Category: 3. Business

Multimodal artificial intelligence (AI) models using a combination of molecular, imaging, and clinical features improved the individual prognostic assessment of patients with early breast cancer’s risk of distant recurrence, according to an analysis presented at the 2025 San Antonio Breast Cancer Symposium (Abstract GS1-08). 

The team assessed various models and found that the addition of molecular features significantly strengthened the prognostic accuracy for early distant recurrence, while histopathologic data drove improvements in accuracy for late distant recurrence. 

 

“This study shows the potential for how AI can be leveraged to develop better diagnostic tests that may more accurately estimate recurrence risk and individualize treatment decisions,” stated Joseph A. Sparano, MD, Chief of the Division of Hematology and Oncology at the Mount Sinai Tisch Cancer Center. 

Background and Study Methods 

The TAILORx trial established the use of Oncotype Dx 21-gene recurrence scores for guiding treatment with endocrine therapy with or without chemotherapy for patients with T1-2, N0 hormone receptor–positive/HER2-negative early breast cancer. Oncotype Dx is considered a prognostic indicator for 10-year distant recurrence, but has limited value for late distant recurrence for more than 5 years. 

Researchers developed several single and multimodal AI models to improve distant recurrence risk prognostication. The models combined clinical, molecular, and histopathological features to determine prognosis for early (<5 years), late (>5 years), and overall distant recurrence (15 years) with the use of primary tumor samples and clinical data from patients in the TAILORx trial who volunteered for the analysis. 

“Our goal was to develop a new diagnostic test that provides better prognostic estimation of recurrence risk, including late recurrence risk, by studying tumor specimens from the TAILORx trial,” Dr. Sparano said. “We developed an AI model that evaluates both the images of digitized slides used for routine pathologic assessment, plus the molecular and clinical characteristics of breast cancer to provide better prognostic information about cancer recurrence risk out to 15 years, including early recurrence within 5 years after diagnosis, and late recurrence after 5 years.” 

The researchers digitized slides from 4,462 primary tumor samples and nucleic acids were extracted and sequenced using Caris MI Tumor Seek–Hybrid. They used 63% of the samples for model training and 37% were saved for an independent validation set. 

Single-modality models included separate clinical, image, and molecular features; there was also an expanded molecular model, dual modality for combinations of the three modalities, and a multimodal model for all three combined. The expanded molecular model included 42 genes from the Oncotype DX, MammaPrint, Prosigna, EndoPredict, and Breast Cancer Index commercial gene signatures plus 57 high-variance genes. 

Continuous risk scores were separated into high-risk vs low-risk groups than aligned with the Oncotype DX recurrence score cutoff for distinguishing between high and low genomic risk. 

Key Findings 

Continuous Oncotype DX recurrence scores achieved a concordance index (C-index) of 0.617 for overall distant recurrence and 0.738 for early distant recurrence, but did not provide prognostic value for late distant recurrence (C-index = 0.518). When Oncotype DX was combined with clinical features, the C-index scores were similar for overall (0.600), early (0.706), and late (0.512) distant recurrence. 

The multimodal AI model combining clinical, molecular, and histopathological features performed best of all tested models in terms of prognostic performance for overall distant recurrence (C-index = 0.705; hazard ratio [HR] for high vs low risk = 3.6; P < .001) and late distant recurrence (C-index = 0.656; HR = 2.84; P < .001). For early distant recurrence, it performed second best after the dual-modality model of clinical and expanded molecular features (C-index = 0.776; HR = 5.6; P < .001). 

The expanded molecular model performed best of all single-modality models for early distant recurrence prognostication (C-index = 0.757), while imaging features were stronger for late distant recurrence prognostication (C-index = 0.637; HR = 1.9; P = .001). 

In the validation set, the multimodal AI model again outperformed Oncotype DX for overall distant recurrence through 15 years (C-index = 0.733 vs 0.631; P = .00049) and late distant recurrence after 5 years (C-index = 0.705 vs 0.527; P = .000031). 

“AI-based pathomic tools that rely on evaluation of tissue sample slides routinely generated from clinical practice can be captured with scanners or even widely available smartphones, uploaded electronically, and analyzed centrally—with minimal cost,” Dr. Sparano said. 

Disclosure: This research was a public-private partnership between the federally funded ECOG-ACRIN Cancer Research Group and Caris Life Sciences, supported by the Breast Cancer Research Foundation, the National Cancer Institute of the National Institutes of Health, and the U.S. Postal Service Breast Cancer Research Stamp Fund. Dr. Sparano serves as a consultant for AstraZeneca, Delphi Diagnostics, Genentech, Genomic Health/Exact Sciences, Novartis, and Pfizer; is a member of the scientific advisory board for PreciseDX; and receives institutional research support from Olema Oncology. For full disclosures of the other study authors, visit abstractsonline.com.  

