, annual losses of Honeybee Colonies averaged between 40% and 50%. However, this year, a combination of factors such as nutritional deficiencies, mites infections, viral diseases, and potential pesticide exposure during the previous pollination season contributed to even higher losses.
Priya Chakrabarti Basu, assistant professor of health and pollinator behavior at WSU, expressed concern over the increasing losses, stating, “The demand for pollination remains high, putting pressure on beekeepers to maintain colonies to meet these needs.”
Flocking bees form clusters in Las Vegas trees. Gabe Ginsberg / Getty Images
Brandon Hopkins, professor of pollinator ecology at WSU, warned that higher colony losses could result in increased costs for farmers relying on bee colonies.
Hopkins added, “This level of national loss could potentially lead to beekeepers facing bankruptcy, affecting farmers who depended on them for pollination.”
The Honeybee industry had a production value of around $350 million in 2023, as reported by the Agriculture Department.
Hopkins noted that extreme Honeybee losses also pose a particular risk to the almond industry this year.
He stated, “The almond industry heavily relies on robust colonies, and this year, due to low supply, any beehives are in high demand.”
Reflecting on the situation, Hopkins added, “I haven’t seen a decline like this since the colony collapse in 2008.”
Initial predictions for the upcoming Atlantic hurricane season indicate that it could be particularly severe and potentially break records.
Colorado State University, a renowned center for hurricane and tropical weather forecasting, has released forecasts stating that there could be 11 hurricanes, with five of them potentially reaching Category 3, 4, or 5 status, which means wind speeds of at least 111 mph. In total, researchers anticipate 23 named storms for this season.
“This is the most accurate forecast we’ve made for April,” stated Colorado meteorologist and Atlantic hurricane forecaster Philip Klotzbach during a video news conference.
On average, an Atlantic hurricane season typically sees 14 named storms, seven hurricanes, and three major hurricanes (Category 3 or higher), according to the National Hurricane Center.
The primary reasons for the above-average expectations for the upcoming season (June 1 to November 30) are the unprecedented levels of warmth in the Atlantic Ocean and the natural fluctuations caused by La Niña. Ocean temperatures have hit record highs in the past year, enhancing the probability of potent storms and potentially intensifying them at a faster rate.
According to Colorado’s forecast, there is a 62% likelihood of a major hurricane striking the U.S. coastline, an increase of about 19% from the norm. However, this projection was disclosed earlier this year and will be updated as the season progresses. The National Oceanic and Atmospheric Administration has yet to release its forecast.
Other hurricane experts also express concerns about the combination of unnatural ocean warming and La Niña’s natural impacts.
“All signs point towards what could potentially be a highly active hurricane season in 2024, with very powerful hurricanes. That’s definitely something to be worried about,” remarked meteorologist and hurricane expert John Morales from NBC 6 South Florida.
Sea surface temperatures are climbing globally, setting new daily records for over a year. This trend has baffled marine scientists and is likely influenced by climate change. Some of the most significant temperature anomalies have been observed in the waters off the west coast of Africa, where many Atlantic hurricanes that hit the U.S. East Coast originate.
“The ocean heat content in the tropical eastern Atlantic is currently *3 months* ahead of the norm,” noted Brian McNoldy, a senior research scientist at the University of Miami’s Rosenstiel School of Ocean, Atmospheric, and Earth Sciences, in a tweet. In simpler terms, the ocean’s current heat levels resemble those of a typical July.
Ocean heat serves as fuel for extreme storms. If a hurricane’s winds intensify suddenly as it nears the coast, there is a heightened risk of rapid intensification. In recent years, there has been an observed uptick in such intensification. Last year, Hurricane Idalia rapidly strengthened from a Category 1 to a Category 4 storm within 24 hours.
Morales expressed that this swift intensification is “one of the greatest concerns I’ve had to keep to myself over the past 15, 20 years as a hurricane forecaster.”
“Eventually, we’ll witness a mundane tropical storm transform into a Category 4 hurricane by the time it makes landfall in Miami 36 hours later,” he warned. “And individuals may not have made the essential preparations.”
In a new paper in the journal Nature Machine Intelligence, leading computer scientists from around the world review recent advances in machine learning that are converging towards creating collective machine-learned intelligence. They propose that this convergence of scientific and technological advances will lead to the emergence of new types of AI systems that are scalable, resilient, and sustainable.
Saltoggio other. In other words, we will see the emergence of collective AI, where many artificial intelligence units, each able to continuously acquire new knowledge and skills, form a network and share information with each other.
Loughborough University Dr. Andrea Sortoggio and colleagues recognize striking similarities between collective AI and many science fiction concepts.
One example they give is Borg – a cybernetic life form that appears in the Star Trek universe that operates and shares knowledge through a linked collective consciousness.
However, unlike many science fiction stories, the authors envision that collective AI will bring major positive breakthroughs across a variety of fields.
“Instantaneous knowledge sharing across a collective network of AI units that can continuously learn and adapt to new data enables rapid response to new situations, challenges, and threats,” said Dr. Sortogeo.
