Pirates vs. Diamondbacks betting preview targets the under as both offenses go cold in series Former LSU coach Brian Kelly uses AI to prepare for job interviews, proving he's just like the rest of us Newsom office source responds to planned protest against trans athlete at state playoff girls' track meet Framber Valdez gets what he deserves for punk move, suspended six games after drilling Boston's Trevor Story MLB's new automated strike zone has a hidden feature helping umpires become more accurate than ever FIFA's World Cup ticket defense falls apart when compared to college football and NFL playoff prices WHO doesn't expect large Hantavirus outbreak US blockade keeps stranglehold on Iran's economy Pratt issues SHOCKING WARNING to socialist opponent: 'Stabbed in the NECK!' 'Fox & Friends' explores wearable technology's role in health and wellness OutKick President Trump on $1,000 World Cup ticket prices: 'I wouldn't pay it either, to be honest' Trump told the New York Post he'wouldn't pay it either' despite wanting to attend the tournament With the 2026 World Cup right around the corner, excitement among soccer fans in the United States is reaching a fever pitch. The tournament, which is being held, at least partially, on U.S. soil for the first time in over three decades, will feature some of the best players in the world competing for global soccer supremacy. If you want to get out and see a match taking place in America this summer, though, you may want to think about taking out a second mortgage on the house, because these tickets are rather steep in price. The get-in price for the United States' opening-round game against Paraguay in Southern California on June 12 is around $1,000, and in this economy, a lot of the middle-class fans are feeling priced out. U.S. FIFA fans celebrate at a watch party in Washington, D.C.'s Dupont Circle.

President Trump on $1,000 World Cup ticket prices: 'I wouldn't pay it either, to be honest' Pirates vs. Diamondbacks betting preview targets the under as both offenses go cold in series Former LSU coach Brian Kelly uses AI to prepare for job interviews, proving he's just like the rest of us Newsom office source responds to planned protest against trans athlete at state playoff girls' track meet Framber Valdez gets what he deserves for punk move, suspended six games after drilling Boston's Trevor Story MLB's new automated strike zone has a hidden feature helping umpires become more accurate than ever FIFA's World Cup ticket defense falls apart when compared to college football and NFL playoff prices WHO doesn't expect large Hantavirus outbreak US blockade keeps stranglehold on Iran's economy Pratt issues SHOCKING WARNING to socialist opponent: 'Stabbed in the NECK!' 'Fox & Friends' explores wearable technology's role in health and wellness The four-time MVP was tendered a $15.5M deal but may push for more before signing Pittsburgh Steelers legend Jerome Bettis said he understands that players may be frustrated about Aaron Rodgers being unsigned, but the four-time MVP has deserved the benefit of the doubt. Aaron Rodgers is headed to Pittsburgh. He'll be visiting the city and, not coincidentally, the Steelers -- a source familiar with the free-agent quarterback's tentative plans confirmed on Thursday. The story, first reported by Pittsburgh's 93.7 The Fan, is that Rodgers will fly into town on Friday and expects to spend time with the club through the weekend. One small issue: The Steelers are not exactly sure this is happening, per a source.

Sherrone Moore accuser Paige Shiver speaks out in new interview: he'had complete control over me' Megan Rapinoe calls on traditional WNBA media to be replaced with those who'understand queer culture' The NFL Draft continues to be one of the worst'sporting events' of the year New Russini-Vrabel photos raise ESPN conflict questions but the network won't answer them ESPN's Mad Dog Russo melts down over'U-S-A' chants at the RBC Heritage A piece of the UFC White House event's setup is sitting in Pennsylvania Amish country Viral Ottawa Senators fan blamed for team's 0-2 playoff start banished to Taiwan'First Take' host acts disgusted when she has to cover Vrabel-Russini drama Gen Jack Keane: You can't believe anything Iran says until it executes Will Cain: Everything about Hasan Piker is'communism wrapped in a Che Guevara T-shirt' Trump: 'Can I finish my question, wise guy?' DHS attorney speaks out after UCLA protest chaos and claims he received'death threats' Trump: Why would I use a nuclear weapon? A Vegas Insider study combed through all 30 MLB teams' subreddits to find which fanbases swear the most online When you start thinking about which MLB teams' fanbases have the filthiest mouths, there's a good chance a few cities instantly jump to mind. But a new survey from Vegas Insider has found the most foul-mouthed fanbases in the MLB, and the top team might surprise you a little at first... and then it will make total sense. A new survey has found that Athletics fans are the most foul-mouthed in Major League Baseball. Technically, a franchise that played in Philadelphia at one point, but is now in Sacramento limbonow in Sacramento limbo ahead of a move to Vegas: the Athletics.

