Le Journal

President Donald Trump disembarks Air Force One as he arrives at Zurich Airport before attending the World Economic Forum in Davos, Switzerland, on January 21, 2026. | Chip Somodevilla/Getty Images This story appeared in The Logoff, a daily newsletter that helps you stay informed about the Trump administration without letting political news take over your life. Subscribe here. Welcome to The Logoff: Donald Trump is once again moving markets — and being moved by them in turn.  What happened? On Wednesday afternoon, the president said in a social media post that he was backing away from new tariffs on Europe after reaching a “framework of a future deal with respect to Greenland,” potentially involving US sovereignty over new US military bases there. The Dow and other US stock indices jumped in response, after suffering a bad day on Tuesday because of Trump’s continuing threats. It’s the latest example of one of the more reliable patterns to emerge from Trump’s first year back in office: TACO, or “Trump always chickens out.” Why TACO? The term comes from a Financial Times column in May breaking down the market response to Trump backing down on his “Liberation Day” tariffs. As my colleague Andrew Prokop explained at the time, the short-lived tariffs pushed the market to the verge of crisis, and Trump blinked. TACO postulates Trump always handles market dips the same way: Once the damage gets bad enough, he will relent. What’s the context? Until Wednesday, Trump had been all-in on acquiring Greenland, logic and decades-old US alliances be damned. Over the weekend, he announced escalating tariffs on eight European countries over their opposition to the plan, and threatened Norway’s prime minister. What’s the big picture? TACO is, in some ways, good news — a built-in circuit breaker for some of Trump’s more damaging impulses. But as Arin Dube, a University of Massachusetts economics professor, points out, it’s not a permanent solution. As markets grow accustomed to Trump backing down, they’re less likely to get jittery in the first place; if they don’t fall, Trump also doesn’t get the signal to stop touching the stove. The result could be an ever-elongating TACO cycle where Trump tiptoes closer to the brink of a seriously damaging decision — and maybe over it — before the markets send a signal he should reconsider. And with that, it’s time to log off… Last month, my colleague Benji Jones traveled to Australia to report on “the largest effort ever mounted to keep the Great Barrier Reef alive.” The Australian government’s nearly $300 million Reef Restoration and Adaptation Program is planned as a multi-decade effort to bolster the dying reefs, including by spawning new corals in a lab. You can read his excellent reporting here, along with some wonderful photos of the reefs. Enjoy, and we’ll see you back here tomorrow!

Le groupe belge Chaton Laveur présente l’extrait La source qui se retrouvera sur son album Labyrinthe, qui sortira le 13 mars prochain. On y retrouve un rock texturé qui est… L’article La source est apparu en premier sur Le Canal Auditif.

Ceux qui ont suivi Blesse depuis que le groupe est né des cendres de Zen Bamboo vont faire le saut en écoutant le premier extrait de leur prochain album prévu… L’article (Tragédie) est apparu en premier sur Le Canal Auditif.

The FDA released draft guidance in January 2026 encouraging the use of Bayesian statistics in clinical trials, a shift that could benefit patients with rare diseases like ALS. Here’s something strange about how we test new drugs: Every clinical trial has to pretend that nothing like it has ever come before. Even if clinicians have tested similar drugs for years, or if decades of research point in a certain direction, each trial must prove — independently — that the drug works based solely on what happens inside that specific study. Prior knowledge doesn’t count.  This story was first featured in the Future Perfect newsletter. Sign up here to explore the big, complicated problems the world faces and the most efficient ways to solve them. Sent twice a week. For more than 60 years, this blank slate approach has been the Food and Drug Administration’s gold standard — and for good reason. If you let prior research formally count toward proving a drug works, drug companies might easily cherry-pick the studies that flatter their results. Naturally, such rules have led to academic circle jerks over whether past research should factor into the final verdict on a drug. But for patients, the cost of starting from scratch every time can be high.  For people with rare diseases, where only a few hundred individuals worldwide might have a condition, running a traditional trial can be nearly impossible, because there simply aren’t enough patients to enroll. For children, it has meant re-proving what we already learned in adults. And for everyone, it has meant slower, more expensive trials that throw away useful information. Now, the FDA is telling drug companies and researchers they don’t have to start from scratch anymore. Last week, the agency released new guidance encouraging companies to use a statistical approach, that would usually be used on a case-by-case basis, called Bayesian methods. (We’ll get more into that later.)  What that means is that, for the first time, companies can formally incorporate what they already know — from earlier studies, from related drugs, from real-world evidence — to help answer the central question of whether a drug works. The FDA’s guidance is still a draft, and details may shift over the coming months, but the policy signal is clear.  “It sounds so intuitive to just use the data that you have before to inform the next thing that you do,” said Merit Cudkowicz, a neurologist at Massachusetts General Hospital who runs a major ALS clinical trial, “instead of just having this sort of amnesia.” Two ways of looking at the world For a drug to get FDA approval, it has to prove it works in three phases of clinical trials. But “proving it works” can mean different things, depending on how you handle uncertainty.  The traditional approach — called frequentist statistics — asks a narrow question: If this drug doesn’t actually work, how likely is it that we’d see results this strong just by chance? If that probability is very low (typically below 5 percent), the drug passes the test. The appeal is objectivity; the trial data speaks for itself, and what you believed going in doesn’t formally enter the math. Bayesian statistics, the new rule of the land, flips the question. It asks: Based on everything we already know, how likely is it that this drug works? Then, it updates that estimate as new trial data comes in. The result isn’t a binary pass/fail, but a probability — say, a 94 percent chance the drug is effective. That doesn’t mean anything goes, and the FDA still has to draw a line in the sand that’s pre-agreed before the trial runs.  The practical upshot is that Bayesian methods let you formally “borrow” information from other places. If you’ve already tested a drug in adults, you can use that data when evaluating it in children. If you’re running a trial with multiple drugs, data from one arm of the study can inform another. This flexibility matters most in situations where…

