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Oil price slides as US and Iran pause fire; cancer treatments help AstraZeneca beat profit forecasts
Rolling coverage of the latest economic and financial news
AmEx raises 2026 revenue growth forecast as affluent cardholders keep spending
American Express increased its full-year revenue growth forecast and beat Wall Street expectations for second-quarter profit on Friday as its affluent customers continued to swipe their cards for travel and dining despite lingering economic uncertainty.
7 events in Lancaster County this week, from a cat benefit car show to a Hispanic festival
With a beautiful weather forecast planned, there's no excuse to not get out and try something new this weekend.
Tencent Music Entertainment Group Stock 12‑Month Price Target Raised to $14.31, Implies 60% Upside
Explore more price target data and ratings for Tencent Music Entertainment Group on the Forecasts tab, and track all previous and future analyst recommendations for Tencent Music Entertainment Group in the dedicated News Flow.Select reference data provided by FactSet. Copyright © 2026 FactSet Resea…
AMC Entertainment Holdings, Inc. Stock 12‑Month Price Target Raised to $2.79, Implies 27% Upside
Explore more price target data and ratings for AMC Entertainment Holdings, Inc. on the Forecasts tab, and track all previous and future analyst recommendations for AMC Entertainment Holdings, Inc. in the dedicated News Flow.Select reference data provided by FactSet. Copyright © 2026 FactSet Researc…
Spain vs. Argentina World Cup Finals Weather Update, Forecast
Ahead of Sunday's World Cup final between Argentina and Spain, here is what the weather forecast is shaping up to be.
'These are striking forecasts': Super El Niño keeps getting even more likely, and it could bring a humanitarian crisis
Forecasts point to the ongoing El Niño rapidly strengthening to a likely all-time record in the coming months, fueling temperature rises and extreme weather.
AMC Entertainment Gears Up For Q2 Print; Here Are The Recent Forecast Changes From Wall Street's Most Acc
AMC (NYSE: AMC) to release Q2 earnings on July 20 before opening bell. Expected loss of 6c/share and revenue of $1.46B.
"Lacks excitement": Netflix tumbles 9% as weak earnings forecast deepens doubts over growth
Netflix's shares tumbled 9.2% before the bell on Friday following another weaker-than-expected earnings forecast from the streaming major, deepening doubts about its ability to sustain growth momentum.
How wildfire smoke affects air quality and your health
Coming up at 9 a.m. on Thursday, MPR News guest host Kelly Gordon talks about how air quality is monitored and forecast and the health risks of breathing wildfire smoke.
United Airlines lifts outlook as travel demand buffers higher costs
The Chicago-based carrier's forecast reflects the whiplash airlines have endured after the Iran conflict sent jet-fuel prices surging earlier this year.
Why Elevance Earnings Gave Health Insurance Stocks A Cold; UNH Up Next
Elevance Health stock dived and inflected UNH and other S&P 500 health insurers after it easily beat Q2 forecasts, but only slightly hiked its outlook.
Elevance raises annual profit forecast on better medical cost management
Elevance Health raised its annual profit forecast after beating second-quarter earnings estimates on Wednesday, as it looks to keep medical costs in check.
Study shows a blood test can help identify healthy people at high risk for Alzheimer's disease
A blood test that helps diagnose Alzheimer’s might also forecast if healthy older adults will develop symptoms
Study shows a blood test can help identify healthy people at high risk for Alzheimer’s disease
A blood test that helps diagnose Alzheimer's might also forecast if healthy older adults will develop symptoms.
ECU Health welcomes 152 new resident physicians and fellows to train across Eastern NC
WCTI ABC 12 Greenville and WYDO Fox 14 Greenville offer local and national news reporting, sports, and weather forecasts to viewers in the Greenville, New Bern, Washington, North Carolina region, including Farmville, Wilson, Rocky Mount, Goldsboro, Snow Hill, Pikeville, and Simpson, North Carolina.
Xbox Project Helix Vs. PS6 - Data Firm Reveals Its Forecasted Sales Figures For Each
But also warns that the outcome "could look very different"
IBM forecasts preliminary Q2 revenue below estimates as spending shifts to AI
IBM's preliminary second-quarter revenue forecast came below Wall Street estimate on Tuesday, as customers prioritized spending on AI infrastructure, including servers, storage and memory purchases, sending its shares slumping 17% in premarket trading.
How Burry’s Prediction-Market Bet and New Products Will Impact Flutter Entertainment (FLUT) Investors
Recently, reports emerged that Michael Burry has taken positions in DraftKings and Flutter Entertainment, framing them as a regulatory-focused investment amid growing attention on prediction markets as competitors to traditional sportsbooks. Flutter Entertainment’s work on its own prediction-market products highlights how the company is actively preparing for possible shifts in how sports-related wagering and forecasting are regulated and offered. We’ll now examine how Flutter’s...
Norway vs. England World Cup Quarterfinals Weather Update, Forecast
The latest update on the weather in Miami ahead of the Norway vs. England quarterfinals match at the World Cup.
Who will win race for Ohio governor? Key forecaster calls it a toss-up
The Ohio governor's race between Vivek Ramaswamy and Amy Acton is now a toss-up, according to one forecaster. See why.
'Not a run-of-the-mill El Nino.' Forecasters predict historic strength
It should rank among the most intense El Ninos since the weather agency started tracking them in 1950.
