“Flock on the Pole” song

Mass surveillance technology might be popular with police departments, but for the US public, it’s becoming an increasingly difficult sell.

At least 54 cities across the US have voted to cancel, non-renew, or reject Flock’s automatic license plate reader (ALPRs) since the start of the year, according to data collected by the Washington Examiner. That comes after reporting in February that some 30 cities had done the same since 2025, indicative of the rising backlash against the tech.

In all, the Examiner reports cities in 23 states have shredded their Flock contracts so far this year, led by California and Wisconsin, which each have seven rejections, cancellations, or deactivations. Other heavy hitters include New York, Washington, and Massachusetts with four each, and Virginia with three.

Though Flock has its cameras in over 5,000 US cities as of July 2026, the rise in rejections at the municipal level comes amidst a tidal wave of outrage around issues of privacy and civil rights.

In cities where civic leaders have been slow to respond to these anxieties, citizens have moved quickly to take matters into their own hands by damaging, obscuring, or stealing the surveillance cameras in order to stop them from recording footage. A rise in official cancelations or rejections shows municipal leaders are catching on to the reality that the American populace broadly disproves of the technology, and they aren’t afraid to show it.

Arguably the highest-profile contract cancellation for Flock came when the Los Angeles Police Department decided to non-renew its years-long contract with the company, a decision motivated by a searing internal audit of the company’s technology. The internal report found that, in just two months, Flock’s ALPRs contributed to 161 false stolen-vehicle alerts, resulting in a false-positive rate of 32.3 percent.

With no sign of the outrage settling anytime soon, it’s likely these 54 cities could just be the start.

Read full story here…

Read full story here…

Recent research from North Carolina State University, published in Parasites & Vectors and Royal Society Open Science, suggests that localized mosquito-control practices may create conditions that favor the evolution of insecticide resistance in mosquito populations.

Aedes albopictus, commonly known as the tiger mosquito, is an invasive species that has become widespread across much of the eastern United States. It breeds in a variety of artificial and natural containers that collect standing water, such as gutters and buckets.

In North Carolina, control of this mosquito is often managed at the household level, either through private mosquito-control services or by individuals applying pyrethroid insecticides. These chemicals are widely used because they are effective against adult mosquitoes and considered among the safer insecticides for use around people, pets, and homes, but they are typically applied without coordination among neighboring properties.

Modeling Mosquito Mutations

In one study, a team led by Jennifer Baltzegar tracked the emergence and spread of a resistance mutation known as F1534S in tiger mosquitoes in Wake County between 2016 and 2024. They looked at the F1534S mutation, which researchers have associated with knockdown resistance.

The mutation was absent from the Wake County samples collected in 2016 and 2017. In 2023, the researchers detected the resistance-associated mutation in 84% of the sampled residential areas, and they found it in 39% of the mosquitoes they sampled. The researchers’ models indicated that the resistance allele was under strong selection pressure, meaning mosquitoes carrying it had a significant evolutionary advantage in the local environment.

“Homeowners with money will pay to spray for mosquitoes, which selects for localized resistance,” said Martha Burford Reiskind, associate professor of biological sciences at NC State and corresponding author on both papers. “Over time, as more and more resistance builds up in the population, it will become harder to control populations. The insecticides will no longer be as effective.”

Wealth as a Selection Pressure

A second study, led by Cole Butler, looked at how resistance is distributed across neighborhoods with different property values. The team sampled mosquito populations across 31 residential blocks throughout Raleigh, covering a range of property values. They found that neighborhoods with higher total property values, which the researchers used as an indicator of likely spending on pest control, showed higher frequencies of resistance. While the study did not track individual spraying habits, the pattern suggests that greater private investment in mosquito control may be contributing to the spread of resistance.

That pattern breaks from how insecticide resistance usually spreads globally. “We see the rapid rise of pesticide resistance in places like South America and Asia where there is continuous spraying in some countries,” Burford Reiskind said. “But resistance driven by private mosquito control is novel in the contiguous U.S., to our knowledge.”

How the Mutation Actually Works

The F1534S mutation occurs in the mosquito’s gene for the voltage-gated sodium channel, which is the same site where pyrethroid insecticides act. This genetic alteration decreases the mosquito’s sensitivity to pyrethroids and thus enables some individuals to survive exposure to insecticides. The resistance allele is only partially recessive, so not all mosquitoes display the full effect. As a result, the mutation can spread through a population before resistance is noticeable.

“This mutation is often the first to show up when there’s insecticide resistance,” Burford Reiskind said.

Where the Research Goes Next

Beyond being a nuisance, tiger mosquitoes can transmit diseases such as dengue, Zika, chikungunya, and dog heartworm. While the current studies did not assess disease transmission directly, Michael Reiskind, a professor at NC State and co-author of both papers, notes that future research will expand to include a broader range of neighborhoods. “We want to make a better effort to get a diversity of neighborhoods, age, and wealth to extend our findings and see whether they work at different housing densities and wealth levels,” Reiskind said.

The researchers recommend using integrated pest management techniques since these methods decrease the need for chemical insecticides. They stressed that eliminating standing water where larvae develop is a key preventive measure. Effective pest control means addressing the entire life cycle and not depending just on chemical insecticides.

Putting these integrated approaches into practice may be challenging since many suburban homeowners already rely on seasonal mosquito-spraying services. The rapid development of resistance in Wake County suggests that managing this trend will likely require cooperation among homeowners, pest control companies, and public health agencies. Achieving that cooperation may require these groups to rethink how they manage private mosquito control.

Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.

Have you ever wondered how researchers use artificial intelligence (AI) in environmental science? Certain AI computer models can learn from scientific data and operate without manual programming, in a process called machine learning. Machine learning models can identify patterns in environmental datasets and help scientists assess public health risks linked to environmental conditions.

One well-known public health risk is the rise in antibiotic-resistant bacteria. These bacteria carry genes called antibiotic resistance genes that protect them from antibiotic medicines. As the number of these genes increases, the medicines become less effective, and diseases spread faster.

Agricultural soils are the largest reservoirs of antibiotic resistance genes, because manure from livestock like cows and pigs often contains leftover antibiotics fed to them. When used as fertilizer, this manure transfers antibiotics into the agricultural soil, where bacteria develop antibiotic resistance genes.