“For example, in a cybersecurity environment, when one AI unit identifies a threat, it can quickly share knowledge and prompt a collective response, which helps the human immune system protect the body from external intruders. It’s the same as protecting it.”
“It could also lead to the development of disaster response robots that can quickly adapt to the situation they are dispatched to, and personalized medical agents that combine cutting-edge medical knowledge with patient-specific information to improve health outcomes. Yes, the potential applications are vast and exciting.”
Researchers acknowledge that there are risks associated with collective AI (such as the rapid spread of potentially unethical or illegal knowledge), but that AI units have their own objectives and independence from the collective. The authors emphasize the important safety aspect of their vision: to maintain
“This will enable democracy for AI agents and greatly reduce the risk of AI domination by a few large systems,” said Dr. Sortoggio.
After analyzing recent advances in machine learning, the authors concluded that the future of AI lies in collective intelligence.
The study focuses global efforts on enabling lifelong learning (where AI agents can extend their knowledge throughout their operational life) and developing universal protocols and languages that allow AI systems to share knowledge with each other. It became clear that it was.
This differs from current large-scale AI models such as ChatGPT, which have limited lifelong learning and knowledge sharing capabilities.
Such models are unable to continue learning because they acquire most of their knowledge during energy-intensive training sessions.
“Recent research trends are extending AI models with the ability to continuously adapt once deployed, allowing their knowledge to be reused in other models, and effectively recycling knowledge to increase learning speed and energy.” It’s about optimizing demand,” said Dr. Sortogeo.
“We believe that the currently dominant large-scale, expensive, non-sharable, non-lifetime AI models will be replaced by sustainable, evolving, and shared collections of AI units in the future. I don’t believe I will survive.”
“Thanks to communication and sharing, human knowledge has increased step by step over thousands of years.”
“We believe that similar movements are likely to occur in future societies of AI units that achieve democratic and cooperative collectives.”
_____
A. Saltoggio other. 2024. Collective AI with lifelong learning and sharing at the edge. nat mach intel 6, 251-264; doi: 10.1038/s42256-024-00800-2
A new study reveals that a spatiotemporal substitution method used to predict species responses to climate change inaccurately predicts the effects of warming on ponderosa pines. This finding suggests that this method may be unreliable in predicting species’ future responses to changes in climate. Credit: SciTechDaily.com
A new study involving researchers at the University of Arizona suggests that changes are happening faster than trees can adapt. The discovery is a “warning to ecologists” studying climate change.
As the world warms and the climate changes, life will migrate, adapt, or become extinct. For decades, scientists have introduced certain methods to predict how things will happen. seed We will survive this era of great change. But new research suggests that method may be misleading or producing false results.
Flaws in prediction methods revealed
Researchers at the University of Arizona and team members from the U.S. Forest Service and Brown University found that this method (commonly referred to as spatiotemporal replacement) shows how a tree called the ponderosa pine, which is widespread in the western United States, grows. I discovered something that I couldn’t predict accurately. We have actually responded to global warming over the past few decades. This also means that other studies that rely on displacement in space and time may not accurately reflect how species will respond to climate change in coming decades.
The research team collected and measured growth rings of ponderosa pine trees from across the western United States, dating back to 1900, to determine how trees actually grow and how models predict how trees will respond to warming. We compared.
A view of ponderosa and Jeffrey pine forests from Verdi Mountain near Truckee, California.Credit: Daniel Perrette
“We found that substituting time for space produces incorrect predictions in terms of whether the response to warming will be positive or negative,” said study co-author Margaret Evans, an associate professor at the University of Arizona. ” he said. Tree ring laboratory. “With this method, ponderosa pines are supposed to benefit from warming, but they actually suffer from warming. This is dangerously misleading.”
Their research results were published on December 18th. Proceedings of the National Academy of Sciences. Daniel Perrette, a U.S. Forest Service ORISE fellow, is the lead author and received training in tree-ring analysis through the university’s summer field methods course at the University of Arizona Research Institute. The study was part of his doctoral dissertation at Brown University, and was conducted with Dov Sachs, professor of biogeography and biodiversity and co-author of the paper.
Inaccuracies in space and time substitutions
This is how space and time permutation works. All species occupy a range of favorable climatic conditions. Scientists believe that individuals growing at the hottest end of their range could serve as an example of what will happen to populations in cooler locations in a warmer future.
The research team found that ponderosa pine trees grow at a faster rate in warmer locations. Therefore, under the spatial and temporal displacement paradigm, this suggests that the situation should improve as the climate warms at the cold end of the distribution.
“But the tree-ring data doesn’t show that,” Evans said.
However, when the researchers used tree rings to assess how individual trees responded to changes in temperature, they found that ponderosa was consistently negatively affected by temperature fluctuations.
“If it’s a warmer-than-average year, they’re going to have smaller-than-average growth rings, so warming is actually bad for them, and that’s true everywhere,” she says.
The researchers believe this may be happening because trees are unable to adapt quickly enough to a rapidly changing climate.