Lee Zeldin pushes back on long-range climate predictions, 'science denier' label
EPA Administrator Lee Zeldin argued climate projections should be viewed as a range of possible outcomes and said disagreeing with long-term forecasts should not be viewed as science denial. (Credit: Nicholas Ballasy for Fox News Digital)
Melco Resorts & Entertainment Limited Stock 12‑Month Price Target Raised to $8.44, Implies 60% Upside
Explore more price target data and ratings for Melco Resorts & Entertainment Limited on the Forecasts tab, and track all previous and future analyst recommendations for Melco Resorts & Entertainment Limited in the dedicated News Flow.Select reference data provided by FactSet. Copyright © 2026 FactS…
Trees keep absorbing carbon long after they stop growing
Oak trees keep absorbing carbon dioxide long after their annual growth has ended, revealing that photosynthesis and wood production are not as closely linked as scientists once believed. The finding could reshape forecasts of how much carbon forests will be able to store in a warmer future.
Netflix Games Suggest a Flat Future for Entertainment
Xbox layoffs, PlayStation's digital-only future, and Netflix video game forecast an increasingly flattened entertainment economy. Unhinged, review.
State Establishes NC Economic Forecasting Initiative at UNC Kenan-Flagler Business School
The North Carolina General Assembly has allocated $3 million to support the North Carolina Economic Forecasting Initiative (NCEFI). These funds, appropriated from the 2026-27 state budget, will establish the state’s... View Article
Electronic Health Record Ehr Systems Market Forecast Points Higher Toward 2035, Driven by Interoperability Mandates and Cloud Migration
World EHR systems market forecast to 2035: 6-8% CAGR, cloud adoption, interoperability mandates, and value-based care drive growth. Key players and regional insights.
Heatwave conditions forecast in Somerset as health alert issued
Just weeks after a June temperature record was set in Merryfield, Somerset, the hot weather is set to return.
What a 50-year-old Post prediction got right — and wrong
Science editor Thomas O’Toole forecast innovations, including gene editing, deep-sea mining and nuclear-powered artificial hearts.
Reduced sunshine linked to higher demand for mental health care
The study highlights the relationship between weather and mental health care use, suggesting routine weather patterns could forecast service demand effectively.
Apple Eyes 10 Million Foldable iPhones in Major Expansion Move
Apple's Foldable iPhone Strategy Just Got Bigger as Production Forecast Jumps
Northern Lights Forecast: Aurora Could Be Visible In Northern States Today—Days After Powerful X-Class Solar Flare
Forecasters expect a coronal mass ejection and a powerful X-class solar flare could impact the Earth starting on Thursday.
Video shows masked thieves smashing glass, grabbing items during Bowie business break-in
WBFF Fox45 provides local news, weather forecasts, traffic updates, notices of events and items of interest in the community, sports and entertainment programming for Baltimore and nearby towns and communities in Maryland, including Annapolis, Crofton, Glen Burnie, Linthicum, Pasadena, Severn, Cockeysville, Dundalk, Essex, Parkville, Owings Mills, Towson, White Marsh, Woodlawn, Hampstead, Sykesville, Taneytown, Westminster, Elkton, Perryville, Pt. Deposit, Rising Sun, Aberdeen, Abingdon, Bel Air, Edgewood, Fallston, Havre De Grace, Columbia, Elkridge, Ellicott City, Fulton, and Ocean City.
England vow to ‘keep pounding the rock’ shows Thomas Tuchel will stick with his tactic
Thomas Tuchel forecast how DR Congo was going to play. He got it wrong but at least England do have a plan, and they’re going to stick to it
Sudden danger: the science and stakes of flash flood forecasting
Flash floods pose a major threat to lives and property. When it comes to researching them, understanding the rain as it falls is only half the battle. The next step is knowing how the water will move once it hits the ground.
This satellite constellation transformed earth science by creatively tuning in to GPS signals
Governments and companies have launched similar constellations to use the method, called GNSS-R, to monitor and forecast wildfires, flooding and more.
Corona maker Constellation Brands tops forecasts as beer business drives earnings growth
Strong beer performance lifts quarterly resultsConstellation Brands Inc Class A (NYSE:STZ) reported first-quarter results ahead of Wall Street expectations, supported by continued strength in its beer division. Adjusted earnings came in at $3.
Philadelphia Health Department announces Heat Health Emergency ahead of forecast high temps
A Heat Health Emergency has been declared in Philadelphia by the city's health department beginning at 11 a.m. Wednesday, July 1 through 8 p.m. on Saturday, July 4.
Oil markets move from fearing shortages to worrying about surpluses
Global oil supply recovery eases shortage fears as Brent crude falls to $73. Wall Street is lowering forecasts amid demand concerns.
Life Science Reagent Market Forecast Points Higher Toward 2035, Driven by Biopharma Scale-Up and Cell Therapy Expansion
World life science reagent market forecast 2026-2035: CAGR 6-8%, index 2035=185. Bioprocessing, cell therapy, and QC drive growth. Key players: Thermo Fisher, Merck, Danaher.