To better understand how antibiotic-resistant bacteria will affect global health in the future, scientists want to develop faster ways to detect antibiotic resistance genes. However, current methods of detection are slow and fail to predict future trends. To address this problem, a group of scientists from Algeria used machine learning models to predict the prevalence of antibiotic resistance genes in soil microbes under future climate scenarios, and to identify the key environmental drivers and high-risk areas.

​The team compiled existing microbial data from 3 public databases: the National Center for Biotechnology Information’s Sequence Read Archive, the Metagenomic Rapid Annotations using Subsystems Technology database, and the Joint Genome Institute’s Integrated Microbial Genomes & Microbiomes database. Their final dataset contained about 2,000 agricultural soil samples from 67 countries across 6 continents. These samples represented a wide range of soil characteristics, climate conditions, and different types of antibiotic resistance genes. 

They integrated the microbial dataset with climate data, including global temperature and precipitation measurements from WorldClim, and land use data, including crop types, irrigation systems, and livestock density from the European Space Agency Climate Change Initiative Land Cover maps. The researchers also included data on future climate projections for 2050 and 2070 from the World Climate Research Programme based on low, medium, and high global greenhouse gas emission scenarios. 

After preparing the datasets, the team set up 6 machine learning models that differ in terms of their data size, model complexity, analytical speed, and customization. These models included Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Deep Neural Network (DNN), and Logistic Regression (LR). 

They ran each model using different combinations of variables from their datasets, including soil properties, microbial community metrics, climate variables, and land use characteristics. To validate the models, the team separated the full dataset into 10 random subsets. They trained the models on 9 of the subsets, then recorded the result of the 10th subset. Each model repeated this process 10 times in a different order so that each subset was evaluated once in the results, using a technique known as stratified 10-fold cross-validation. The researchers scored each model on 5 performance metrics, including precision, sensitivity, and predictive power. Based on these results, they determined that LightGBM was the most accurate model. 

They found that the LightGBM model identified soil temperature as the strongest environmental predictor of antibiotic resistance genes. At soil temperatures above 18°C (about 64°F), the number of antibiotic resistance genes increased dramatically. The model also identified soil acidity, organic carbon content, moisture, and annual precipitation as influential variables, accounting for 71% of the increase in antibiotic resistance genes under future climate scenarios. Likewise, the LightGBM model predicted that the number of high-risk areas would increase by 35% under the high-emissions scenario, with South Asia, Sub-Saharan Africa, and Mediterranean Europe as the most vulnerable regions.

Based on the LightGBM model output, the team concluded that soil conditions will change to favor antibiotic resistance genes under all future climate scenarios. They also suggested that LightGBM is a fast and effective model for environmental predictions that could be used to assess other climate change risks such as natural disasters. They recommended that future researchers explore how to apply machine learning methods to early warning systems and utilize machine learning results in environmental management decisions. 

The post Predicting microbial antibiotic resistance with AI appeared first on Sciworthy.

Opponents of the rapid buildout of data centers held 142 protests across 42 states on Saturday in the first nationwide effort to channel anger at the AI infrastructure expansion that has ramped up over the past year and roiled local politics.

The protests were coordinated by HumansFirst, co-founded by a former leader of the modern-day Tea Party. She compares growing opposition to data centers to the right-wing populist movement that emerged in 2009 to protest what it saw as excessive taxation and government overreach.

Protesters rallied against what HumansFirst calls the “unaccountable” buildout of data centers and “unacceptable infringement on our liberty.”

In Fredericksburg, Virginia, one young man carried a sign reading, “We thrive on water not data.” In Atlanta, a small knot of protesters wielded signs reading, “Georgia never voted for hyperscale AI data centers.”

Nationwide and city-specific counts of protesters were not immediately available, said Chris Barron, president at Right Turn Strategies, who is handling press relations for HumansFirst.

Attendance was lower than expected in some rural areas and major cities such as Atlanta, local organizers told Reuters. But HumansFirst praised the efforts of community activists. “We were thrilled with the turnout,” Barron said.

Data center opposition is among the few issues uniting Americans across ideological lines, with just a third of Americans approving of the pace of data center construction in the U.S., according to a June Reuters/Ipsos poll. Only 14% of respondents would support a data center being built in their community to support artificial intelligence projects for technology firms such as Meta, Alphabet, Amazon, Microsoft and Elon Musk’s xAI.

“The data center industry is continuing to work with policymakers, stakeholders, and residents to ensure data centers strengthen, not strain, the areas where they operate — while mitigating any negative impacts to households and businesses,” said Josh Levi, president of the Data Center Coalition, the industry’s association and lobbying group.

RED AND BLUE STATES REPRESENTED

Although HumansFirst co-founder Amy Kremer compared her current efforts to the Tea Party movement’s early days in 2009, she said the anger against data centers is nonpartisan.

“They just woke up one day and found out they’re going to have this monstrosity in their community, and they don’t want it,” said Kremer, who predicted data centers will be a defining issue in November’s midterm elections and the 2028 presidential race.

Kremer has criticized Republicans for giving Big Tech a “free pass,” but she and some organizers also said they do not support policies like moratoriums on data center approvals like the one adopted by the Democratic state of New York.

Demands made by organizers from the groups involved include transparency in the development process, protection of resources and environmental health, community benefits such as the creation of well-paying union jobs, and a way to hold developers accountable if they break their promises.

The Republican stronghold of Texas, a hotspot for data center development, hosted 18 protest events, the most of any state. The battleground state of Georgia had 11 rallies. Democratic-dominated California had eight, while the swing state of Pennsylvania and the Republican-leaning states of Florida and Indiana came in at seven each.

Saturday’s Atlanta protest, one of a number of similar actions this month, attracted about a dozen participants who traveled from smaller Georgia towns where the largest data centers are being built, said Jake Watts, 26, a volunteer at that event.

Left-leaning Ivan DelSol, 54, helped lead a protest in the California desert’s Imperial County, where a proposed data center project could use 260 million gallons (984 million liters) of water per year from the Colorado River.

“It’s dystopian that you would use this much fresh water for AI,” said DelSol, who later added around 50 people attended despite temperatures that soared above 100 degrees Fahrenheit (38 degrees Celsius).

