LIV Golf CEO teases announcements in'next 10 days,' talks Bryson DeChambeau's future with LIV Double standard: It's only a controversy when Caitlin Clark walks out with Morgan Wallen Reitan wins after nearly quitting for YouTube, Snedeker's well-earned cry, and early PGA Championship hype Bubba Wallace has tense pit road confrontation, NASCAR fans rip female driver & Sam Busch goes cowgirl! WWE legend The Rock roasts Draymond Green, says he has the'laziest' Black name Mike Vrabel's relationship with Dianna Russini turns Patriots' Mother's Day post into a trolling frenzy Nathan Sales discusses Trump's Iran ceasefire stance amid stalled negotiations Trump's China summit: Families plead for imprisoned Americans Spencer Pratt's'non-partisan' messaging is'resonating' with everyday Americans, CA GOP delegate says Trace Gallagher: Forget about finding the truth, these people can't find the pudding The only language Iran understands is'pain,' Israeli special ops veteran says Kevin O'Leary: Mamdani is the best real estate agent for Miami Beach Jack Carr reveals why he chose Chris Pratt to star in his famous action series'The Terminal List' Democrats are trying to'lie' and'deceive' young people: Sen Ted Cruz Fred Johnson fired off a cryptic Instagram message but didn't directly respond to the allegations Fox News Flash top sports headlines are here. Check out what's clicking on FoxNews.com. Philadelphia Eagles offensive lineman Fred Johnson faced damning allegations from a pregnant ex-girlfriend. Alyssa Okada said in a video posted to her TikTok account on Friday that Johnson kicked her out of their home to pursue other women on a dating app while she was eight months pregnant.

Statistical, 36(2):111-147, R. Sukkar hidden 2012 Medicine, pages Z. Sun, progression data.

We show that the matrix perspective function, which is jointly convex in the Cartesian product of a standard Euclidean vector space and a conformal space of symmetric matrices, has a proximity operator in an almost closed form.

The new designs honor the Constitution, Civil War, and more. Breakthroughs, discoveries, and DIY tips sent every weekday. While we've said goodbye to both the year 2025 and the penny, five new United States quarters will be finding their way into your pocket soon enough. The designs of each new quarter will honor the country's 250th anniversary (aka its semiquincentennial). According to a press release from the U.S. Mint, the coins "commemorate 250 years of American Liberty by reflecting our country's founding principles and honoring our Nation's history."

Understanding how protein mutations affect protein structure is essential for advancements in computational biology and bioinformatics. We introduce PRIMRose, a novel approach that predicts energy values for each residue given a mutated protein sequence. Unlike previous models that assess global energy shifts, our method analyzes the localized energetic impact of double amino acid insertions or deletions (InDels) at the individual residue level, enabling residue-specific insights into structural and functional disruption. We implement a Convolutional Neural Network architecture to predict the energy changes of each residue in a protein mutation. We train our model on datasets constructed from nine proteins, grouped into three categories: one set with exhaustive double InDel mutations, another with approximately 145k randomly sampled double InDel mutations, and a third with approximately 80k randomly sampled double InDel mutations. Our model achieves high predictive accuracy across a range of energy metrics as calculated by the Rosetta molecular modeling suite and reveals localized patterns that influence model performance, such as solvent accessibility and secondary structure context. This per-residue analysis offers new insights into the mutational tolerance of specific regions within proteins and provides higher interpretable and biologically meaningful predictions of InDels' effects.