 

FDA proposes the first new sunscreen molecule in decades: bemotrizinol

A proposal released Dec. 11 by the US Food and Drug Administration would bring a new sunscreen molecule, an ultraviolet (UV) filter, to the country for the first time since 1996.

Specifically, the agency would add bemotrizinol (BEMT) to a list of molecules that it considers generally recognized as safe and effective, a move often referred to as a GRASE designation.

BEMT has been available in the European Union and most other parts of the world since 2000, and its FDA approval would be granted partially by considering that long history of safe use. The FDA has had the authority to use such data since 2002 but has resisted repeated urging to do so from public health advocates, consumer product makers, ingredient suppliers, and Congress.

Carl D’Ruiz, a regulatory affairs manager at the ingredient maker DSM-Firmenich who has worked on BEMT for 23 years, calls the development “a sure win for American public health and skin cancer prevention.”

BEMT is a particularly good UV filter, proponents say, because it blocks both major types of UV radiation and its high molecular weight prevents it from absorbing through the skin into the bloodstream. Though only this ingredient is on the table for now, policy experts think that approval of BEMT could smooth the way for more molecules already used in other countries to be approved in the US in the near future.

The agency is accepting public comment on the proposal, which could bring BEMT sunscreens to US consumers as early as the fall of 2026.

—Craig Bettenhausen

US House passes defense bill barring work with biotech companies ‘of concern’

On Dec. 10, the US House of Representatives narrowly passed a reconciled version of the annual defense policy bill, which would authorize roughly $900 billion in national security spending along with various other measures. The final draft of the National Defense Authorization Act (NDAA) includes a Senate-proposed amendment (PDF) that would bar federal agencies from procuring equipment or services from “biotechnology companies of concern.”

The amendment, known as the Biosecure Act, doesn’t name specific companies but states that these companies are ones affiliated with “a foreign adversary’s military, internal security forces, or intelligence agencies,” particularly China’s. According to Science, some science policy experts believe that the measure could harm academic collaborations with China, as well as negatively affect US pharmaceutical supply chains.

Notably, one measure that didn’t make it into the final bill, which reconciles the House and Senate versions of the NDAA, was the House-proposed SAFE Research Act, which many in the academic and scientific communities have spoken out against in recent months (PDF). Like the Biosecure Act—but far more expansive—the SAFE Research Act would have prohibited federal agencies from funding any researcher who is either affiliated with or collaborating with anyone who is affiliated with what was broadly defined as a “hostile foreign entity.”

Another provision that is included the final fiscal year (FY) 2026 NDAA would prevent the US Department of Defense from altering indirect cost rates for the research grants it provides prior to consulting “the extramural research community,” which comprises universities, independent research institutes, and private foundations.

Back in June, the DOD announced the implementation of a 15% indirect cost cap, similar to caps other federal agencies had announced, which was subsequently struck down by a federal judge in October.

The bill is now headed to the Senate, which is expected to vote on the FY 2026 NDAA by the end of December. If the Senate also passes the bill, it will then go to the president to be signed into law.

—Krystal Vasquez

50 US chemical plants are exempt from a regulation for toxic air pollution, report says

Some 50 US chemical manufacturing plants have a 2-year exemption from certain requirements under the Clean Air Act, says a report published Tuesday from the Union of Concerned Scientists (UCS). The plants are among 188 industrial facilities that have been granted such exemptions by the White House.

“For people across the country, these exemptions translate directly into higher toxic air pollution exposure and cancer risks,” says Darya Minovi, a senior analyst at the UCS and a coauthor of the report. The UCS says almost 4.6 million people live within 3.2 km (2 miles) of at least one of 546 facilities that are eligible for an exemption.

President Donald J. Trumped in March offered a 2-year exemption from nine hazardous air pollution regulations to 546 industrial facilities. Trump invoked part of the Clean Air Act that allows such exemptions if the president determines that technology to implement a toxic air pollution limit isn’t available and if issuing an exemption is in the national security interests of the US.

Of the 50 chemical plants exempted from a 2024 regulation for controlling pollutants that are known or suspected to cause cancer in humans, 18 are in Louisiana and 17 are in Texas, the report says. The 2024 regulation, strongly opposed by the chemical industry, was expected to slash ethylene oxide and chloroprene emissions from chemical manufacturing facilities by up to 80%.

Another 167 chemical manufacturing plants are also eligible for the exemption, the report says.

Facilities in other industries have also sought and have been granted exemptions from hazardous-air-pollutant regulations. Seventy coal-fired power plants are exempt from a regulation to limit their mercury emissions, according to the report. In addition, 39 commercial sterilizers are operating exempt from a regulation that would control their release of ethylene oxide.

Though an email address at the US Environmental Protection Agency receives the applications for exemptions, they are forwarded to the White House, which makes determinations, an agency spokesperson tells C&EN.