Although water is frequently cited as a top public concern, especially in water-stressed regions, the data center industry says its water use is not as significant as other industries.

In Tyler, Texas, first-time activist and self-described “political nomad” Eva Cardona, 31, organized a protest that also drew about a dozen people.

“I’ve been hearing about unregulated AI and the rapid growth was alarming me. I wanted to do something more hands-on than just your standard Facebook post,” Cardona said.

Read full story here…

In April, someone threw a Molotov cocktail at Sam Altman’s San Francisco home, and within days a second attack put gunfire into the property. The Wall Street Journal reports that AI executives are hardening personal security as opposition to the industry moves from online posts into the physical world.

Prosecutors say Daniel Moreno-Gama, the 20-year-old accused in the first attack, traveled from Texas to San Francisco intending to kill Altman, and had writings on him about AI’s purported risk to humanity. He faces two counts of attempted murder and attempted arson in California state court. Two more suspects were later arrested in connection with the second incident. Around the same time, The Information described Silicon Valley leaning into a new breed of bodyguards for AI leadership.

The pressure isn’t only on the people at the top. According to the Data Center Watch Q1 2026 report, organized opposition groups roughly doubled from 396 at the end of last year to 833 by the end of March, spanning 49 states, and opponents blocked or delayed at least 75 projects worth about $130 billion in a single quarter. That is a very different problem from a viral tweet. It is permits denied, votes lost, sites relocated.

The honest caveat is that these are two different signals lumped into one story. A handful of documented attacks on high-profile figures is not a wave, and some of the loudest ‘threats are rising’ framing comes from firms that sell protection into that fear. What the reporting doesn’t give you is how widely Moreno-Gama’s ideology is actually shared versus one radicalized individual, or how much of the blocked $130 billion in projects gets rerouted rather than cancelled.

What this changes for anyone building in AI is where the friction now lives. Less in Washington policy, more in the county zoning hearing, the executive comp package, and the calculus of whether to be publicly identified with a frontier lab at all. The winners are the security and threat-intelligence firms scaling into that gap, and the labs that treat community relations as an infrastructure problem before it becomes a personal one.

Read full story here…

A single machine learning model just turned one of condensed matter physics’ most persistent problems into a navigable map.

In a study published in National Science Review, researchers led by Nanjing University used AI to sort millions of magnetoresistance curves into 13 clean categories, then charted exactly how tuning a material’s electronic properties pushes it from one behavior to another.

The work targets the anomalous Hall effect, a decades-old phenomenon that governs how electrons move through certain magnetic materials, and it could hand physicists a shortcut to some of the most sought-after states in modern electronics.

A Problem Too Complex to Map by Hand

The anomalous Hall effect occurs when electrons moving through a magnetic material exhibit behavior beyond the predictions of the conventional Hall effect. Standard Drude theory predicts that resistance should shift as electrons move through a magnetic field. Anomalous Hall systems break that rule, producing resistance curves with double peaks, plateaus, and non-saturating shapes that shift with the material’s band structure and the way its electrons scatter.

The challenge becomes even greater when multiple electron energy bands are active near the Fermi level. Each additional parameter increases the difficulty of interpreting the resulting resistance curves, and until now, constructing a comprehensive map linking electronic properties to resistance behaviors has not been possible.

The Nanjing team set out to create that map. They generated more than 2.27 million magnetoresistance curves from a two-band model spanning five electronic parameters, then set an unsupervised algorithm loose on the dataset with no instructions beyond finding the pattern.

Millions of Curves

Every curve in the dataset collapsed into just 13 distinct types, a verdict so blunt that even the researchers seemed surprised. A trained neural network took over from there, sorting new curves into those same 13 categories with 99% accuracy and turning what had looked like an unruly set of data into something closer to a field guide.

Next, the team created phase diagrams and topological networks to visualize how changing just one parameter could shift a material from one type of resistance curve to another. Instead of a fixed chart, the result is more like an interactive roadmap; adjust a setting, and you can see the transition happen in real time.

To check whether any of this held up outside a computer model, the researchers tested their framework against real measurements from gated Fe5GeTe2 nanoflakes, a magnetic material whose properties can be tuned electrically. The experimental data closely matched the AI-generated phase diagrams, providing the model with a real-world anchor rather than a purely theoretical one.

“This is just the beginning. We believe this framework can be extended to a wide range of intricate models and will help uncover many more complex problems in condensed matter physics,” said Hongtao Yuan, professor at Nanjing University and an author of the study.

A Map That Points to New Physics

The significance of this work extends beyond classification. The phase diagrams indicate which parameter combinations are likely to produce large magnetoresistance, including giant magnetoresistance, a phenomenon already used in hard drive technology. These maps also identify regions where the quantum anomalous Hall effect may occur, a state in which an electric current flows along the edges of a material without energy loss due to resistance.

That kind of lossless conduction has long been seen as a target for low-power electronics, but identifying the narrow parameter windows that produce it has historically relied on trial-and-error. A phase diagram that predicts those windows in advance changes the search from guesswork into targeting.

“Our model provides a framework for comprehensively addressing the complex magnetoresistance behavior in anomalous Hall systems, and serves as a platform for predicting parameter regions that may host intriguing quantum phenomena such as giant magnetoresistance,” said author of the study Ganyu Chen.

The team frames the two-band model as a starting point rather than a finished tool. The same machine-learning approach, they note, should extend to more complex systems beyond ferromagnets, including topological insulators and superconducting junctions, areas of physics that have their own tangled, high-dimensional parameter spaces still waiting for a map. Whether AI-built phase models can keep pace with that added complexity, or whether some of these systems will require entirely new modeling strategies, remains an open question the field is only beginning to test.

Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.

Not that anyone in power is going to care, but there’s even more evidence that Americans are coming to overwhelmingly loathe AI — despite, or perhaps because, they’re using chatbots more than ever.

In a sweeping new poll conducted by Pew Research, only 16 percent of respondents said they believed AI will have a positive impact on society — a number as dismal as the perception of the tech.

Meanwhile, 49 percent of adults say they use AI chatbots like ChatGPT, which remains the most popular by a considerable margin, with a quarter saying they use the tools daily. That proportion is considerably higher than the 33 percent of American adults who said they used AI chatbots in 2024.