Conor Sen: This demographic cliff is reshaping universities. Cities are next
An undergraduate enrollment shortfall at Syracuse University is the latest sign that long-forecast demographic pressures are taking their toll on large and well-known institutions, too. Syracuse laid blame on the
Making Our Own Luck
We are cross-posting a piece that former NIH official Kris Willis wrote for Macroscience. Kris is now the President and Founder of the Woodley Park Institute, for which I’m a board member. Picking winners or hedging bets? Federal science agencies have long wrestled with the question of how to ensure their billions of dollars of grants and contracts result in maximum benefits for Americans. On the surface, it might seem that a goal-oriented, interventionist style of management would be the best policy: prioritize our most pressing problems and distribute funding accordingly.This top-down strategy creates a unique hazard, though: bad choices can send resources in the wrong direction, resulting in slow progress that impedes the delivery of tangible benefits to citizens. Moreover, if decision makers pursue an applied advance when the underlying scientific principles remain poorly understood, they risk costly failures and a loss of support for the research enterprise at large. Examples include Nixon’s 1971 War on Cancer, undertaken on the premise that a cure could be achieved in less than a decade, the drive to build an operational commercial fusion plant by the mid-1990s, and the National Plan to Address Alzheimer’s Disease, which failed to produce the effective treatments and prevention strategies promised by 2025. The contrasting laissez-faire approach is to hedge your bets. If the path to an advance is impossible to predict, then the wisest choice might be to distribute funding to as widely as possible. The main risk of this strategy is that spreading funding thinly may limit the resources available to the most promising research, resulting in the same negative outcomes as the interventionist approach: limited support for the most promising topics, slow progress, and delayed returns. In reality, at science agencies like the National Science Foundation and National Institutes of Health, program managers navigate between these two extremes, setting some funds aside to target high-priority areas and using the rest to cover the widest possible range of meritorious proposals. This middle path seems like the most rational way to proceed, but it still requires making choices. How much should be set aside for high priority topics? What should those topics be? How should we define merit? Expert opinion can provide a helpful guide, but leading experts can have remarkably different opinions about what matters and what should be prioritized. Moreover, experts and administrators alike can be risk averse, giving an edge to high-profile, well-established concepts. What’s really needed is a data-driven framework to help guide decision-making, one that doesn’t simply repackage prestige or incumbency. Recognizing breakthroughs years in advance Many breakthroughs fail to attract funding or recognition at the earliest stages of development. Katalin Karikó, for example, spent years struggling to win grants for work on mRNA that would eventually win her and Drew Weissman a Nobel Prize. Similar stories can be told about Jim Allison’s groundbreaking studies leading to the development of cancer immunotherapy, Stan Pruisner’s demonstration that prions can self-replicate without DNA, or Robin Warren and Barry Marshall’s proof that Helicobacter pylori, not stress, causes ulcers. Yet at some point, the scientific community recognizes and responds to transformative breakthroughs like these. How does that change happen? And what if science funders could identify the areas that are on the cusp of a breakthrough? Those questions led my collaborators and me to study how scientists responded to past breakthroughs. Based on patterns in that data, we developed a means of predicting future breakthroughs, using biomedicine as a proof of concept. It’s worth taking a moment to describe how our approach works. Briefly, our prediction process begins by grouping the 18 million or so peer-reviewed papers in PubMed, the authoritative database of biomedical research, into roughly 50,000 unique topics. A small subset of these topics are so widely acclaimed as breakthroughs that they have been recognized with a major prize like a Nobel or a Lasker Award. We theorized that studying the development of these outliers over time, especially before they gained acclaim, was the key to identifying future instances of them. After defining topics, we chose 21 representative breakthroughs and asked what they looked like before they won prestigious prizes. To answer that question, we re-ran our topic-mapping algorithm repeatedly, winding back the clock one year each time, stringing together the years to follow the progress of each field as it grew and advanced. We found that in each case, the years leading up to a discovery followed a pattern: a burst of papers on a new, rapidly evolving topic, many of which quickly became influential. These characteristics—the percent of papers that are new, the percentage of papers that are brought in from other topics, and the influence of each individual paper—can all be measured separately for any given topic. Together, they act as a predictive signal that is detectable an average of five years before a transformative discovery is made, and up to thirty years before the discovery receives a major prize. Scanning the current research landscape for topics that display this signal allows us to predict what work will likely produce a future breakthrough. Each year we analyzed includes four or five signals, a number that appears to have remained constant across a twenty year time frame. In their classic work on the sociology of science, Bruno Latour and Steve Woolgar established that the desire to participate in a major discovery is an important motivator for scientists as they consider whether they should take up (or abandon) a research problem. Although further work is needed, the simplest explanation for our results is that this behavior is widespread enough to detect at scale. A foundational advance, or something like it, draws the attention of scientists and causes them to change the direction of their research. They vote with their feet, staking their own reputation and careers by publishing on and citing the new idea. That upswell of interest is the foundation of our breakthrough signal. What kind of research does our model identify as a breakthrough? After building a model based on the patterns in our 21 preselected breakthroughs, we tested it by identifying all early signals of discovery from 1994 through 1997. Of the 18 signals we found in that time period, 17 can be traced forward in time to a breakthrough. Examples include super-resolution fluorescence microscopy (see the image above), the directed evolution of proteins and enzymes (Chemistry Nobel, 2018), and sequencing the human genome (National Medal of Science, 2008). The discovery of the role of leptin signaling in obesity is a particularly interesting case study. Around 1950, scientists noticed the existence of a type of lab mouse that suffered from a unique inherited form of obesity. When given the freedom to choose their own diet, these mice ate more than normal and preferred food that was high in fat. Researchers had few clues as to why the animals overate; early experiments pointed to the existence of a soluble ob factor found circulating in the bloodstream that regulated appetite, but its identity was unknown. In 1994, Jeffrey Friedman and colleagues identified and sequenced the ob gene, noting that the protein it encoded appeared to fit the profile of a circulating factor. A year later, a trio of high-profile publications demonstrated that ob was a hormone that regulated body