The Dallas Mavericks’ rookie star and number one overall pick Cooper Flagg just played the 41st game of his young career on Monday against the New York Knicks. His inaugural season has been a little interesting. Head coach Jason Kidd experimented with Flagg at point guard to open the season, a tough situation for any young player. The Mavericks’ odd lineups made it even tougher for the first year forward. Kidd eventually pivoted away from that experiment and put Flagg at the forward spot, and things improved drastically. Flagg won Western Conference rookie of the month in both November and December, and looks to be in a tight race with Kon Knueppel for Rookie of the Year. With half of his first season in the books, I wanted to look back at the last ten years of number one overall picks and see how Flagg compares. I compiled each number one pick’s basic counting stats—field goal percentage, 3-point percentage, points, rebounds, assists, steals, blocks, and 3-pointers made. Slash stats are points/rebounds/assists per game. All stats are through a player’s first 41 games of their career, regardless of whether those first 41 games came in their rookie year or in later seasons. 2025: Cooper Flagg 48% FG, 29% 3P, 18.8/6.3/4.1, 1.3 steals, .8 blocks Totals: 770 points, 257 rebounds, 170 assists, 52 steals, 32 blocks, 4o 3PM Flagg didn’t land into the perfect situation. The Mavericks are still reeling from the Luka Doncic trade, left in an odd purgatory. The roster is built poorly, expensive, yet far from being a contender for much of anything besides the play-in tournament. Worse still, the best player on that awkwardly fit roster, Anthony Davis, is consistently injured. Yet the Mavericks haven’t made any moves toward rebuilding the team around Flagg. Head coach Jason Kidd went full mad scientist and experimented with Flagg at point guard early in the season. With Davis in and out of the lineup, there’s been no consistent rotation. Trade rumors and the detritus from the Doncic trade still linger over the franchise. Flagg’s first 41 games in the NBA haven’t been ideal. But he’s put up numbers just as good or better than several of the number one overall picks listed below. 2015: Karl-Anthony Towns 52% FG, 33% 3P, 15.6/9.5/1.2, .6 steals, 1.7 blocks Totals: 638 points, 391 rebounds, 50 assists, 26 steals, 69 blocks, 11 3PM Towns was a good scorer the first half of his rookie season, but really stood out on defense. He’s one of the better rebounders on this list, and is second only to Victor Wembanyama in blocks. Flagg edges Towns in scoring and is a better playmaker early on, and has better on ball defense. He’s also a much better shooter (40 3pm through 41 games compared to Towns’ 11) than the self-proclaimed “best shooting big man in NBA history.” 2016: Ben Simmons 51% FG, 0% 3P, 16.6/8/7.2, 1.9 steals, .9 blocks Totals: 679 points, 328 rebounds, 296 assists, 77 steals, 38 blocks, 0 3PM Simmons’ career has derailed to a point where he is now a professional competitive fisherman (no really, he is), so it’s easy to forget what a stat-stuffing phenom he was early on. Simmons did a little of everything on the basketball court, except shoot 3’s. The Sixers had a more coherent vision for their team when Simmons was drafted than the Mavericks currently do with Flagg on board. Simmons also missed his first season in the league, giving him a full calendar year of development that Flagg hasn’t had. 2017: Markelle Fultz 43% FG, 23% 3P, 8.2/3.1/3.4, 1 steal, .2 blocks Totals: 337 points, 129 rebounds, 138 assists, 39 steals, 9 blocks, 8 3PM Fultz didn’t get to his 41st career game until his third season in the league. Even with the extra time in the NBA to develop, his stats are unremarkable. Due to all his injuries and issues with his jump shot, it’s impressive that Fultz has been able to carve out a career that’s going on eight years in the league. It speaks to his mental toughness, despite whatever went wrong early on his career. Needless to…