In other words, the tech’s widespread adoption isn’t helping its perception. A full 40 percent of respondents said they anticipate AI will have a negative impact on society, and 31 percent said it will impact them personally in a negative way, too.

This varies quite a bit by age. Gen Z adults, ages 18 to 29, were the most wary of AI, with 48 percent believing it’ll be negative for society. Yet they’re also the group that reported using AI the most, at 66 percent.

Interestingly, the 30-49 year olds and the 50-and-up brackets are more closely aligned, at 39 percent and 37 percent respectively viewing it as negative. They’re using AI less, though the dropoff between their usage is significant: 61 percent of 30-49 year olds said they used AI chatbots, while only 42 percent of 50-64 year olds did. It was less than a quarter for 65 years and older.

What’s driving this gap between perception and usage is unclear. You could argue that some feel compelled to use it, even when recognizing the tech’s shortcomings and the ethical dubiousness of the industry that’s building it. In fact, many are literally forced to use it at work, with bosses often more enthusiastic about the tech than workers are.

In any case, it’s a real problem for AI’s long-term staying power. Right now the industry is being propelled by hype and the mountains of cash that’re being pumped into it, while profits remain elusive. If no one likes AI years or decades from now, will there be enough customers to keep the industry running?

Read full story here…

New analysis of ancient frozen Greenland rubbish heaps, called middens, has revealed new insights into several ancient societies that have passed through the country over the last five millennia. The findings include insights into ancient diseases and animals that the inhabitants kept but didn’t eat.

Although the team noted the potential risk that climate change could release dangerous ancient microbes into the modern environment, their analysis found little evidence that the pathogens they identified could pose a threat beyond the immediate area around the middens.

Ancient Middens Offer Historical Insights Beyond Traditional Artifacts

Archaeologists have often excavated items discarded by ancient societies from trash heaps to learn more about the people who lived there. Although such excavations often unearth things like animal bones and mollusk shells, offering insights into diet and hunting habits, these ancient rubbish heaps can also contain human artifacts, such as discarded blades and other tools.

In a country like Greenland, where different peoples have settled the region since 2,500 BCE, the ancient middens may also contain microbes that could offer researchers an entirely different type of historical data. For example, microbial data from middens could reveal which diseases ancient populations had to survive. The team notes that microbes could also help determine what ancient societies kept “but perhaps didn’t eat.”

Still, studies of ancient midden microbes are limited, leaving a gap in historical knowledge. The authors of the new paper also highlight that rapidly increasing Arctic warming could release infectious diseases to which humans have lost immunity over the ensuing generations.

Over One Thousand New Species Were Identified

To bridge the information gap, Dr Frank Møller Aarestrup, a professor at the National Food Institute of Denmark Technical University and corresponding author of the article detailing the team’s research, examined samples collected during 2020 and 2021 expeditions in West and South Greenland. During those expeditions, scientists collected samples from middens locked in time by permafrost, dating back as far as 4,500 years.

Among the middens sampled were the ancient Norse sites of Kapisilit and Narsarsuaq, where researchers collected soil samples from what the new paper’s authors termed “historic winter enclosures.” The site also contained evidence that it was a summer grazing ground for livestock.

After selecting several soil samples, Dr. Aarestrup’s team sequenced the DNA of ancient microbes found in the midden samples. Next, they compared the data, including entire bacterial communities, to 143 soil samples previously collected from areas of permafrost far away from any known historical human settlements.

According to the team’s statement, the sequencing revealed a total of 1,207 species and differing ranges of bacterial species per midden. In denser areas, the team identified as many as 202 species, whereas less microbe-dense middens had as few as nine.

The team’s analysis also found several species that had not been previously identified. This resulted in the team labeling the unidentified microbes using what they termed “broad taxonomic categories” above the genus and species level, such as family and order.

Preserving the ‘Biological Legacy’ of Human Activity

When comparing the microbial communities in the ancient middens with those from samples collected away from human settlements, the team found a significantly richer community of ancient bacteria preserved in the middens. The team notes that this discrepancy confirms that the middens “preserved the biological legacy of human activity.”

The most soil-like bacterial communities were identified within the oldest Paleo-Inuit middens. The researchers said the finding suggests that the microbial “imprint” left behind by humans and animals diminishes over time.

Bacterial communities were also associated with the types of waste discarded in each ancient midden. For example, although most middens contained bacteria associated with human and animal hosts, middens from colonial-era Nuuk contained decomposing seal skins. The middens from colonial-era sites were also rich in the bacterium Clostridium perfringens, which the team notes is “a major cause of food poisoning.”

Risk of Ancient Pathogens is Low

Although the team identified strains of bacteria that can cause botulism or other life-threatening conditions, such as toxic shock syndrome, sepsis, and gangrene, they also found a diversity of antimicrobial-resistance genes in the genomes of bacteria collected from the same middens.

According to the study authors, finding the two sets of genes in ancient and modern soil layers “signaled that microbes resistant to antimicrobials can linger in permafrost for centuries.”

Fortunately, the team’s analysis found that most of the identified microbes, including potentially dangerous pathogens, don’t spread far from middens, even those undergoing defrosting. As a result, the team said that these ancient microbes  “pose little risk to public health,” at least for the time being.

“Here we show that the risk of release of ancient pathogens from ancient middens in Greenland is currently low,” Dr. Aarestrup explained.

Instead, the researcher noted that Arctic middens appear to function like “long-term natural experiments,” in which both human- and animal-associated bacteria, including “opportunistic” species carrying resistance genes, remain detectable centuries later as a broader “legacy of human activity.”

The study “Microbial composition of archaeological middens: Tracing Human Footprints Through Centuries in Greenland’s Ancient Settlements” was published in Frontiers in Microbiology.

Christopher Plain is a Science Fiction and Fantasy novelist and Head Science Writer at The Debrief. Follow and connect with him on X, learn about his books at plainfiction.com, or email him directly at christopher@thedebrief.org.