weight and fat deposition by regulating appetite. One of these was co-authored by Friedman, who christened it leptin from the ancient Greek word for thin. Using our model, the data from 1996 make it clear this advance would become a breakthrough. Scientists had flocked to study the biology of the new hormone, generating the flurry of new papers and citations required to produce a signal. One of these demonstrated that serum leptin concentrations reflected the amount of adipose tissue in the human body, providing clinical validation of the earlier mouse studies. Before the identification of leptin, scientists were unable to point to a specific molecule that controlled appetite and adiposity, so that it was impossible to rule out non-physiological causes of obesity; its characterization fundamentally changed our concept of weight gain. In 2010, Friedman and fellow pioneer Douglas Coleman were awarded a Lasker for the discovery. Our work also identifies breakthroughs in clinical practice and behavioral and social sciences, even though these areas are less likely to draw the attention of major prize committees. One example that falls into this category is the introduction of endovascular aneurysm repair, a minimally invasive surgical procedure for the treatment of abdominal aortic aneurysm, a leading cause of death among Americans over the age of 65. Relative to the previous standard of care, it reduces in-hospital mortality by almost four-fold. In 2017, Timothy Chuter received the American College of Surgeons’ prestigious Jacobsen Innovation award for his multiple refinements to the technique. Another under-recognized breakthrough identified by our model is the development of standardized survey instruments to assess the quality of life for HIV+ patients. The antiretroviral cocktails introduced in the mid-1990s reduced AIDS mortality, but treatment came with serious side effects. The new evaluations showed that patients whose disease progressed had worse physical functioning than Americans with other chronic diseases, while those who were asymptomatic maintained a health-related quality of life on par with the overall US population. This evidence made it possible for patients and activists to argue that even in the absence of a total cure, and in spite of the side effects, alleviation of symptoms was a worthwhile priority for clinicians. My colleagues and I have failed to identify any significant recognition for the physicians and health policy experts who pioneered this advance, although in 2016, researchers called for the World Health Organization to set targets for good health-related quality of life as part of its framework to end the AIDS pandemic. From forecasting to funding How should science funders use our predictions? Any serious discussion of what we should do with this new capability needs to begin with an understanding of its limitations. We can’t (and shouldn’t) invest all of our resources in the small number of topics predicted to produce breakthroughs. Innovative new fields are born from existing ones; they rarely arise de novo. If funders don’t maintain a diverse portfolio, the breakthroughs of tomorrow will have no antecedents. Think of this as eating the seed corn, or killing the goose that lays the golden egg. There are also multiple goals of funding beyond basic scientific discovery, including translating discoveries into clinical practice, developing new technologies, supporting economic development, and training new scientists. The investments needed to accomplish these goals are likely different from those required to support emerging breakthroughs. Finally, we must be aware that we may not capture every instance of a breakthrough. The concept is fuzzy, and not every significant discovery is recognized with a major award. Further, breakthroughs are rare, meaning that our dataset is, by necessity, small. This makes formal estimates of accuracy challenging. Although our overall success rate appears to be high, any one signal may turn out to be a false positive. We found one apparent example: the development of the third generation COX-2 inhibitors, Vioxx and Celebrex, which were much celebrated in the early 2000s as effective but non-addictive pain relievers, meets all the algorithmic criteria of a breakthrough. Five years after it received FDA approval, researchers demonstrated that Vioxx was associated with serious cardiovascular complications, and the manufacturer voluntarily pulled the drug from the market. Celebrex remains available, although like other NSAIDs, it carries an FDA warning for increased cardiovascular risk. Decision makers who were overly focused on the breakthrough potential of this research might have wasted substantial resources nurturing an area that ultimately proved to be a failure—but so would those who relied on expert opinion at the time. That said, the potential benefits of funding the next mRNA vaccine or cancer immunotherapy years earlier than we might have are large. When we can’t assume the outcome of an action, it’s worth conducting an experiment. Can targeted support for areas that are poised to produce a breakthrough increase the return on scientific investment without cannibalizing future advances? With the necessary administrative infrastructure in place, conducting this experiment would be straightforward. First, identify all the breakthrough signals between 2018 and 2023, select half at random, and commit to a decade of investment. For biomedicine, a six year window should yield about 30 topics with the potential to produce a breakthrough. Providing strong support to half of these might be accomplished for $300 million a year. That’s roughly half of the recent annual budget of the NIH Common Fund or around 20% of the fiscal year 2025 budget for ARPA-H. After five years, then again at ten, compare to see which group — the one that was actively managed, or the control — produced more breakthroughs, and on what timescale. Although we used biomedicine as an example, the same experiment could just as well be run for physics, materials science, or any other discipline. Any attempt to undertake such an experiment should keep three points top of mind. First, we can predict the topic of future breakthroughs, but the people and vision for how to move the work forward are still important. In practice, this means success requires active program managers with the expertise to develop the science and the authority to do so, including by recruiting investigators. Second, we would be wise to learn from the failures of past attempts to fund transformative research. High among these are funding modestly repackaged work, confusing the genuinely novel with the merely unfamiliar, and relying too much on incumbent investigators. All of these risks can be mitigated by thoroughly and intelligently integrating high quality data into portfolio management. Finally, we need to have patience. Even with strong funding support, producing a breakthrough takes time. If supporting breakthroughs is the best way to nurture the birth of new fields, we also need to accurately describe and properly manage the other two stages, so that we invest wisely throughout the productive lifetime of ideas and divest once they reach the point of diminishing returns. We would benefit from more study of all of these phases, especially the transition from one to another. More research could answer questions like: When does a breakthrough become an established field? How do the availability of funding, the size and characteristics of the available workforce, and the accumulation of evidence that disagrees with prevailing models affect a field’s decline? Exactly how do old ideas give birth to new ones, and can we speed up the process? The more we understand about the dynamics of scientific progress, the better we can maximize the odds that our curiosity leads to results. I’d call that finding a way to make our own luck.