Edward Cabrera's strikeout prop is the play as struggling Phillies face surging Cubs today Nuggets vs Timberwolves Game 3 pick hinges on Jaden McDaniels calling out Denver's entire defense Charles Barkley was disgusted by Magic's highly questionable pregame handshake ChatGPT predicted the first round of the NFL Draft and here's what it said Curt Cignetti was so focused this offseason, he turned down all external requests: 'I'm 95% football' Former MLB owner claims'despicable' San Francisco Giants are the reason the A's left Oakland Longtime NASCAR crew chief tells wild story about one of the sport's biggest characters WNBA finally embraces Caitlin Clark's stardom with unprecedented national TV schedule Why are the Mets so bad? Flyers mascot Gritty pens letter to fans ahead of first playoff game... eight years after he debuted Hasan Piker justifies'social murder' of CEO Fox News celebrates'Bring Your Kids to Work Day' Trump says there's'no time frame' to secure Iran deal Iranian activist praises Trump's intervention after female protesters saved from execution Steve Hilton praised for'offering solutions' in CA gubernatorial debate Middle East tensions escalate over US blockade, Iran's actions Fernando Mendoza is expected to go No. 1 to the Raiders, but Glazer says the night has more in store Based on what Jay Glazer's hearing 2026 NFL Draft is about to heat up in a rather big way. What's this bombshell that's going to hit the Draft tonight at 8 p.m. ET from Pittsburgh? Are we about to get a major trade into a top spot? Will a team trade with the Arizona Cardinals to get into the No. 3 spot, which has been a loud rumor this week? I know something that's going on, I just can't say it yet because I was told you can't say it until kind of getting on the clock there, Glazer said on Wednesday during an episode of Wake Up Barstool on Fox Sports.

A recent line of work has established uncoupled learning dynamics such that, when employed by all players in a game, each player's regret after T repetitions grows polylogarithmically in T, an exponential improvement over the traditional guarantees within the no-regret framework. However, so far these results have only been limited to certain classes of games with structured strategy spaces--such as normal-form and extensive-form games. The question as to whether O(polylogT) regret bounds can be obtained for general convex and compact strategy sets--which occur in many fundamental models in economics and multiagent systems--while retaining efficient strategy updates is an importantquestion.

In the context of localization, however, there is no natural definition of classes. Therefore, images areartificially separated intopositive/negativeclasses with respect to the chosen anchor images, based on some geometric proximity measure.

Unlike most primary headaches, secondary headaches need specialized care and can have devastating consequences if not treated promptly. Clinical guidelines highlight several 'red flag' features, such as thunderclap onset, meningismus, papilledema, focal neurologic deficits, signs of temporal arteritis, systemic illness, and the 'worst headache of their life' presentation. Despite these guidelines, determining which patients require urgent evaluation remains challenging in primary care settings. Clinicians often work with limited time, incomplete information, and diverse symptom presentations, which can lead to under-recognition and inappropriate care. We present a large language model (LLM)-based multi-agent clinical decision support system built on an orchestrator-specialist architecture, designed to perform explicit and interpretable secondary headache diagnosis from free-text clinical vignettes. The multi-agent system decomposes diagnosis into seven domain-specialized agents, each producing a structured and evidence-grounded rationale, while a central orchestrator performs task decomposition and coordinates agent routing. We evaluated the multi-agent system using 90 expert-validated secondary headache cases and compared its performance with a single-LLM baseline across two prompting strategies: question-based prompting (QPrompt) and clinical practice guideline-based prompting (GPrompt). We tested five open-source LLMs (Qwen-30B, GPT-OSS-20B, Qwen-14B, Qwen-8B, and Llama-3.1-8B), and found that the orchestrated multi-agent system with GPrompt consistently achieved the highest F1 scores, with larger gains in smaller models. These findings demonstrate that structured multi-agent reasoning improves accuracy beyond prompt engineering alone and offers a transparent, clinically aligned approach for explainable decision support in secondary headache diagnosis.