In the ongoing battle against prostate cancer, one of the most formidable obstacles researchers and clinicians face is the cancer cells’ remarkable ability to develop resistance to treatments. These malignant cells employ sophisticated adaptive mechanisms to survive the onslaught of therapeutic agents, rendering many promising drugs less effective over time. A groundbreaking study led by Dr. Noel Warfel and his team at the MUSC Hollings Cancer Center has uncovered a hitherto unrecognized pathway that explains why certain protein-targeting drugs falter, offering fresh hope for more potent and durable therapies. Published in the latest issue of Cancer Letters, this research not only elucidates a novel survival mechanism in prostate cancer cells but also proposes an innovative therapeutic strategy to circumvent drug resistance.

At the heart of this discovery lies PIM1, a serine/threonine kinase well-known for its role in promoting prostate tumor growth, survival, and resistance to conventional therapies. Despite the development of various PIM1 inhibitors aimed at curbing its kinase activity, clinical success has been elusive, particularly in patients with solid tumors. The study probes the inadequacies of these conventional inhibitors and shifts the focus towards understanding the multifaceted biology of PIM1. Dr. Warfel’s work reveals that simply inhibiting PIM1’s enzymatic function does not fully neutralize its cancer-supporting properties, as the protein wields influence beyond its traditional kinase signaling.

Classically, kinase inhibitors designed to target PIM1 have been intended to block its enzymatic activity—effectively halting the phosphorylation events that drive tumor progression. However, Warfel’s team discovered that these drugs paradoxically cause an accumulation of PIM1 protein within cancer cells. Rather than being degraded, the surplus protein lingers and continues to facilitate cancer cell survival through kinase-independent mechanisms. This phenomenon results in a paradoxical biological double-edged sword: while inhibiting the enzyme’s catalytic function, the drugs inadvertently empower cancer cells with a fresh lifeline to resist death.

Key to this newly uncovered survival mechanism is the interaction between PIM1 and another protein known as HMGB1, a chromatin-binding factor usually confined to the nucleus. HMGB1 has a pivotal role in orchestrating cellular responses to DNA damage, but when PIM1 protein is abundant, these two form a complex that relocates HMGB1 from the nucleus to the cytoplasm. Once in the cytoplasm, HMGB1 ignites autophagy—a cellular recycling process that allows cancer cells to eliminate dysfunctional organelles, particularly damaged mitochondria.

Damaged mitochondria are notorious sources of reactive oxygen species and oxidative stress, conditions that can precipitate cell death. By facilitating the clearance of these harmful mitochondria, the PIM1-HMGB1 axis effectively lowers oxidative stress, bestowing cancer cells with a remarkable resilience against therapies designed to induce lethal damage. This mitophagy-driven defense mechanism enables prostate cancer cells to survive treatment regimens that would otherwise be effective, thus revealing a sophisticated layer of therapeutic evasion.

The implications of these findings are profound. They underscore a fundamental flaw in the current approach to drug design for kinase targets: the assumption that merely inhibiting the catalytic activity of a protein suffices to halt its oncogenic functions. Dr. Warfel emphasizes that the presence of the PIM1 protein itself—irrespective of its enzymatic activity—can sustain drug resistance, signaling a need for therapies that eliminate the protein entirely rather than merely neutralizing its kinase function.

In response to this challenge, the research team previously engineered a novel class of molecules known as proteolysis-targeting chimeras (PROTACs), specifically designed to induce the degradation of the PIM1 protein. Their lead compound, PIMTAC, capitalizes on the cell’s own proteasomal machinery to selectively tag and destroy PIM proteins, rather than simply inhibiting their kinase activity. Laboratory experiments and mouse model studies demonstrate that PIMTAC significantly enhances cancer cell death by increasing oxidative stress and disrupting the HMGB1-mediated survival pathway, outperforming conventional PIM1 inhibitors.

PIMTAC’s capacity to degrade PIM1 addresses both the signaling-dependent and -independent functions of the protein, offering a more comprehensive treatment strategy. By eliminating the kinase-independent survival effects, this approach holds promise for overcoming the persistent issue of drug resistance that hampers the efficacy of current therapies. The data suggest that this novel method could extend beyond prostate cancer to other malignancies where PIM proteins contribute to disease progression, including breast, lung, and various hematologic cancers.

While the development of PIMTAC represents a significant advance, the research remains in its preclinical phase. Challenges such as optimizing systemic delivery of the relatively large PROTAC molecule and improving its tumor-targeting specificity need to be addressed before clinical trials can commence. However, the insights gleaned from these studies reaffirm the importance of in-depth biological exploration of cancer targets, even those that have been the focus of research for many years.

This work also reflects a broader paradigm shift in oncology drug development. Increasing recognition of non-catalytic roles played by kinases and other oncogenic proteins suggests a future where protein degradation technologies might supersede traditional enzyme inhibition. Dr. Warfel envisions a landscape in which cancer therapeutics not only disable protein functions but remove the underlying protein itself, thereby dismantling multiple cancer-supportive mechanisms simultaneously.

Ultimately, this study epitomizes the continuous innovation and relentless inquiry needed to outsmart cancer’s adaptability. By uncovering a concealed survival pathway and offering a way to dismantle it, researchers add a crucial weapon to the anticancer arsenal. For patients battling advanced prostate cancer, particularly those facing the frustrations of treatment resistance, such advances kindle hope for more effective, durable therapies that can translate to improved outcomes and prolonged survival.

The journey from laboratory breakthrough to clinical application involves numerous hurdles, but endeavors like Dr. Warfel’s offer a compelling blueprint for future cancer research. Exploring the nuanced biology of proteins like PIM1 not only deepens scientific understanding but also fuels the creation of revolutionary treatments with the potential to save lives. This study stands as a testament to the power of reexamining established targets with fresh eyes and cutting-edge techniques, underscoring the importance of basic and translational research in reshaping cancer therapy.

As the medical community continues to explore the complexities of tumor biology, the integration of protein-targeting strategies such as PROTACs will likely play an instrumental role in overcoming therapeutic resistance. The PIM1-HMGB1 interaction and its influence on mitophagy highlight how intricate and multifaceted cancer cell survival mechanisms can be. Future investigations will undoubtedly build upon this foundational work, expanding the horizon of possibilities for precise, effective, and personalized cancer treatment modalities.

