You're viewing the US edition You can switch to the UK or AU homepage at any time using this menu. Terrified electrician trapped for hours inside 130-foot high gas station sign recorded goodbye message to wife and kids: 'It was my final words' Was Meghan's supposed role in Guy Ritchie Netflix series leaked as a PR tactic? Watch: Trump's brutal killing of two suspected drug smugglers as chilling details of their US'INVASION' are exposed Lindsay Clancy's supporters are all suddenly acting strange. I'm afraid of what they might do next... something very sick is going on: KENNEDY Husband of slain therapist finally breaks silence on killing that rocked their ritzy horse country town... as fear and rumors run wild over mystery of powerful pharma exec's daughter who was knifed to death Trump's Treasury chief to launch massive financial warfare against Iran as US seeks to cripple'tyrannical' regime Sandra Bullock's devastating SIX YEARS in hiding: Insiders reveal actress was so'lonely and scared' that her'personality changed'... and how ALS death of partner Bryan at 57 nearly ruined her Horrified tourist finds note on her restaurant receipt saying'burn the eggs and spit on them' after she asked for them not to be runny Why a nose job isn't guaranteed to fix your face: From Wayne Rooney to Bella Hadid, a plastic surgeon reveals the celebrities who look better after the procedure - and who shouldn't have gone under the knife Kate shows off new lighter locks and sun-kissed glow after summer holidays - as she enjoys Balmoral break ahead of Harry and Meghan's return Dr. Oz addresses concerns about MMR vaccine after Trump calls shot'lethal' Tom Cruise's sex life crisis: Insiders reveal actor's secret'frustration' at 64... real reason Ana de Armas fling collapsed... lover's name he can't bear to hear... and Victoria Beckham's failed interventions Meghan is being cruelly used: LIZ JONES's warning to the Duchess as her film offer is abruptly'withdrawn' Two police officers fired and arrested for'egregious criminal conduct' during violent car stop in Arizona Shelley Fabares dead at 82: The Donna Reed Show actress who starred alongside Elvis Presley remembered by'heartbroken' family I've watched the full Perez Hilton knife video. The truth about this tragedy is beyond horrific but somehow it's just got worse: ROB SHUTER Yellowstone creator Taylor Sheridan hit with shock lawsuit claiming he STOLE idea for show starring Kevin Costner... days after highly anticipated spin-off was scrapped The Hollywood face never changes!

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. Just 10 weeks after conception, most fetuses move their right arm more than their left. 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 . Are you right-handed or left-handed? The answer for most of us is right. That's because nearly 90 percent of all people are right-dominant.

Children and people with mental health disorders are especially vulnerable.

Why are most people right-handed? A mix of biology, environment, and evolution helps explain our rightie-dominated world. Around 85 to 90 percent of people are right-handed. Breakthroughs, discoveries, and DIY tips sent every weekday. Roughly 85 to 90 percent of people are right-handed, while just 10 to 15 percent are left-handed, and a small percentage are ambidextrous.

Brain morphology is shaped by genetic and mechanical factors and is linked to biological development and diseases. Its fractal-like features, regional anisotropy, and complex curvature distributions hinder quantitative insights in medical inspections. Recognizing that the underlying elastic instability and bifurcation share the same physics as simple geometries such as spheres and ellipses, we developed a physics-transfer learning framework to address the geometrical complexity. To overcome the challenge of data scarcity, we constructed a digital library of high-fidelity continuum mechanics modeling that both describes and predicts the developmental processes of brain growth and disease. The physics of nonlinear elasticity from simple geometries is embedded into a neural network and applied to brain models. This physics-transfer approach demonstrates remarkable performance in feature characterization and morphogenesis prediction, highlighting the pivotal role of localized deformation in dominating over the background geometry. The data-driven framework also provides a library of reduced-dimensional evolutionary representations that capture the essential physics of the highly folded cerebral cortex. Validation through medical images and domain expertise underscores the deployment of digital-twin technology in comprehending the morphological complexity of the brain.