Meanwhile, the EPA is reconsidering whether it will change or withdraw regulations for the nine toxic air pollutants.

—Cheryl Hogue, special to C&EN

EU plans to replace fossil fuels with biological matter

The European Commission last month announced a new bioeconomy strategy that aims to increasingly use organic matter that can grow back to replace fossil fuels in producing materials such as plastics and other chemicals.

The EU’s bioeconomy, estimated in 2023 at €2.7 trillion (about $3.2 trillion), already employs over 17 million people, accounting for 8% of jobs in the region. The plan aims to create even more jobs, as well as reduce resource dependence on individual countries or regions, while supporting activities that “provide sustainable practical solutions and alternatives to critical raw materials.”

The EU policy, launched on Nov. 27, would involve implementing measures to boost biobased innovations in sectors such as agriculture, forestry, aquaculture, and biotechnology through soliciting public and private investments, streamlining regulations and approvals, and creating a “bioeconomy investment deployment group” to scale up private financing.

The move could accelerate shifts in the packaging industry from plastics—amid the current impasse on treaty talks to end plastic pollution—to biobased materials. Other sectors might also be encouraged to replace fossil-fuel-derived chemicals: for example, pharmaceuticals and personal care items could use biobased chemicals such as algae, while microorganisms may be used in the production of fertilizers.

But environmental groups worry that the strategy doesn’t go far enough with regard to protecting ecosystems, particularly when crops are used to produce biofuels such as ethanol to substitute for fossil fuels. Many groups judge such practices as unsustainable and a threat to food security.

The European Environmental Bureau, the region’s largest network of environmental civil society organizations, warns in a statement that “while focusing on scattered product innovation efforts instead of tackling the root causes of nature, pollution, and climate crises, the [European] Commission has missed a crucial opportunity.”

The European Biogas Association, meanwhile, has welcomed the strategy. “Recognising biogases and their co-products in the EU Bioeconomy Strategy highlights one of the most practical and immediate ways to deliver a circular, low-carbon, and competitive bioeconomy,” Harmen Dekker, the association’s CEO, says in a statement.

—Paula Dupraz-Dobias, special to C&EN

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Jamie Dimon has signalled support for former Federal Reserve governor Kevin Warsh to be the US central bank chair, while also telling Wall Street executives that frontrunner Kevin Hassett is likelier to support Donald Trump’s call for rate cuts.

The JPMorgan chief executive told the bank’s private conference for asset management CEOs in New York on Thursday evening that he agreed with Warsh’s writings on the Fed, people familiar with the matter said.

Dimon, one of the most influential voices on Wall Street, also said that Hassett was more likely to cut interest rates in the short term were he to be Fed chair, the people said.

Trump, Treasury secretary Scott Bessent and other top officials interviewed Warsh at the White House on Wednesday, the Financial Times reported earlier this week. The president is expected to interview at least one other candidate next week. He has said he could announce his decision in the coming weeks. 

Trump has relentlessly criticised current Fed chair Jay Powell for not bowing to his demand to drastically lower US borrowing costs to fuel faster economic growth and lower the US government’s debt repayments, labelling the Fed chair a “numbskull” and a “moron”.

Dimon warned earlier this year that “the independence of the Fed is absolutely critical” and that playing around with the Fed “can often have adverse consequences”. JPMorgan declined to comment on Dimon’s remarks on Thursday.

The Fed cut rates to a three-year low on Wednesday — the third quarter-point move in a row. But the bar for more action is now high, with Powell saying the Fed was “well positioned to wait to see how the economy evolves”. In a fractious meeting, several regional central bank presidents called for borrowing costs to remain on hold.

Trump has vowed to avoid making the same mistake as he did with Powell, saying loyalty and a willingness to lower rates aggressively are the key criteria for getting the job.

Hassett has sought to play down concerns that he would ignore other rate-setters’ fears over inflation remaining too high, saying earlier this week that the Fed’s independence to set rates free from pressure from the White House was important.

However, the White House economist also supported Trump’s calls for a rate cut that was “at least double” what the majority of US rate-setters were willing to support.

A Washington insider who has served in both Trump administrations, Hassett’s closeness to the president has dogged his candidacy. Many academic economists and central bank insiders would prefer current Fed governor Christopher Waller for the role, who is also popular on Wall Street.

While many are wary of Hassett’s closeness to the president, Warsh is also an unpopular figure at the central bank due to his frequent criticisms of the Fed since leaving the institution.

The Hoover Institution economist is also viewed by some on Wall Street as too hawkish to win Trump’s backing, with transcripts of Federal Open Market Committee meetings from 2008 showing that Warsh was reiterating his concerns about inflation just days before the collapse of US investment bank Lehman Brothers. 

Dimon’s comments come after the FT reported last week that Wall Street bond investors had expressed concerns about Hassett to Treasury officials, who had solicited feedback about the Fed chair candidates from market participants directly.

