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The American revolutionaries who popularized science in the early United States
Benjamin Franklin and other citizen scientists are core parts of the American experiment. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Benjamin Franklin's kite experiment in 1752 was a pivotal scientific event, which demonstrated the connection between lightning and electricity. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .
Eagles Super Bowl champion accused of kicking out pregnant ex-girlfriend, seeking women on dating app
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.
5 new quarters commemorate 250 years of American independence
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."
PRIMRose: Insights into the Per-Residue Energy Metrics of Proteins with Double InDel Mutations using Deep Learning
Brown, Stella, Preisig, Nicolas, Davis, Autumn, Hutchinson, Brian, Jagodzinski, Filip
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.
Developing Fairness-Aware Task Decomposition to Improve Equity in Post-Spinal Fusion Complication Prediction
Yuan, Yining, Tamo, J. Ben, Shi, Wenqi, Zhong, Yishan, Nnamdi, Micky C., Brenn, B. Randall, Hwang, Steven W., Wang, May D.
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.