Micron and Qualcomm forecasts ignite $400 billion AI chip stock rally
Shares of chipmakers surged late on Wednesday, adding over $400 billion in market value after strong forecasts from Micron Technology and Qualcomm breathed fresh life into Wall Street's recently waning AI stock rally.
Micron forecasts quarterly revenue above estimates
Micron Technology forecast quarterly revenue above Wall Street estimates on Wednesday, signaling that insatiable demand for AI-related infrastructure will continue to drive strong demand for its memory chips.
WMO Executive Council seeks to balance growing demands for science and services
“The demand for what WMO delivers and what National Meteorological and Hydrological Services produce - weather forecasts and warnings, climate data and standards and for scientific cooperation - is growing. Every extreme event makes that clearer,” WMO Secretary-General Celeste Saulo told the opening session.
Federal science cuts could threaten California wildfire forecasts
More cuts to federal science programs could threaten weather forecasts crucial to predicting wildfire behavior.
BofA forecasts 75 bps of rate hikes in 2026 on labour market resilience, new Fed chair
BofA Global Research expects the Federal Reserve to hike interest rates by 75 basis points in 2026, it said on Monday, citing resilient economic data and rising expectations of a hawkish Fed under new Chair Kevin Warsh.
Global entertainment revenues to reach $4.2 trillion by 2030 as advertising powers industry growth
The global media industry is on course to generate US$4.2 trillion in annual revenues by 2030, according to the latest forecasts. The sector will add around US$600 billion in new revenue over the n…
Record-breaking UK heatwave forecast as health warnings issued
London (PA Media/dpa) -The sweltering heat in the United Kingdom could last until at least Thursday, sparking weather warnings and concerns for vulnerable people, the Met Office said on Monday. Foreca...
Science-Backed Beauty Formulations Market Size and Growth Analysis Study 2026 to 2035
Global Science-Backed Beauty Formulations Market Size is predicted to grow at a 8.6% CAGR during the forecast period for 2026 to 2035. The skincare formulations segment is set to take a large share of the science-backed beauty market. This growth comes from more people focusing on skin health, anti-ageing, acne, hyperpigmentation, and barrier repair.
Health warnings in Kent, Sussex and Surrey as heatwave forecast
An amber heat-health alert for the South East has been issued by the UK Health Security Agency.
UK borrowing surges over forecasts in May as government spending rises
Rolling coverage of the latest economic and financial news
Health and Wellness Market Size to Hit $12.9 Trillion by 2031 Growing at a CAGR of 10.9%: Allied Market Research
As per a new report published by Allied Market Research, titled, "Health and Wellness Market by Product Type (Beauty & Personal Care Products, Health & Wellness Food, Wellness Tourism, Fitness Equipment and Others): Global Opportunity Analysis and Industry Forecast, 2022–2031," the global health and wellness market size was valued at $4.7 trillion in 2021 and is estimated to reach $12.9 trillion by 2031, growing at a compound annual growth rate (CAGR) of 10.9% from 2022 to 2031.
What smart people are saying about Kevin Warsh, the new Fed Chair
New Fed chair Kevin Warsh signaled a sweeping overhaul of the central bank, from forecasts and forward guidance to AI and operations.
The week in politics (June 17, 2026)
On Wednesday's show: We get an update on the forecast for rainy weather across the region. And we discuss the latest developments in politics in our weekly roundup.
Fund honoring late meteorologist to assist in global student experience
Jenni Evans, a world-renowned meteorologist whose research transformed the scientific understanding of tropical cyclones and strengthened storm forecasting worldwide, considered her students’ success her proudest accomplishment. The newly established Jenni Evans Fund in the Department of Meteorology and Atmospheric Science will ensure that those successes continue in perpetuity.
NASA Uses Machine Learning to Enhance Flash Flood Warnings
The Transient Artifact and Continuous Learning System (TACLS) leverages data from continuously operating satellite networks coupled with machine learning models to help meteorologists at the National Weather Service forecast flash floods more efficiently.
SpaceX Bolsters AI Business With $60 Bil Deal. Musk Targets $1 Tril In Revenue.
SpaceX boosts AI business with $60 billion deal for Cursor. Elon Musk makes this bold revenue target. ARK details its SpaceX forecast.
Dave & Buster's Entertainment, Inc. Stock 12‑Month Price Target Cut to $19.78, Implies 61% Upside
Explore more price target data and ratings for Dave & Buster's Entertainment, Inc. on the Forecasts tab, and track all previous and future analyst recommendations for Dave & Buster's Entertainment, Inc. in the dedicated News Flow.Select reference data provided by FactSet. Copyright © 2026 FactSet R…
Dave & Buster's Entertainment, Inc. Stock 12‑Month Price Target Cut to $20.11, Implies 63% Upside
Explore more price target data and ratings for Dave & Buster's Entertainment, Inc. on the Forecasts tab, and track all previous and future analyst recommendations for Dave & Buster's Entertainment, Inc. in the dedicated News Flow.Select reference data provided by FactSet. Copyright © 2026 FactSet R…
What is Helium-3 and could we get it from the moon?
Helium-3 is expensive and demand is forecast to soar, so some are planning to mine it on the moon.
AMS Science Preview: 30-Day Forecasts? Little Ice Age, wave-dampening hurricanes
The latest AMS journals research discusses the possibility of 30-day weather forecasts, whether the Little Ice Age was caused by depopulation of the Americas, the cooling effects of multi-year La Nina…
NFL Analyst Makes Prediction for Bucs' Season
Former Super Bowl champion Logan Ryan envisions a dominant NFC South run for Tampa Bay, forecasting a double-digit win season fueled by Todd Bowles’ sophisticated defensive schemes.