Several market participants in the $30tn Treasury market said they were worried about Hassett’s alignment with Trump.

Hassett, who also worked at the Fed during the 1990s and holds a doctorate in economics, has long been considered the leading contender for the role of next chair.

Market reaction to a potential Hassett nomination has so far been fairly muted, although two investors pointed to the jump in an important market measure of inflation — which estimates the average rate of inflation over five years in five years’ time.

The five-year, five-year forward has climbed by 0.06 percentage points, reaching its highest level in a month, since late November.    

A hundred people joined a demonstration today in downtown Montreal. Gathering in front of the offices of Axium Infrastructure, the main shareholder of the Montréal Gateway Terminals Partnership (MGTP), demonstrators expressed solidarity with 32 MGTP agents that have been on strike since September 22, 2025. 

“We had to bring the pressure to where the decision-makers were,” said CUPE representative Loïc Blanchard. “That’s why we’re here today. It’s time to come to an agreement, and our members want a fair settlement. The company is very profitable because of our members. The workers deserve better!”

The last collective agreement for members expired on December 31, 2024. On September 11, members voted 96% in favour of a strike action.

Discussions as of recent have come to a stalemate over contracting out and wages. Further, the employer has not agreed on the minimum operating requirements to keep the terminals running smoothly. The latest offer was rejected by a unanimous vote.

The employer and the union are scheduled to take part in mediation on December 16 and 17.

As language models (LMs) improve at tasks like image generation, trivia questions, and simple math, you might think that human-like reasoning is around the corner. In reality, they still trail us by a wide margin on complex tasks. Try playing Sudoku with one, for instance, where you fill in numbers one through nine in such a way that each appears only once across the columns, rows, and sections of a nine-by-nine grid. Your AI opponent will either fail to fill in boxes on its own or do so inefficiently, although it can verify if you’ve filled yours out correctly.

Whether an LM is trying to solve advanced puzzles, design molecules, or write math proofs, the system struggles to answer open-ended requests that have strict rules to follow. The model is better at telling users how to approach these challenges than attempting them itself. Moreover, hands-on problem-solving requires LMs to consider a wide range of options while following constraints. Small LMs can’t do this reliably on their own; large language models (LLMs) sometimes can, particularly if they’re optimized for reasoning tasks, but they take a while to respond, and they use a lot of computing power.

This predicament led researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) to develop a collaborative approach where an LLM does the planning, then divvies up the legwork of that strategy among smaller ones. Their method helps small LMs provide more accurate responses than leading LLMs like OpenAI’s GPT-4o, and approach the precision of top reasoning systems such as o1, while being more efficient than both. Their framework, called “Distributional Constraints by Inference Programming with Language Models” (or “DisCIPL”), has a large model steer smaller “follower” models toward precise responses when writing things like text blurbs, grocery lists with budgets, and travel itineraries.

The inner workings of DisCIPL are much like contracting a company for a particular job. You provide a “boss” model with a request, and it carefully considers how to go about doing that project. Then, the LLM relays these instructions and guidelines in a clear way to smaller models. It corrects follower LMs’ outputs where needed — for example, replacing one model’s phrasing that doesn’t fit in a poem with a better option from another.

The LLM communicates with its followers using a language they all understand — that is, a programming language for controlling LMs called “LLaMPPL.” Developed by MIT’s Probabilistic Computing Project in 2023, this program allows users to encode specific rules that steer a model toward a desired result. For example, LLaMPPL can be used to produce error-free code by incorporating the rules of a particular language within its instructions. Directions like “write eight lines of poetry where each line has exactly eight words” are encoded in LLaMPPL, queuing smaller models to contribute to different parts of the answer.

MIT PhD student Gabriel Grand, who is the lead author on a paper presenting this work, says that DisCIPL allows LMs to guide each other toward the best responses, which improves their overall efficiency. “We’re working toward improving LMs’ inference efficiency, particularly on the many modern applications of these models that involve generating outputs subject to constraints,” adds Grand, who is also a CSAIL researcher. “Language models are consuming more energy as people use them more, which means we need models that can provide accurate answers while using minimal computing power.”

“It’s really exciting to see new alternatives to standard language model inference,” says University of California at Berkeley Assistant Professor Alane Suhr, who wasn’t involved in the research. “This work invites new approaches to language modeling and LLMs that significantly reduce inference latency via parallelization, require significantly fewer parameters than current LLMs, and even improve task performance over standard serialized inference. The work also presents opportunities to explore transparency, interpretability, and controllability of model outputs, which is still a huge open problem in the deployment of these technologies.”

An underdog story

You may think that larger-scale LMs are “better” at complex prompts than smaller ones when it comes to accuracy and efficiency. DisCIPL suggests a surprising counterpoint for these tasks: If you can combine the strengths of smaller models instead, you may just see an efficiency bump with similar results.