Fairness in clinical prediction models remains a persistent challenge, particularly in high-stakes applications such as spinal fusion surgery for scoliosis, where patient outcomes exhibit substantial heterogeneity. Many existing fairness approaches rely on coarse demographic adjustments or post-hoc corrections, which fail to capture the latent structure of clinical populations and may unintentionally reinforce bias. We propose FAIR-MTL, a fairness-aware multitask learning framework designed to provide equitable and fine-grained prediction of postoperative complication severity. Instead of relying on explicit sensitive attributes during model training, FAIR-MTL employs a data-driven subgroup inference mechanism. We extract a compact demographic embedding, and apply k-means clustering to uncover latent patient subgroups that may be differentially affected by traditional models. These inferred subgroup labels determine task routing within a shared multitask architecture. During training, subgroup imbalance is mitigated through inverse-frequency weighting, and regularization prevents overfitting to smaller groups. Applied to postoperative complication prediction with four severity levels, FAIR-MTL achieves an AUC of 0.86 and an accuracy of 75%, outperforming single-task baselines while substantially reducing bias. For gender, the demographic parity difference decreases to 0.055 and equalized odds to 0.094; for age, these values reduce to 0.056 and 0.148, respectively. Model interpretability is ensured through SHAP and Gini importance analyses, which consistently highlight clinically meaningful predictors such as hemoglobin, hematocrit, and patient weight. Our findings show that incorporating unsupervised subgroup discovery into a multitask framework enables more equitable, interpretable, and clinically actionable predictions for surgical risk stratification.

Is "Six Seven" Really Brain Rot? The viral phrase is easy to dismiss, but its ubiquity suggests something crucial about human nature. Recently, my wife was texting with a friend who lives in Singapore. The news from the other side of the world turned out to be that kids there had discovered "six seven." On Halloween, our friend reported, a boy with a handmade "six seven" jersey had earned applause as he made his way through her neighborhood--a place that's a long way from Sixty-seventh Street in Philadelphia, which the rapper Skrilla may have been referencing in his song "Doot Doot (6 7)," which came out last December.

Philly's'transit vigilante' created a real-time bus tracker for his neighbors With a sports timer and some clever coding, Max Goldberg built a DIY display that tells South Philly commuters exactly when their next bus will arrive. Breakthroughs, discoveries, and DIY tips sent every weekday. Philadelphia's mass transit system has had a rough go of it lately. The Pennsylvania city's main public transit provider, SEPTA, has been dealing with massive service cuts, including the elimination of entire bus routes. But South Philly resident Max Goldberg is undeterred.

Early and accurate diagnosis of Alzheimer's disease (AD), a complex neurodegenerative disorder, requires analysis of heterogeneous biomarkers (e.g., neuroimaging, genetic risk factors, cognitive tests, and cerebrospinal fluid proteins) typically represented in a tabular format. With flexible few-shot reasoning, multimodal integration, and natural-language-based interpretability, large language models (LLMs) offer unprecedented opportunities for prediction with structured biomedical data. We propose a novel framework called TAP-GPT, Tabular Alzheimer's Prediction GPT, that adapts TableGPT2, a multimodal tabular-specialized LLM originally developed for business intelligence tasks, for AD diagnosis using structured biomarker data with small sample sizes. Our approach constructs few-shot tabular prompts using in-context learning examples from structured biomedical data and finetunes TableGPT2 using the parameter-efficient qLoRA adaption for a clinical binary classification task of AD or cognitively normal (CN). The TAP-GPT framework harnesses the powerful tabular understanding ability of TableGPT2 and the encoded prior knowledge of LLMs to outperform more advanced general-purpose LLMs and a tabular foundation model (TFM) developed for prediction tasks. To our knowledge, this is the first application of LLMs to the prediction task using tabular biomarker data, paving the way for future LLM-driven multi-agent frameworks in biomedical informatics.

Coin collectors, go dust off those running shoes. Breakthroughs, discoveries, and DIY tips sent every weekday. For Americans wishing they could participate in The Great Canadian Treasure Hunt, there is a new opportunity stateside. This month, the rare coin dealer and auction house Stack's Bowers Galleries is inviting the public to join in on a treasure hunt to celebrate the 90th anniversary of the firm's first auction. Certificates for rare coins and banknotes will be hidden in New York City, Boston, Philadelphia, and Miami, all cities where the auction house has retail store fronts.