Why Accel Entertainment (ACEL) Is Up 9.5% After Beating Revenue Forecasts With 8.5% Growth
Earlier this quarter, Accel Entertainment reported quarterly revenue growth of 8.5% year on year, surpassing analyst expectations even as operating income trends were mixed. This revenue surprise stands out because it reflects resilience in Accel’s local gaming footprint while many consumer discretionary peers also beat sales forecasts. Next, we’ll examine how this stronger-than-expected revenue performance may influence Accel Entertainment’s existing investment narrative and growth...
Maryland Science Center marks 50th anniversary; Harbor plaza groundbreaking
WBFF Fox45 provides local news, weather forecasts, traffic updates, notices of events and items of interest in the community, sports and entertainment programming for Baltimore and nearby towns and communities in Maryland, including Annapolis, Crofton, Glen Burnie, Linthicum, Pasadena, Severn, Cockeysville, Dundalk, Essex, Parkville, Owings Mills, Towson, White Marsh, Woodlawn, Hampstead, Sykesville, Taneytown, Westminster, Elkton, Perryville, Pt. Deposit, Rising Sun, Aberdeen, Abingdon, Bel Air, Edgewood, Fallston, Havre De Grace, Columbia, Elkridge, Ellicott City, Fulton, and Ocean City.
She Used A.I. to Create Better Forecasts for Extreme Weather. Then Her Funding Was Cut.
Amy McGovern was the director of an A.I. institute dedicated to weather forecasting — until the National Science Foundation ended its funding.
Braves versus White Sox rain delay updates
It’s raining in Chicago, y’all. The weather has not been conducive to playing baseball most of the afternoon in Chicago. Here’s a forecast. Looks decent at 10pm and it appears that MLB wants this game to happen and happen in front of 25 people. Stay tuned for updates.
Senate map tightens as top forecaster moves 3 races toward Democrats
Sabato's Crystal Ball shifts three Senate races toward Democrats, saying they have a clearer path to winning the majority in the midterm elections.
Assessing Accel Entertainment (ACEL) Valuation After A Mixed Q1 Earnings Beat And Modest Share Price Reaction
Q1 report and tempered market reaction Accel Entertainment (ACEL) recently posted its Q1 results, with revenue up 8.5% year over year and ahead of analyst expectations, while earnings per share also beat forecasts but adjusted operating income fell short. The stock has seen a modest move higher since the release, suggesting that investors are weighing stronger top line and EPS figures against the softer adjusted operating income performance as they reassess the company’s current setup. See...
CPI: Inflation rose to 4.2% in May, matching the forecast
The new CPI report showed inflation in May climbed to the highest rate in about three years.
The World Cup could be a petri dish for disease. Wastewater could sound the alarm
As millions of soccer fans pack FIFA World Cup venues, public health scientists created a wastewater monitoring network to forecast potential disease threats—from measles to Ebola
University of Miami joins new NOAA Cooperative Institute to advance sustainable U.S. aquaculture
Rosenstiel School researchers will contribute expertise in ocean science, environmental forecasting, and aquaculture innovation through a national partnership focused on strengthening America's seafood supply.
We can create the future of science right now
Paul Litvak is the founder and Executive Director of the Robyn Dawes Institute and a Visiting Scholar at UC Berkeley. He has a PhD in Behavioral Decision Research from Carnegie Mellon where he studied emotions and the sunk cost bias. Over 15 years in industry, he solved a wide range of challenging technical problems. At Meta he created models optimizing the ad review process. At Google he ran experiments to measure social influence. At Airbnb, he was a product manager leading machine learning teams optimizing search ranking and price suggestions. He also co-founded and led product at Rhythmic Health, a venture-backed biosensing startup, creating an accurate low cost system using a color changing strip and a smart phone to measure salivary lactate. The bottleneck At this point, it is uncontroversial to say that science needs to stop using the PDF article as the unit of knowledge and currency. The unbearable slowness of scientific publishing, the profit motive and margins: I’m not saying anything new. The PDF also sucks because it’s hard to extract structured information, which makes it hard to do evidence synthesis. As a result, we do much less evidence synthesis than is needed. And evidence synthesis ultimately undergirds most policy and medical decision-making. I can see second-by-second real-time odds for any sporting or newsworthy event, but a school board can’t see the best evidence on whether their 8th graders should be taught algebra. As a society, we don’t treat this as an important problem. Again, not controversial. Not only is the problem well understood, but the solution has already been laid out. What we need is AI-assisted living evidence synthesis - (1) an open knowledge graph of atomic claims, (2) claims linked to evidence, (3) assessment and synthesis of each piece of evidence, and (4) continuous updating with new data. What few realize (yet) is that the technical capacity to build this vision for a significant portion of science already exists. Not only that, scientists and startup teams are already building many of these components. I know this because I’ve been surveying the space and talking to many of the builders. There are some missing pieces: for example, evaluations of how well some of the components work. But at this point, most of what’s missing is a fully end-to-end working integration of all of these parts. In the rest of this essay, I’m going to lay out all the parts of a working living evidence layer for science and who is working on them, and propose concrete next steps for building this system. What’s now possible The diagram above outlines the components of a living evidence synthesis platform, including some of the teams working on each component1. Scientific PDFs are processed into claims with associated evidence. The evidence is subjected to a forensic audit, methodological evaluation and robustness and reproducibility checks. Finally it’s given a weight in a continuously updating synthesis. What follows is a description and status of each component and a few of the teams working on them. Document understanding The first thing you need to be able to do is turn an article into structured data. Mostly that means parsing PDFs. There are often multicolumn layouts that confuse non-specialized PDF to text processing libraries. For scientific papers, there is the added complexity of parsing formulas and tables and figures. This is a really hot area - there are startups offering APIs, and it seems like a new open source package gets posted to Github every few weeks. What follows isn’t exhaustive. A package called GROBID was the state of the art for a while, they didn’t update their