The researchers note that, in theory, you can plug in dozens of LMs to work together in the DisCIPL framework, regardless of size. In writing and reasoning experiments, they went with GPT-4o as their “planner LM,” which is one of the models that helps ChatGPT generate responses. It brainstormed a plan for several “Llama-3.2-1B” models (smaller systems developed by Meta), in which those LMs filled in each word (or token) of the response.

This collective approach competed against three comparable ones: a follower-only baseline powered by Llama-3.2-1B, GPT-4o working on its own, and the industry-leading o1 reasoning system that helps ChatGPT figure out more complex questions, such as coding requests and math problems.

DisCIPL first presented an ability to write sentences and paragraphs that follow explicit rules. The models were given very specific prompts — for example, writing a sentence that has exactly 18 words, where the fourth word must be “Glasgow,” the eighth should be “in”, and the 11th must be “and.” The system was remarkably adept at handling this request, crafting coherent outputs while achieving accuracy and coherence similar to o1.

Faster, cheaper, better

This experiment also revealed that key components of DisCIPL were much cheaper than state-of-the-art systems. For instance, whereas existing reasoning models like OpenAI’s o1 perform reasoning in text, DisCIPL “reasons” by writing Python code, which is more compact. In practice, the researchers found that DisCIPL led to 40.1 percent shorter reasoning and 80.2 percent cost savings over o1.

DisCIPL’s efficiency gains stem partly from using small Llama models as followers, which are 1,000 to 10,000 times cheaper per token than comparable reasoning models. This means that DisCIPL is more “scalable” — the researchers were able to run dozens of Llama models in parallel for a fraction of the cost.

Those weren’t the only surprising findings, according to CSAIL researchers. Their system also performed well against o1 on real-world tasks, such as making ingredient lists, planning out a travel itinerary, and writing grant proposals with word limits. Meanwhile, GPT-4o struggled with these requests, and with writing tests, it often couldn’t place keywords in the correct parts of sentences. The follower-only baseline essentially finished in last place across the board, as it had difficulties with following instructions.

“Over the last several years, we’ve seen some impressive results from approaches that use language models to ‘auto-formalize’ problems in math and robotics by representing them with code,” says senior author Jacob Andreas, who is an MIT electrical engineering and computer science associate professor and CSAIL principal investigator. “What I find most exciting about this paper is the fact that we can now use LMs to auto-formalize text generation itself, enabling the same kinds of efficiency gains and guarantees that we’ve seen in these other domains.” 

In the future, the researchers plan on expanding this framework into a more fully-recursive approach, where you can use the same model as both the leader and followers. Grand adds that DisCIPL could be extended to mathematical reasoning tasks, where answers are harder to verify. They also intend to test the system on its ability to meet users’ fuzzy preferences, as opposed to following hard constraints, which can’t be outlined in code so explicitly. Thinking even bigger, the team hopes to use the largest possible models available, although they note that such experiments are computationally expensive.

Grand and Andreas wrote the paper alongside CSAIL principal investigator and MIT Professor Joshua Tenenbaum, as well as MIT Department of Brain and Cognitive Sciences Principal Research Scientist Vikash Mansinghka and Yale University Assistant Professor Alex Lew SM ’20 PhD ’25. CSAIL researchers presented the work at the Conference on Language Modeling in October and IVADO’s “Deploying Autonomous Agents: Lessons, Risks and Real-World Impact” workshop in November.

Their work was supported, in part, by the MIT Quest for Intelligence, Siegel Family Foundation, the MIT-IBM Watson AI Lab, a Sloan Research Fellowship, Intel, the Air Force Office of Scientific Research, the Defense Advanced Research Projects Agency, the Office of Naval Research, and the National Science Foundation.

UNIVERSITY PARK, Pa. — Stefano Terlizzi, J. Brennan Early Career Professor of Nuclear Engineering at Penn State and a joint appointee at Idaho National Laboratory (INL), was named to the American Nuclear Society’s 2025 40 Under 40 list. This honor recognizes rising leaders whose work is advancing the future of nuclear science and technology. 

The ANS 40 Under 40 program highlights individuals making meaningful contributions across the nuclear sector, from advanced reactor development and fuel cycle innovation to policy, modeling and space applications. Terlizzi’s selection places him among a cohort of early-career professionals whose work is helping shape a new era for nuclear energy at a time of rapid technological and generational change. 

Terlizzi, 35, leads research in multiphysics simulation for microreactors and advanced reactor systems. He developed the first simulation predicting hydrogen redistribution in microreactors and led advanced modeling and simulation tools developed by the U.S. Department of Energy’s (DOE) Nuclear Energy Advanced Modeling and Simulation (NEAMS) program for the Microreactor Applications Research Validation and Evaluation (MARVEL) reactor project. He also founded Penn State’s Computational Reactor Engineering and Analysis (CREA) Lab and contributes to modernizing reactor physics education as part of DOE-supported initiatives. 