Subject of Research: Cells

Article Title: Kinase-independent signaling by PIM1 promotes drug resistance by increasing mitophagy and reducing oxidative stress

News Publication Date: 27-May-2026

Web References:

• Cancer Letters Article: https://www.sciencedirect.com/science/article/pii/S0304383526003745
• Previous related work: https://www.mdpi.com/2073-4409/11/6/1006

References: DOI: 10.1016/j.canlet.2026.218611

Image Credits: Medical University of South Carolina, Photo by Clif Rhodes

Keywords: Kinase inhibitors, Prostate cancer, Autophagy

Physicists report the discovery of new evidence for a mysterious quantum state in an unusual class of materials, in findings that could illuminate a long-running debate over how these systems transition into superconductivity.

In physics, symmetry breaking refers to processes in which a symmetric state collapses from its initial state of disorder into a more ordered one, albeit now possessing less symmetry.

The new research findings reveal evidence that time-reversal symmetry, a fundamental kind of symmetry in nature, appears to be broken at temperatures significantly higher than previously recognized.

The research was detailed in a recent study published in Nature Physics.

Mysterious Kagome Metals

In their study, an international team of researchers led by a team at the Korea Advanced Institute of Science and Technology focused on what are known as kagome metals—a variety of ferromagnetic quantum materials possessing unique qualities.

For their study, the researchers used a specific kagome metal called Cesium Vanadium Antimonide (CsV₃Sb₅), a novel material first discovered in 2021 by researchers at UC Santa Barbara’s NSF Quantum Foundry.

The researchers found that during experiments with the material, which is recognized as a candidate superconductor, CsV₃Sb₅ showed indications that time-reversal symmetry was broken at temperatures significantly higher than previously recognized.

This is significant because it is suggestive of an elusive phenomenon physicists call loop-current order, which describes a state in which the circulation of electrons throughout a material occurs in persistent microscopic loops.

A Signal Emerges

Using an advanced photoemission technique, the team was able to uncover subtle differences in the way electrons respond to circularly polarized light, which is light that is produced using an electric field vector that rotates in a circle as the light wave propagates, equaling one complete rotation for each wavelength.

Based on the team’s measurements, a distinctive signal emerged, which they say is consistent with a time-reversal symmetry-breaking state, meaning that their observations provide what may be the best experimental evidence to-date for an exotic quantum state of matter known as loop-current order, wherein electrons spontaneously assume microscopic wave-like formations.

Loop-current order in kagome metals has been an ongoing area of study for physicists, and the team’s new findings offer what may be the best evidence yet uncovered.

Exotic States and High-Temperature Superconductivity

One reason the team’s findings are so significant is that their results offer some clarity for the otherwise complex sequence of phase transitions that are known to occur within CsV₃Sb₅.

Based on their research, the team now believes loop-current order manifests as the temperature begins to decrease, which is then followed by a periodic rearrangement of electrons known as a charge density wave. Finally, superconductivity then follows, accompanied by the absence of electrical resistance altogether.

Fundamentally, the team’s work could offer new insights into the behavior and characteristics of strongly correlated quantum materials, which could hold promise for future studies involving the mechanisms that produce exotic states of matter.

“These insights deepen our understanding of the phase landscape in kagome metals and highlight connections with a correlated system exhibiting analogous transition hierarchy,” the team reports.

In the future, such studies may also help scientists finally attain the long-sought goal of high-temperature superconductivity, whereby materials capable of conducting electricity with zero resistance, and at temperatures above 30 K (-243°C), helping to reduce the current expenses associated with cooling systems required for superconducting, and ultimately helping to bring these high-power engineering technologies into commercial viability.

The team’s recent study, “Evidence of time-reversal symmetry breaking above the charge density wave order in a kagome metal,” was published in Nature Physics.

Micah Hanks is the Editor-in-Chief and Co-Founder of The Debrief. A longtime reporter on science, defense, and technology with a focus on space and astronomy, he can be reached at micah@thedebrief.org. Follow him on X @MicahHanks, and at micahhanks.com.

In a groundbreaking study poised to redefine cancer immunotherapy paradigms, researchers have unveiled a programmable RNA engineering platform that restores immune recognition in notoriously immune-resistant tumors such as prostate cancer. This innovative approach, centered on precise manipulation of mRNA alternative polyadenylation (APA), revives the expression of major histocompatibility complex class I (MHC-I) molecules — the pivotal sentinels of immune surveillance routinely downregulated in these tumors. The findings, detailed in Nature Biomedical Engineering, shed light on an elusive molecular mechanism of immune evasion and herald a new frontier in translational cancer immunology.

Prostate cancer and analogous immune-cold tumors have long bedeviled clinicians by resisting immune checkpoint inhibitor therapies, a frontline strategy in modern oncology. A primary culprit in this resistance is the diminished presentation of tumor-specific antigens on MHC-I molecules, molecules intrinsically responsible for flagging abnormal cells to cytotoxic CD8 T cells. Without adequate MHC-I display, these malignant cells evade immune detection, rendering checkpoint blockade largely ineffective. Despite the clinical significance, no therapies have yet emerged that selectively restore MHC-I expression, underscoring a critical unmet medical need.

The investigative team addressed this gap by engineering the 3′UTR CRISPR/dCas13 system (3′UTRCES), an advanced RNA-editing platform designed to modulate mRNA 3′ untranslated region (3′UTR) dynamics in vivo. Unlike conventional gene editing tools that alter DNA sequences, 3′UTRCES uniquely targets RNA transcripts’ polyadenylation sites to manipulate APA patterns — a post-transcriptional regulatory mechanism influencing mRNA stability, localization, and translation. Leveraging a catalytically inactive Cas13 (dCas13) fused to programmable effectors, this system reprograms 3′UTR architecture to reinstate normal gene expression profiles critical for immune function.

Central to their discovery is the identification of tumor-specific 3′UTR shortening in the E3 ubiquitin ligase adaptor SPSB1, a molecular player hitherto not linked to immune modulation. Through sophisticated analyses, the researchers demonstrated that aberrant SPSB1 3′UTR truncations promote enhanced ubiquitination and subsequent degradation of MHC-I molecules. This selective downregulation disrupts the immune system’s ability to recognize and eliminate malignant cells without affecting programmed death-ligand 1 (PD-L1), an immune checkpoint protein. These insights decode a novel mechanism by which tumors suppress antigen presentation, bypassing immune control.