The human brain is one of the most complex objects in the universe – and that complexity may be due to a surge of hormones released by the placenta during pregnancy. While numerous ideas have been proposed to explain human brain evolution, it remains one of our greatest scientific mysteries. One explanation, known as the social brain hypothesis, suggests that our large brains evolved to manage complex social relationships. It posits that navigating large group dynamics requires a certain degree of cognitive ability, pushing social species to develop bigger brains. For instance, other highly sociable animals, such as dolphins and elephants, have relatively large brains too.

Imagine for a moment you are a child in 1941, sitting the common entrance exam for public schools with nothing but a pencil and paper. You read the following: "Write, for no more than a quarter of an hour, about a British author." Today, most of us wouldn't need 15 minutes to ponder such a question. We'd get the answer instantly by turning to AI tools such as Google Gemini, ChatGPT or Siri. Offloading cognitive effort to artificial intelligence has become second nature, but with mounting evidence that human intelligence is declining, some experts fear this impulse is driving the trend.

Diffusion MRI (dMRI) provides unique insights into fetal brain microstructure in utero. Longitudinal and cross-sectional fetal dMRI studies can reveal crucial neurodevelopmental changes but require precise spatial alignment across scans and subjects. This is challenging due to low data quality, rapid brain development, and limited anatomical landmarks. Existing registration methods, designed for high-quality adult data, struggle with these complexities. To address this, we introduce FetDTIAlign, a deep learning approach for fetal brain dMRI registration, enabling accurate affine and deformable alignment. FetDTIAlign features a dual-encoder architecture and iterative feature-based inference, reducing the impact of noise and low resolution. It optimizes network configurations and domain-specific features at each registration stage, enhancing both robustness and accuracy. We validated FetDTIAlign on data from 23 to 36 weeks gestation, covering 60 white matter tracts. It consistently outperformed two classical optimization-based methods and a deep learning pipeline, achieving superior anatomical correspondence. Further validation on external data from the Developing Human Connectome Project confirmed its generalizability across acquisition protocols. Our results demonstrate the feasibility of deep learning for fetal brain dMRI registration, providing a more accurate and reliable alternative to classical techniques. By enabling precise cross-subject and tract-specific analyses, FetDTIAlign supports new discoveries in early brain development.

Sleep plays a crucial role in neonatal development. Monitoring the sleep patterns in neonates in a Neonatal Intensive Care Unit (NICU) is imperative for understanding the maturation process. While polysomnography (PSG) is considered the best practice for sleep classification, its expense and reliance on human annotation pose challenges. Existing research often relies on multichannel EEG signals; however, concerns arise regarding the vulnerability of neonates and the potential impact on their sleep quality. This paper introduces a novel approach to neonatal sleep stage classification using a single-channel gradient boosting algorithm with Hjorth features. The gradient boosting parameters are fine-tuned using random search cross-validation (randomsearchCV), achieving an accuracy of 82.35% for neonatal sleep-wake classification. Validation is conducted through 5-fold cross-validation. The proposed algorithm not only enhances existing neonatal sleep algorithms but also opens avenues for broader applications.

Recent advances have enabled the study of human brain development using brain organoids derived from stem cells. Quantifying cellular processes like mitosis in these organoids offers insights into neurodevelopmental disorders, but the manual analysis is time-consuming, and existing datasets lack specific details for brain organoid studies. We introduce BOrg, a dataset designed to study mitotic events in the embryonic development of the brain using confocal microscopy images of brain organoids. BOrg utilizes an efficient annotation pipeline with sparse point annotations and techniques that minimize expert effort, overcoming limitations of standard deep learning approaches on sparse data. We adapt and benchmark state-of-the-art object detection and cell counting models on BOrg for detecting and analyzing mitotic cells across prophase, metaphase, anaphase, and telophase stages. Our results demonstrate these adapted models significantly improve mitosis analysis efficiency and accuracy for brain organoid research compared to existing methods. BOrg facilitates the development of automated tools to quantify statistics like mitosis rates, aiding mechanistic studies of neurodevelopmental processes and disorders. Data and code are available at https://github.com/awaisrauf/borg.