“I’m very proud to serve as a faculty member at Penn State while collaborating with INL,” Terlizzi said in an ANS article. “Both institutions have a strong tradition of civilian nuclear applications, and I enjoy working with bright, enthusiastic students and colleagues to advance the field.” 

Terlizzi said his interest in nuclear science began at an early age in his hometown of Turin, Italy. At seven, he watched the science documentary “L’Universo,” featuring Piero Angela, which introduced him to nuclear propulsion and sparked a lifelong fascination with energy and space exploration. 

Reflecting on his career, Terlizzi highlighted the importance of mentorship and collaboration.  

“I’ve been lucky to learn from many outstanding mentors,” Terlizzi said. “Dan Kotlyar, my doctoral advisor at Georgia Tech, emphasized consistency and practical solutions. At INL, Mark DeHart set the standard for excellent mentorship, and professors like Elia Merzari, Jon Schwantes and Arthur Motta have been invaluable in helping me navigate academia.” 

Terlizzi is a world-class reactor physicist, according to Merzari. 

“He has integrated seamlessly into the Penn State nuclear engineering community,” Merzari said. “We are incredibly fortunate to have him among us. It has been wonderful to see him grow so quickly into a key member of our faculty, excelling in teaching, research and service.” 

Terlizzi’s career combines technical innovation with educational leadership. Through the CREA Lab, he fosters research on advanced reactor design while preparing the next generation of reactor physicists and nuclear engineers. His work integrates cutting-edge computational modeling with hands-on training in reactor physics, providing students with unique opportunities to engage with DOE-supported initiatives. 

Looking forward, Terlizzi said his goals include mentoring emerging talent in nuclear engineering and contributing to the realization of advanced reactor designs. His advice for others who wish to have an impact on the field is simple: “Keep doing your work with integrity, and your time will come,” he said. 

One month after seizing on the field, Avery passed away at home surrounded by her family.

Following the soccer match, doctors had given her a high dose of dexamethasone, a strong anti-inflammatory medication that allowed the family to bring their daughter home from the hospital. “It snapped her out of it and brought her back,” says Paul. “It bought us a month and a day to make a lot of memories and give us all a chance to say goodbye.”

The Lafferty family made casts of their hands held together and heartbeat recordings. Avery wrote messages to her loved ones, even as the cancer spread to other parts of her brain and made it difficult to process her thoughts. “We wouldn’t trade that month for anything,” says Paul.

On July 12, 2024, Avery’s family told her that it was okay to rest and that they would see her again one day. Her legacy lives on through Avery’s Little Army, which is striving to spread awareness about pediatric cancer, fund research, and support local families with their own battles. They have raised over $250,000 for organizations including Children’s Brain Tumor Project, The Cure Starts Now, the ChadTough Defeat DIPG Foundation, and Dana-Farber Cancer Institute.

The family hopes that drugs like KL-50 could soon help pediatric patients with glioblastoma who otherwise have few options. “It’s stunning when you hear your child has a diagnosis like this and you realize there’s nothing out there,” says Lisa. “What we love about Dr. Bindra is he always seemed innovative and knew this was a massive gap that needed to be funded.”

Paul says that the passionate doctors and researchers that the family came to know through Avery’s battle give him hope that one day there will be better treatments. “The soccer player is doing everything he can to win the game—that’s how these individuals do their research,” he explains. “It’s beyond just a paycheck for them. The passion they have to try to find a cure for these diseases is impressive.”

Washington, DC: The Executive Board of the International Monetary Fund (IMF) completed the Fifth Reviews under the Extended Fund Facility (EFF) and Extended Credit Facility (ECF) arrangements, and the Second Review under the Resilience and Sustainability Facility (RSF) arrangement for Papua New Guinea. The completion of these reviews allows for the immediate disbursement of SDR 121.07 million (about US$165 million) under the ECF/EFF arrangements and SDR 39.48 million (about US$54 million) under the RSF arrangement, bringing total disbursements under the IMF-supported programs so far to SDR 622.48 million (about US$851 million).

The ECF/EFF arrangements with Papua New Guinea were approved by the Executive Board on March 22, 2023, in an overall amount equivalent to SDR 684.32 million (260 percent of quota) to help address a protracted balance of payments need—manifested in foreign exchange shortages—and to support the authorities’ reforms to address longstanding structural impediments to inclusive growth. The 24-month RSF arrangement, which was approved by the Executive Board on December 11, 2024, in an overall amount of SDR 197.4 million (75 percent of quota), aims to help address risks to prospective balance of payments stability associated with longer-term structural challenges posed by climate change.