Armed with this mechanistic understanding, the team deployed lipid nanoparticle (LNP) delivery of the 3′UTRCES constructs directly into syngeneic mouse models harboring prostate tumors. This therapeutic intervention reversed the aberrant SPSB1 3′UTR shortening, effectively restoring MHC-I expression on tumor cells. The restored antigen presentation reinvigorated the infiltration of CD8 cytotoxic T lymphocytes, amplifying antitumor immune responses and sensitizing tumors to subsequent immune checkpoint therapies. This combinatorial strategy outperformed existing modalities, offering a tangible path to overcome immune resistance.

The implications of this study extend beyond prostate cancer, representing a conceptual shift in targeting post-transcriptional gene regulation to modulate tumor immunity. By precisely tailoring RNA isoforms through 3′UTRCES, it becomes feasible to rectify pathological gene expression patterns without permanent genomic alterations, minimizing off-target effects and paving the way for tunable, reversible therapies. This technological stride potentially positions LNP-3′UTRCES as a versatile platform for broad-spectrum cancer immunotherapy.

Delving deeper into the molecular underpinnings, the research highlights how alternative polyadenylation — once considered a subtle nuance of gene regulation — plays an outsized role in immune evasion. APA events that truncate key regulatory 3′UTRs can rewire protein expression profiles, impacting ubiquitin-mediated degradation pathways. In SPSB1’s case, 3′UTR shortening shields MHC-I from stable expression, underscoring the delicate interplay between RNA processing and immune visibility. These revelations chart a new investigative avenue for uncovering APA-driven oncogenic mechanisms.

The study harnessed state-of-the-art transcriptomic and proteomic methodologies to validate the APA signatures and subsequent functional outcomes. Single-cell RNA sequencing and cross-linking immunoprecipitation unveiled the precise RNA-protein interactions modulated by 3′UTR alterations. Meanwhile, in vivo tumor growth assays meticulously quantified the therapeutic efficacy of 3′UTRCES-mediated intervention. The robust experimental design marries molecular biology with immuno-oncology, reinforcing the translational promise of RNA engineering.

Beyond prostate cancer, the researchers posit that similar APA-driven immune escape mechanisms may underpin resistance in other malignancies characterized as immune-cold or immunologically excluded. Tumor heterogeneity presents obstacles, yet the adaptability of the 3′UTRCES platform offers a pathway to customize RNA editing interventions tailored to tumor-specific APA landscapes. This precision medicine approach aligns with emerging oncology paradigms striving to unify genetic and epigenetic therapeutic targeting.

Crucially, this work confronts a longstanding conceptual bottleneck — the absence of clinically viable modalities to restore MHC-I expression selectively. Immune checkpoint inhibitors, though transformative, rely heavily on intact antigen presentation machinery to elicit durable responses. By restoring the tumor’s antigen-presenting capacity, 3′UTRCES synergizes with immune checkpoint blockade, reinvigorating exhausted cytotoxic T cells and promoting tumor clearance. This paradigm underscores the importance of targeting upstream immune suppression pathways in cancer.

The LNP delivery mechanism employed is equally noteworthy, showcasing advances in nanomedicine that enable precise nucleic acid transport to tumor sites. Lipid nanoparticles offer biocompatibility, customizable surface chemistry, and efficient endosomal escape — attributes critical for clinical translation. As the field gravitates toward RNA-based therapeutics post-COVID-19, leveraging these delivery systems in oncology sets a precedent for future innovations combining synthetic biology and immunotherapy.

Looking ahead, the scalability and safety of 3′UTRCES-mediated therapies warrant thorough exploration through preclinical and clinical studies. Immunogenicity of RNA-guided effectors, off-target RNA interactions, and durability of therapeutic responses remain important considerations. Nevertheless, the current findings provide a compelling proof of concept, invigorating translational research efforts to combat immune escape mechanisms effectively.

This pioneering research not only reframes our understanding of tumor immune evasion but also exemplifies how RNA technology can be harnessed to unlock therapeutic opportunities in recalcitrant cancers. By transforming APA from a molecular signature of immune suppression into a therapeutic target, the study opens vistas for next-generation cancer treatments that restore immune competence at the RNA level. This convergence of molecular biology, immunotherapy, and RNA engineering heralds a new dawn in oncology innovation.

In summary, the programmable editing of mRNA 3′UTRs via the 3′UTRCES platform revitalizes MHC-I expression in prostate tumors, counteracting immune suppression driven by SPSB1-mediated degradation pathways. This novel mechanistic insight, coupled with effective lipid nanoparticle delivery and demonstrated synergy with immune checkpoint inhibitors, marks a transformative advance in overcoming tumor immune resistance. The study exemplifies how precision RNA engineering can recalibrate tumor immunogenicity and redefine therapeutic potentials in cancer treatment.

As research continues to unravel the complexities of RNA-mediated regulation of immune visibility in cancer, the integration of programmable RNA tools like 3′UTRCES with established immunotherapies holds immense promise. These synergistic strategies may soon accelerate the development of personalized cancer therapies designed to circumvent immune escape and foster more durable clinical remissions. The future of oncology may indeed lie at the interface of RNA engineering and immunomodulation.

This landmark work not only elucidates a previously unrecognized molecular mechanism of immune evasion but also delivers a versatile, programmable therapeutic platform for precise post-transcriptional gene regulation in cancer. Its translational implications inspire optimism for expanding the repertoire of RNA-based interventions that can be tailored to diverse tumor contexts, ultimately enhancing patient outcomes and transforming cancer immunotherapy at its molecular roots.

Subject of Research: Programmable RNA engineering to restore MHC-I expression and overcome immune evasion in prostate cancer.

Article Title: Programmable mRNA 3′UTR engineering restores MHC-I and overcomes immune evasion in prostate cancer.