Myofascial pain (MP) is a leading cause of chronic low back pain, yet its tissue-level drivers remain poorly defined and lack reliable image biomarkers. Existing studies focus predominantly on muscle while neglecting fascia, fat, and other soft tissues that play integral biomechanical roles. We developed an anatomically grounded explainable artificial intelligence (AI) framework, LAYER (Layer-wise Analysis for Yielding Explainable Relevance Tissue), that analyses six tissue layers in three-dimensional (3D) ultrasound and quantifies their contribution to MP prediction. By utilizing the largest multi-model 3D ultrasound cohort consisting of over 4,000 scans, LAYER reveals that non-muscle tissues contribute substantially to pain prediction. In B-mode imaging, the deep fascial membrane (DFM) showed the highest saliency (0.420), while in combined B-mode and shear-wave images, the collective saliency of non-muscle layers (0.316) nearly matches that of muscle (0.317), challenging the conventional muscle-centric paradigm in MP research and potentially affecting the therapy methods. LAYER establishes a quantitative, interpretable framework for linking layer-specific anatomy to pain physiology, uncovering new tissue targets and providing a generalizable approach for explainable analysis of soft-tissue imaging.

Storytelling is a powerful form of communication and may provide insights into factors contributing to gaps in healthcare outcomes. To determine whether Large Language Models (LLMs) can identify potential underlying factors and avenues for intervention, we performed topic-aware hierarchical summarization of narratives from African American (AA) storytellers. Fifty transcribed stories of AA experiences were used to identify topics in their experience using the Latent Dirichlet Allocation (LDA) technique. Stories about a given topic were summarized using an open-source LLM-based hierarchical summarization approach. Topic summaries were generated by summarizing across story summaries for each story that addressed a given topic. Generated topic summaries were rated for fabrication, accuracy, comprehensiveness, and usefulness by the GPT4 model, and the model's reliability was validated against the original story summaries by two domain experts. 26 topics were identified in the fifty AA stories. The GPT4 ratings suggest that topic summaries were free from fabrication, highly accurate, comprehensive, and useful. The reliability of GPT ratings compared to expert assessments showed moderate to high agreement. Our approach identified AA experience-relevant topics such as health behaviors, interactions with medical team members, caregiving and symptom management, among others. Such insights could help researchers identify potential factors and interventions by learning from unstructured narratives in an efficient manner-leveraging the communicative power of storytelling. The use of LDA and LLMs to identify and summarize the experience of AA individuals suggests a variety of possible avenues for health research and possible clinical improvements to support patients and caregivers, thereby ultimately improving health outcomes.

Objective: Fluoropyrimidines are widely prescribed for colorectal and breast cancers, but are associated with toxicities such as hand-foot syndrome and cardiotoxicity. Since toxicity documentation is often embedded in clinical notes, we aimed to develop and evaluate natural language processing (NLP) methods to extract treatment and toxicity information. Materials and Methods: We constructed a gold-standard dataset of 236 clinical notes from 204,165 adult oncology patients. Domain experts annotated categories related to treatment regimens and toxicities. We developed rule-based, machine learning-based (Random Forest, Support Vector Machine [SVM], Logistic Regression [LR]), deep learning-based (BERT, ClinicalBERT), and large language models (LLM)-based NLP approaches (zero-shot and error-analysis prompting). Models used an 80:20 train-test split. Results: Sufficient data existed to train and evaluate 5 annotated categories. Error-analysis prompting achieved optimal precision, recall, and F1 scores (F1=1.000) for treatment and toxicities extraction, whereas zero-shot prompting reached F1=1.000 for treatment and F1=0.876 for toxicities extraction.LR and SVM ranked second for toxicities (F1=0.937). Deep learning underperformed, with BERT (F1=0.873 treatment; F1= 0.839 toxicities) and ClinicalBERT (F1=0.873 treatment; F1 = 0.886 toxicities). Rule-based methods served as our baseline with F1 scores of 0.857 in treatment and 0.858 in toxicities. Discussion: LMM-based approaches outperformed all others, followed by machine learning methods. Machine and deep learning approaches were limited by small training data and showed limited generalizability, particularly for rare categories. Conclusion: LLM-based NLP most effectively extracted fluoropyrimidine treatment and toxicity information from clinical notes, and has strong potential to support oncology research and pharmacovigilance.