package for nearly two years until very recently. In the meantime reducto.ai released an AI powered PDF extraction API, PaddleOCR became popular, IBM released a model called Docling, and both Mistral and Gemini created models and libraries. I also know of at least one other well-funded psychology research group working on a paper parser. By contrast, there are few open evals in this space, with no extensive evals for complex table comprehension in particular. Nonetheless, I’m confident this will be a solved problem soon, given the combination of LLM advances and developer interest. Hypothesis level extraction There has been increasing interest in comprehending the extracted text of papers and linking information to evidence for each hypothesis. A lot of work has already been done. Trialstreamer (Marshall et al. 2020) and RobotReviewer LIVE (Marshall et al. 2023) demonstrated automated extraction of trial population, intervention, and outcome at scale on clinical RCTs. PaperQA2 (Skarlinski et al. 2024) and Ai2 ScholarQA (2024) extended this to retrieval-augmented question answering with citation grounding. Elicit, Consensus, and SciSpace operationalized claim-level extraction for end users. OpenEval (Booeshaghi et al. 2026) is the most recent and most ambitious: 1.96 million atomic claims extracted from 16,087 eLife manuscripts using Claude Sonnet 4.5, grouped into ~299,000 results, with LLM evaluations showing 81% agreement with human peer review on a 2,487-paper subset. None of these solutions link claims to test-statistics, as you would need to evaluate randomized controlled trials. This is why I built the evidence.guide API - to extract hypotheses and associated test statistics from behavioral science papers. The best public eval of this kind of extraction I’m aware of comes from the recent SCORE project - they had humans code thousands of psychology papers to extract their claims by hand. It would be extremely helpful to the world if all scientific PDFs were available as structured open data. I’ve been working to make this happen, both directly at Berkeley and through coordination with large entities I can’t yet speak of; as hard as it is to do, I think it’s possible2. Forensic audit A lot of work has been done on forensic audit, but some gaps remain. Of course, for biology papers that rely on images for evidence, there are a variety of tools (notably Proofig and ImageTwin) to spot anomalies. These are still well short of what sleuths like Elizabeth Bik can do on her own, but these tools are constant companions among fraud analysts. There’s someone working on auditing Excel files for anomalies, and a number of teams are automating numerical checks like GRIM and SPRITE, including Scrutiny project, the INSPECT-SR team as well as statcheck. The regcheck team is building a way to use AI to compare preregistrations to analyses in papers, to ensure there aren’t significant deviations. Nonetheless, there are many other kinds of anomalies to screen for, both public and less publicly known. And there are no formal evals for anomaly detection that I’m aware of. Still, there’s a lot to draw from in this space and I’m pretty certain we will be able to scan papers for most kinds of obvious anomalies in the near future. Methodological review This area has been white hot, though I fear for many of the startups in this space, because this capability may become commoditized. There are at least six different AI peer review companies, including Refine.ink, Reviewer3, ReviewerZero.ai, Q.E.D. Science, Paper Wizard, and Isitcredible. Coarse (a pun on refine) was also recently created as an open source alternative. These systems provide qualitative feedback on the content of papers, spotting methodological weaknesses and mathematical errors. They seem to work pretty well, and many academics report bitterly that they exceed the average quality of typical peer reviewers. But there are few evals here either. What evals exist so far involve using LLM-as-judge (circularity problems abound) or comparing against human reviews of questionable quality. What you’d ideally want is an eval that measures capturing known errors in papers3. Reproducibility and robustness Another active area has been using AI agents to automate computational reproducibility4 and robustness5 checks in papers that report numerical results. For more recent papers where data and code are available, AI agents can see whether they can re-run the analyses and produce the numbers reported in the published paper. In addition to a handful of individual academics who have been experimenting using Claude Code for this, the Institute for Replication is a leading group working on building an end to end system. The evals related to this problem are the most mature, with CORE-Bench (Siegel, Kapoor, Narayanan 2024) and PaperBench (OpenAI 2025) available to benchmark agents on this task. There is also work on getting AI agents to test alternative ways of analyzing the data to ensure the results are robust to small analytic design choices. Synthesis This is the most underdeveloped area where significant investment is required. Although some automated evidence synthesis systems exist — for example, otto-sr is building an AI agent to write systematic reviews — none of these incorporate the full range of paper level signals to weight evidence appropriately. Nor is there anything like an eval or a gold standard for a good systematic review. Arguably Cochrane reviews are the closest we have to gold standard human systematic reviews, though I’ve heard academics in the know complain about their uneven quality. A key question for a synthesis platform is how to weight anomalies and methodological issues in assessing the quality of a piece of evidence. This is an unsolved problem and one I’m very keen to work on. Continuous updating There are many pieces of basic infrastructure available for monitoring for new research and initiating updates. OpenAlex is the current open citation graph. Retraction Watch integrated into Crossref in October 2023. Scite tracks how citations support, contrast, or mention prior claims. The Living Evidence Network demonstrated continuous-update workflows in clinical guidelines. Engineering this is a relatively straightforward task. When you look over this technical architecture and all the progress being made, it’s hard not to be optimistic that a living guide to scientific evidence will be built. The stakeholders are ready The social infrastructure for this is starting to coalesce — it’s not just a pie in the sky academic exercise to imagine this coming into existence. Institutions like the Center for Open Science, the Institute for Replication, the INSPECT-SR, the Living Evidence Network and more are all working on scaling work to improve research quality. Funders are also aligned. The Sloan Foundation has funded living evidence work through COS. Coefficient Giving supports the Institute for Replication and COS. The Astera Institute and the Institute for Progress have shown interest in