Papua New Guinea’s economic outlook remains positive as structural reforms continue to bear fruit. Growth is projected to reach 4.5 percent in 2025, driven by increased production in the resource sector and resilient growth in the non-resource sector supported by improvements in access to foreign exchange and favorable agricultural production. Headline inflation is expected to rebound to 3.8 percent in 2025 from a very low base in 2024 as the drag from the low betel nut prices eases. Over the medium term, growth is expected to moderate and stabilize at just above 3 percent, driven mainly by continued expansion of non-resource sector activities, with inflation converging to around 4.5 percent.

The outlook is subject to high uncertainty, with risks tilted to the downside, as Papua New Guinea remains vulnerable to both domestic and external shocks. These risks are exacerbated by considerable capacity constraints and socio-political fragility that limit the government’s ability to design and implement policies aimed at economic stabilization, development, and climate adaptation. Commodity price volatility, as well as global risks arising from geopolitical conflicts, escalating protectionist trade measures, prolonged uncertainty, and decline in international aid could create additional pressure on growth and inflation. On the upside, the kickoff of major resource projects, which are not yet in the baseline scenario, could boost economic growth in the near and medium run, with significant gains in exports once operations commence.

Program performance has remained satisfactory, with the authorities displaying a sustained commitment to reforms. All but one quantitative performance criteria and all indicative targets for end-June 2025 under the ECF-EFF arrangements were met, whilst all but one indicative targets for end-September 2025 were met. The Executive Board approved a waiver of nonobservance of the quantitative performance criteria on the fiscal deficit of the government for end-June 2025, based on the minor nature of the nonobservance and the corrective actions taken by the authorities. All five structural benchmarks due were met or implemented with delay and one structural benchmark due for the next reviews already met. Two reform measures under the RSF arrangement were implemented.

At the conclusion of the Executive Board’s discussion, Mr. Bo Li, Deputy Managing Director, and Acting Chair, made the following statement:

“The Papua New Guinea (PNG) authorities have accomplished steady progress in implementing their homegrown reform agenda under the Fund-supported programs. Sustained commitment to these reforms will help rebuild policy buffers, address the country’s long-standing structural challenges, and secure a more resilient, inclusive, and greener economic growth.

“The authorities have been successfully reducing the fiscal deficit, with additional consolidation efforts envisaged in the 2026 budget. Steps to durably increase revenue mobilization, consistent with the authorities’ Medium-Term Revenue Strategy, further contain the growth of current spending, and improve expenditure efficiency would help reduce public debt vulnerabilities. At the same time, protecting social and capital spending, strengthening debt management capacity, and modernizing cash management practices are also essential.

“Access to foreign exchange has improved significantly in recent months thanks to central banking reforms, increased flexibility of the Kina, and favorable external conditions. The current crawl-like arrangement remains appropriate to tackle the overvaluation of the Kina and facilitate its return to convertibility. A tighter monetary policy stance, through timely adjustments in the Kina Facility Rate, is needed to ensure consistency between monetary policy and the exchange rate regime. Additional steps to modernize monetary policy operations, strengthen the Bank of PNG’s liquidity management capacity, develop the interbank market, and contain financial stability risks would help to support financial sector development.

“Further efforts to promote good governance and enhance the effectiveness of anti-money laundering and countering financing of terrorism framework are critical to support the business environment. Delivering tangible and long-lasting results in the fight against corruption will be key to reinforcing the credibility of the Independent Commission Against Corruption.

“Building resilience to climate-related risks is crucial for achieving high and inclusive growth. The program supported by the Resilience and Sustainability Facility focuses on strengthening disaster risk management, integrating climate considerations in infrastructure governance, developing climate finance, and setting up incentives for forest protection and fuel efficiency will help attaining these objectives.”

 