Article References:
Huang, F., Yuan, F., Li, K. et al. Programmable mRNA 3′UTR engineering restores MHC-I and overcomes immune evasion in prostate cancer. Nat. Biomed. Eng (2026). https://doi.org/10.1038/s41551-026-01720-9

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41551-026-01720-9

In the ongoing battle against prostate cancer, researchers have uncovered a biochemical pathway that promises to revolutionize treatment strategies. A recent study by Zhang, H., Feng, T., Lv, M., and colleagues, published in Cell Death Discovery, reveals how quinolinic acid, a metabolite derived from the enzyme HAAO, significantly influences androgen receptor (AR) signaling via an FDPS-dependent mechanism. This discovery not only deepens our understanding of prostate cancer biology but also highlights a novel avenue for enhancing the efficacy of combination therapies.

Prostate cancer remains one of the most prevalent malignancies affecting men worldwide, with androgen receptor signaling playing a pivotal role in disease progression and therapy resistance. Androgen deprivation therapies (ADT) have long been the frontline treatment; however, many patients eventually develop castration-resistant prostate cancer (CRPC), underscoring the urgent need for new molecular targets and therapeutic strategies. The study conducted by Zhang and colleagues brings to light the critical interplay between metabolites in the kynurenine pathway and AR signaling, potentially offering new hope for overcoming therapeutic resistance.

Central to their findings is quinolinic acid (QA), a metabolite produced downstream of 3-hydroxyanthranilic acid oxygenase (HAAO) activity within the kynurenine pathway of tryptophan metabolism. Quinolinic acid has traditionally been studied for its neurotoxic properties, but this research innovatively identifies QA as a key modulator within prostate cancer cells. The team demonstrated that HAAO expression is markedly elevated in prostate cancer tissues, correlating with advanced disease stages and poorer prognosis.

Mechanistically, quinolinic acid appears to fuel FDPS (farnesyl diphosphate synthase)-dependent androgen receptor signaling. FDPS is a crucial enzyme in the mevalonate pathway, responsible for the synthesis of isoprenoids, which are vital for protein prenylation and cellular proliferation. The researchers showed that elevated QA enhances FDPS activity, leading to increased AR signaling activity. This hyperactivation of AR signaling amplifies cancer cell growth and survival, facilitating resistance to standard therapies.

What makes this discovery particularly compelling is the demonstration that modulating levels of quinolinic acid can sensitize prostate cancer cells to combination therapeutic approaches. The study revealed that inhibiting HAAO or reducing quinolinic acid production diminishes FDPS-dependent AR signaling, thereby suppressing tumor cell proliferation and improving the effectiveness of anti-androgen drugs. These findings suggest a promising therapeutic window where targeting metabolic pathways can synergize with hormonal therapies to overcome resistance.

The implications of these results extend beyond a simple metabolic snapshot. They connect two major biological systems—the kynurenine pathway and the mevalonate pathway—each with independent significance in cancer biology but now linked through quinolinic acid’s regulatory effects. This metabolic crosstalk introduces a paradigm shift in how metabolic intermediates can be exploited to influence oncogenic signaling cascades.

Moreover, the study utilized a combination of advanced molecular techniques including transcriptomics, metabolomics, and functional assays in both in vitro and in vivo models. These comprehensive approaches validated the critical role of HAAO-derived quinolinic acid and its impact on FDPS expression and activity. Importantly, patient-derived xenografts and clinical data underscored the translational relevance of the findings, suggesting readiness for clinical exploration.

Therapeutically, the prospect of combining HAAO inhibitors or agents targeting quinolinic acid synthesis with established anti-androgen therapies such as enzalutamide marks a significant advancement. This combination strategy could potentially prevent or delay the onset of treatment resistance, a major hurdle in managing advanced prostate cancer. Additionally, the study hints at the broader possibility of targeting metabolic vulnerabilities in other androgen-driven cancers.

From a mechanistic perspective, the identification of quinolinic acid as an oncometabolite challenges previous notions relegating it to neurotoxicity roles. It invites further exploration of how metabolic byproducts can act as signaling molecules within the tumor microenvironment, influencing cancer cell behavior and treatment outcomes. This insight opens doors for novel biomarkers that can predict response to therapy and guide personalized medicine approaches.

Furthermore, the study paves the way to investigate whether similar metabolic dependencies exist in other malignancies where steroid hormone receptor signaling is critical. If so, the HAAO-FDPS-AR axis may become a universal target across multiple cancer types, broadening the scope of therapeutic interventions beyond prostate cancer alone.

On the molecular signaling axis, the team discovered that quinolinic acid’s impact on FDPS promotes AR nuclear translocation and transcriptional activity, leading to enhanced expression of AR target genes that drive proliferation and survival pathways. This detailed mechanistic insight provides concrete targets for pharmacological intervention and biomarker development.

This groundbreaking study also raises important questions about the metabolic plasticity of cancer cells and their ability to adapt biosynthetic pathways to sustain oncogenic signaling. Understanding how cancer cells leverage metabolites like quinolinic acid could illuminate resistance mechanisms and identify vulnerabilities exploitable by combination therapies.

In conclusion, the elucidation of HAAO-derived quinolinic acid as a central actor fueling FDPS-dependent androgen receptor signaling heralds a novel metabolic axis driving prostate cancer progression and therapy resistance. By bridging metabolic biochemistry and signal transduction, the research sets the stage for innovative treatment paradigms that combine metabolic inhibition with hormonal therapy, potentially transforming patient outcomes in prostate cancer and beyond.

Zhang and colleagues’ work exemplifies the power of integrative omics and translational research in unraveling complex cancer biology, inviting the scientific community to rethink how metabolic intermediates can be harnessed in the fight against malignancy. As research advances, the HAAO-quinolinic acid-FDPS-AR signaling axis may well become a cornerstone in precision oncology strategies targeting prostate cancer metabolism and hormone receptor signaling.

Subject of Research: Prostate cancer metabolism and androgen receptor signaling modulation by HAAO-derived quinolinic acid.

Article Title: HAAO-derived quinolinic acid fuels FDPS-dependent AR signaling and sensitizes prostate cancer to combination therapy.

Article References:
Zhang, H., Feng, T., Lv, M. et al. HAAO‑derived quinolinic acid fuels FDPS‑dependent AR signaling and sensitizes prostate cancer to combination therapy. Cell Death Discov. (2026). https://doi.org/10.1038/s41420-026-03203-x

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41420-026-03203-x