An individual tree and all its growth rings are a record of that particular tree’s genetics exposed to different climatic conditions from one year to the next, Evans said. But how a species responds as a whole is the result of a slow pace of evolutionary adaptation to the average conditions in a particular location that are different from those elsewhere. Similar to evolution, the movement of trees that are better adapted to changing temperatures could save species, but climate change is happening too quickly, Evans said.
Rainfall effects and final thoughts
Beyond temperature, the researchers also looked at how trees responded to rainfall. They confirmed that, even across time and space, more water is better.
“These spatially-based predictions are really dangerous because spatial patterns reflect the end point after a long period in which species have had the opportunity to evolve, disperse, and ultimately sort themselves across the landscape. Because we do,” Evans said. “But that’s not how climate change works. Unfortunately, trees are in a situation where they are changing faster than they can adapt and are actually at risk of extinction. This is a warning to ecologists. .”
References: “Species responses to spatial climate change do not predict responses to climate change,” by Daniel L. Perrett, Margaret EK Evans, and Dov F. Sachs, December 18, 2023. Proceedings of the National Academy of Sciences. DOI: 10.1073/pnas.2304404120
Funding: Brown University Department of Ecology, Evolution, and Organismal Biology, Brown Institute for the Environment and Society, American Philosophical Society Lewis and Clark Expeditionary and Field Research Fund, Department of Agriculture Forest Service Pacific Northwest Research Station, Department of Energy Oak Ridge Science Institute Education , NSF Macrosystems Biology
Poor forecasting of food demand results in more waste than expected.
According to someone sauce, U.S. grocery stores throw away 10% of the approximately 44 billion pounds of food the country produces annually.It’s not just bad for the environment – food waste is a major source carbon emissions — but expensive for grocery stores. around Retail Insights Food and grocery retailers lose up to 8% of revenue due to inventory shortages.
Entrepreneurs Euro One and Jack Solomon say they have experienced first-hand the micro-level impact of prediction problems, as their local supermarkets often run out of their favorite guacamole.
“We found that even the largest retailers are having trouble predicting future demand and are frequently experiencing overstocks and understocks,” Wang told TechCrunch in an email interview. Told. “Recent extreme weather events have exacerbated fresh produce shortages, making it even more important to allocate limited supplies efficiently. Added to this is inflationary pressures and rising labor costs. , grocery store profits are increasingly threatened.”
Wang and Solomon co-founded the company with the idea of using technology to tackle problems. Guac, a platform that uses AI to predict how many items a grocer will sell per item at a given store location each day. Guac recently raised $2.3 million in a seed round led by 1984 Ventures with participation from Y Combinator and Collaborative Fund.
“Food waste and food security are issues that Jack and I care deeply about, and we were very excited about the opportunity to actually solve food waste at the source,” Wang said.
Previously, Wang worked at Boston Consulting Group and Solomon researched AI for grocery logistics. We both graduated from Oxford University, where we met.
At Guac, engineers Wang, Solomon, and Guac have developed a custom algorithm that predicts grocery order quantities by taking into account variables such as weather, sporting events, betting odds, and even Spotify listening data. We are trying to understand consumer purchasing behavior by building a. Guac customers receive recommendations such as expiration dates, minimum order quantities, promotions, and supplier lead times that are integrated into their existing inventory ordering software and workflows.
“Traditionally, forecasting was done using Excel formulas or simple regression models,” Wang says. “But for fresh produce that expires quickly, you need something better. Because we use so many external variables, we can identify the real-world variables that cause changes in demand.”
Guac is certainly not the only startup in the food demand forecasting game. Crisp, which provides an open data platform for each link in the grocery supply chain, and Freshflow, which is building AI-powered predictive tools to help retailers optimize fresh food inventory replenishment.
But Wang says Guac is differentiated by both its commitment to transparency and its thorough tweaking of its predictive models.
“Rather than a black box that magically predicts a 20% increase in demand, our machine learning model tells our customers:“This 20% increase is due to conferences being held nearby,” Wang said. “Even if a retailer is already using machine learning, we can improve our predictions by having access to more external data sets. Including only specific datasets (such as weather or holidays) actually doubles the prediction error.”
Some early customers seem confident that Guac can add value. The company partners with retailers including grocery delivery companies in North America, Europe and the Middle East, including an unnamed supermarket chain with about 300 locations. Guac is also already profitable and expects to expand its engineering team next year.
“The grocery industry is quite resilient to economic downturns,” Wang said. “Everyone has to eat, but when the economy slows down, fewer people eat out and more people actually buy groceries. The pandemic has also accelerated the digitalization of grocery stores, making predictions We can now integrate more seamlessly with our customers’ systems. Speaking of the pandemic, shopper behavior has been very different during the pandemic, as grocers only have access to historical sales data from the past three years. This means that it is very difficult to rely on and predict future demand. Our algorithm allows us to adjust for how the pandemic biased sales data in 2020 and 2021. “We can also adjust for the residual effects of the pandemic afterwards.”
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