this space. NIH has established an Office for Replication and Reproducibility. Although there are (very unfortunately) serious headwinds in science funding generally, there is an active group of funders interested in metascience. A brief word about what I’ve been doing at RDI. First, as a Visiting Scholar at Berkeley I’ve been actively figuring out how a non-profit and a public university can conduct and make public the results of large-scale academic article data mining. With some of the money I raised from donors, I commissioned a legal analysis of recent case law and publisher text data mining (TDM) agreements in order to understand whether a massive open data mining of academic articles is possible (with caveats, it is). I’ve also been working to bring together stakeholders in this space, and identify gaps. I’ve also been doing some software development in this space, with more to come. A pilot proposal The assumption undergirding all of this is that an AI, given all this information, would make the right judgment about a scientific claim with lots of conflicting evidence, weighing all the factors appropriately. That’s the hypothesis we need to test. Randomized control trial research is the best place to focus on first. RCTs are used to make many of the important decisions in society - from medical trials to public policy changes. And they use a relatively uniform set of inferential statistics with lots of known and available diagnostics. Behavioral science experiments, within the broader realm of RCTs, should be first used as a testbed whose results can be generalized. Because behavioral science is at the vanguard of open science practices, replications abound (there are thousands of them) to serve as ground truth training data. Key Hypothesis Therefore the pilot would test, in behavioral RCTs that have been replicated, whether the quality of evidence for a claim can be used to accurately predict whether that claim will replicate. Secondary Hypotheses Compared to claims that replicated, non-replicated claims demonstrate a greater share of forensic anomalies in their source literatures. Hypothesis level claim and statistic extraction is accurate enough to scale living evidence without onerous human review costs. Replication prediction is more accurate than prediction markets6 or journal prestige. If all the different quality signals we gather do accurately predict which studies will replicate, then we can use that model to score evidence to power the living evidence layer. Why this is informative regardless of outcome If the pilot succeeds, the architecture extends to medical RCTs (where Living Evidence already operates and integration is mostly about claim representation), then to slices of basic biology with stable replication structure. If it fails, the field learns which quality signals are load-bearing and which ones metascience has oversold. Either result is a contribution to knowing what the literature supports. Implications for funders Because this burgeoning ecosystem of builders already exists, a well-informed philanthropic or government funder could play a crucial catalyzing role in bringing this future about. They could play at least three roles: creating open structured datasets, publishing open benchmarks and incentivizing the solving of key technical challenges. First, open archives of papers that the government maintains, like PubMed, could be turned into structured data amenable to large scale metascience and claim aggregation. I know the US government already has an interest in doing this, though some key open questions remain unanswered. How do you determine the best models and systems for accurately extracting information from papers? How can you establish a robust way to allow researchers to flag errors and correct them? And finally, how do you create a legal regime in working with publishers to maximize the scope of available papers? For the latter, a university or a private philanthropy may be better positioned to make structured data publicly available under journal subscription terms or fair use. Philanthropists or government funders could also coordinate to create or commission benchmarks that evaluate whether important problems have been solved. For example, an open benchmark for claim extraction from a range of different scientific article types would be extremely helpful. Ensuring the underlying data are accurate is vital for creating these evaluations. I’ve discussed opportunistically using various human-created datasets for this purpose, but a consistent problem is that errors in human data make it difficult for them to serve as a gold standard. Finally, with structured data and benchmarks available, the government or private philanthropy could use them to incentivize groups to develop machine learning systems that meet these benchmarks. Prizes are one potentially valuable tool for this. For example, you could establish a prize for a system that accurately updates a living meta-analysis for a small set of claims. Prizes are particularly useful as signals of problem importance, and can help create vibrant ecosystems of public and private research—see, for example the role the government played in kickstarting the current work on nuclear fusion. Conclusion The drawbacks of the current scientific publishing system are known. Scientists agree, metascientists agree, philanthropists agree: the published PDF plus citation graph isn’t the right substrate for maintaining a representation of the evidence base in science. The pieces needed to build the alternative either already exist or are rapidly taking shape. The community is forming around exactly this problem, with concrete partnerships and shared infrastructure. A pilot should start on behavioral science RCTs because that’s the slice of empirical science most amenable to legibility, where replication ground truth is richest, and where the failure modes are best documented. What’s been missing is the galvanizing mission to assemble these pieces into something that works. That’s what I’m proposing to build. *** 1 I have a broader field map that I’ll release publicly soon. This is me, building in public! 2 If this is something that you are excited about, please reach out and talk to me. 3 More on this very soon too! 4 This tests whether, given the code and the data, you can get the same statistics as reported in the published paper. 5 This tests whether the results are the same as a paper’s given alternative analytical decisions in conducting the analysis (like outlier omission). Closely related is the idea of a “multiverse” where you come up with many different ways of answering the same underlying research question with the same data, and test whether the results hold in all those alternative methods. There’s been work on the latter as well. 6 Some of the replications, e.g. those from the SCORE project, had paired the experiments with forecasts from prediction markets. So we get to look at this for free.
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