Table 1. Papua New Guinea: Selected Economic and Financial Indicators, 2021–2030
Nominal GDP (2024):	US$31.5 billion 1/
Population (2024):	10.1 million
GDP per capita (2024):	US$3,118
Quota:	SDR 263.2 million
				2021			2022	2023	2024	2025	2026	2027	2028	2029	2030
				Actual			Actual	Actual	Est.	Proj.	Proj.	Proj.	Proj.	Proj.	Proj.
				(Percentage change)
Real sector
Real GDP growth				-0.5			5.7	3.8	3.8	4.5	4.0	3.1	3.1	3.1	3.1
Resource 2/				-11.6			5.1	1.3	1.8	4.3	2.5	0.1	0.2	0.2	0.2
Non-resource				4.2			5.9	4.7	4.5	4.6	4.5	4.1	4.1	4.0	4.0
Mining and quarrying (percent of GDP)				8.2			8.2	8.5	9.9	13.6	15.1	15.1	14.8	14.4	14.1
Oil and gas extraction (percent of GDP)				17.1			23.7	18.9	18.3	14.9	13.8	13.3	12.5	11.9	11.2
CPI (annual average)				4.5			5.3	2.3	0.6	3.8	4.2	4.7	4.5	4.5	4.5
CPI (end-period)				5.7			3.4	3.9	0.7	2.8	4.9	4.5	4.5	4.5	4.5
				(In percent of GDP)
Central government operations
Revenue and grants				15.1			16.6	17.9	17.1	18.1	18.6	19.1	19.2	19.4	19.5
Of which: Resource revenue				1.1			3.9	3.9	3.5	4.4	4.4	4.6	4.6	4.6	4.6
Expenditure and net lending				22.0			21.9	22.3	20.4	20.8	19.8	19.0	18.9	19.0	18.9
Net lending(+)/borrowing(-)				-6.8			-5.3	-4.3	-3.2	-2.6	-1.2	0.0	0.2	0.4	0.5
Non-resource net lending(+)/borrowing(-)				-8.0			-9.1	-8.2	-6.7	-7.0	-5.5	-4.5	-4.3	-4.2	-4.0
				(Percentage change)
Money and credit
Domestic credit				15.9			1.5	12.1	1.6	7.3	-0.1	5.8	-0.2	5.4	4.6
Credit to the private sector				2.5			6.9	14.9	3.2	3.9	11.9	9.2	8.5	11.1	6.1
Broad money				13.4			14.7	9.9	-6.4	-5.3	9.1	9.3	-4.5	10.3	14.6
				(In billions of U.S. dollars)
Balance of payments
Exports, f.o.b.				10.8			14.6	12.8	13.4	14.8	15.2	15.9	16.8	17.8	18.7
Imports, c.i.f.				-4.4			-5.9	-5.4	-4.6	-5.9	-6.7	-7.0	-7.4	-7.7	-8.0
Current account (including grants)				3.3			4.6	2.8	5.0	4.4	4.3	4.0	4.0	3.9	3.8
(In percent of GDP)				12.6			14.4	9.1	15.8	13.8	12.9	11.6	11.3	10.8	10.0
Gross official international reserves				3.2			4.0	3.9	3.7	3.0	3.5	3.7	3.2	3.3	3.9
(In months of goods and services imports)					4.5		5.9	6.7	5.6	3.7	4.3	4.3	3.6	3.7	4.2
				(In percent of GDP)
Government debt
Government gross debt				52.6			48.2	53.9	52.1	51.8	49.6	46.8	44.1	41.5	38.7
External debt-to-GDP ratio (in percent) 3/				25.0			23.5	27.0	27.4	29.2	30.0	28.0	27.5	25.9	23.8
External debt-service ratio (percent of exports)						4.3	2.2	2.7	3.4	4.5	5.4	5.6	8.9	3.6	3.3
Memo Items
US$/kina (end-period)				0.2850			0.2840	0.2683	0.2500	…	…	…	…	…	…
NEER (2005=100, fourth quarter)				91.2			100.3	95.3	89.3	…	…	…	…	…	…
REER (2005=100, fourth quarter)				125.3			134.6	129.0	119.5	…	…	…	…	…	…
Terms of trade (2010=100, end-period)				48.3			70.4	63.8	67.4	70.9	71.7	73.6	74.3	74.7	75.4
Nominal GDP (in billions of kina)				91.6			111.4	110.6	121.5	132.7	144.6	153.4	162.6	172.5	183.2
Non-resource nominal GDP (in billions of kina)						68.4	75.9	80.3	87.3	94.9	102.8	109.9	118.2	127.1	136.9
Sources: Papua New Guinea authorities; and IMF staff estimates and projections.
1/ Based on period average exchange rate.
2/ Resource sector includes production of mineral, petroleum, and gas and directly-related activities such as mining and quarrying, but excludes indirectly-related activities such as transportation and construction.
3/ Public external debt includes external debt of the central government, the central bank, and guarantees to other entities.

 

Lauren Kickel, a member of the Vorys intellectual property (IP) group, was chosen to take part in the Federation of Defense & Corporate Counsel (FDCC) Ladder Down Cleveland Class of 2026. 

According to the FDCC, Ladder Down is a “program dedicated to leadership empowerment, business development, and mentoring — each of which is critical for women lawyers to better position themselves for success.”  The yearlong course includes monthly group trainings and a four-month business development “bootcamp.”  Sessions include panel discussions with national corporate clients, local judges and other legal industry leaders.

Kickel’s practice consists of IP litigation with a particular focus in patent infringement, including software and mobile applications, medical products, personal products as well as apparatuses, systems and processes relevant to the energy industry.

About Vorys: Vorys was established in 1909 and currently has nearly 375 attorneys in 10 offices in Ohio, Washington, D.C., Texas, Pennsylvania, California, London and Berlin.  Vorys currently ranks as one of the 200 largest law firms in the United States according to American Lawyer magazine.  Learn more at vorys.com.

About FDCC: The Federation of Defense & Corporate Counsel is a group of leaders in the legal community, “dedicated to promoting knowledge, fellowship and professionalism” in the pursuit of a more balanced justice system.

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