RFK Jr taunts Donald Trump as he shares pointed'Thanksgiving dinner' photo with the president, Elon Musk and Don Jr Fans hail Cece Winans' 'best ever' rendition of the national anthem on Thanksgiving and beg the NFL to get her to the Super Bowl I've seen it too many times - I have to speak up: KENNEDY Trump plunged into security scandal over Afghan shooter's asylum - after president blamed Biden Bryan Kohberger becomes nightmare prison diva... as he throws huge tantrum over BANANAS behind bars My wife was blindsided when I asked for a divorce. There was no foul play or'other woman' but this is why I did it... and the six subtle signs your partner is planning on leaving you too: RICHARD WARNER My book on the Kennedys was used as a'mistress manual' by Olivia Nuzzi... then this wannabe Carolyn Bessette had the nerve to hound me with these outrageous texts: MAUREEN CALLAHAN Americans are finally realizing why we don't eat turkey eggs Plastic surgeon reveals secrets of Tom Brady's changing face, including'unnatural' procedure... and truth about Ozempic use Lilibet's locks steal the show! Meghan's daughter is every inch the little Princess with her fiery red locks in a neat plait at Thanksgiving outing Kimberly Guilfoyle leaves little to the imagination in a figure-hugging sheer lace gown for Thanksgiving dinner in Athens in her role as US Ambassador - after admitting she's'husband hunting' Hollywood stars who REFUSE to celebrate Thanksgiving over animal cruelty and its'blood-soaked' history Californians were shaken by multiple earthquakes on Thanksgiving morning, raising concerns in the seismically active region. At least 13 tremors, starting around 4:30am PT (7:30am ET) and ranging from magnitude 1.0 to 3.7, were reported near The Geysers geothermal field in Northern California . The last earthquake, a small 1.1 magnitude, was detected at 5:47am PT (8:47am ET).

Leaked recording reveals Campbell's exec's sickening remarks about iconic soup's ingredients How Lauren Sanchez would REALLY look if she'd never had rumored plastic surgery Trump's losing control... MAGA's imploding... and White House insiders tell me why they're REALLY worried: ANDREW NEIL Billionaire family posts VERY unusual obituary after heir, 40, met violent end at $2.8m hunting lodge following marriage scandal These women have lost as much as nine stone WITHOUT jabs: Now they reveal secret to their stunning success, the extraordinary event that brought them together and how it's changed their lives... Judge throws out Comey and James cases as Trump's beauty queen prosecutor is humiliated Her moving videos about the handsome boyfriend who ghosted her went viral and catapulted her to overnight fame. Kate Gosselin's ex Jon is seen at his splashy wedding for the first time as son Collin weighs in on his siblings not attending Fugitive'Slender Man' stabber Morgan Geyser snapped'just Google me' when asked for ID by cops who found her with MUCH older lover It all seems to be falling apart now! Pete Hegseth drops hammer on Democrat senator in'sedition' storm as court martial looms after Trump's execution threat Sabrina Carpenter looks unrecognisable in throwback snap from seven years ago as fans call her rebranding'wild' Neuralink's'Patient 4' feared missing months after getting revolutionary brain chip... now his wife tells the REAL heartbreaking story NFL's first transgender cheerleader makes explosive allegation against Carolina Panthers Slash your cholesterol by a third in just a month... hundreds of thousands are on a new diet that's transforming lives. California was shaken early Monday as a series of earthquakes struck in quick succession, raising concern in the seismically active region. At least seven tremors have been reported, ranging in magnitude from 1.1 to 4.1, with the epicenter near The Geysers.

Scientists reveal what triggered Santorini'earthquake swarm' The swarm of tens of thousands of earthquakes near the Greek island of Santorini earlier this year was triggered by molten rock pumping through an underground channel over three months, scientists have discovered. They used physics and artificial intelligence to work out exactly what caused the more than 25,000 earthquakes, which travelled about 20km (12 miles) horizontally through the Earth's crust. They used each of the tremors as virtual sensors, then used artificial intelligence to analyse patterns associated with them. One of the lead researchers, Dr Stephen Hicks from UCL, said combining physics and machine learning in this way could help forecast volcanic eruptions. The seismic activity started to stir beneath the Greek islands of Santorini, Amorgos, and Anafi in January 2025.

Scientists have discovered an ominous sign which could hint that Yellowstone's supervolcano is building up to an eruption. Using machine learning, researchers found there have been over 86,000 hidden earthquakes between 2008 and 2022. That is 10 times more tremors than scientists had previously detected. Worryingly, more than half of those earthquakes came in swarms - small groups of interconnected tremors - which have been known to precede volcanic activity. The researchers say these'chaotic' swarms were found moving along rough, young fault lines running deep below the Yellowstone Caldera. These clusters of seismic activity are likely caused by hot, mineral-rich water forcing itself through cracks in the rock.

Journaling plays a crucial role in managing chronic conditions by allowing patients to document symptoms and medication intake, providing essential data for long-term care. While valuable, traditional journaling methods often rely on static, self-directed entries, lacking interactive feedback and real-time guidance. This gap can result in incomplete or imprecise information, limiting its usefulness for effective treatment. To address this gap, we introduce PATRIKA, an AI-enabled prototype designed specifically for people with Parkinson's disease (PwPD). The system incorporates cooperative conversation principles, clinical interview simulations, and personalization to create a more effective and user-friendly journaling experience. Through two user studies with PwPD and iterative refinement of PATRIKA, we demonstrate conversational journaling's significant potential in patient engagement and collecting clinically valuable information. Our results showed that generating probing questions PATRIKA turned journaling into a bi-directional interaction. Additionally, we offer insights for designing journaling systems for healthcare and future directions for promoting sustained journaling.

Parkinson's disease (PD) is a neurological disorder requiring early and accurate diagnosis for effective management. Machine learning (ML) has emerged as a powerful tool to enhance PD classification and diagnostic accuracy, particularly by leveraging wearable sensor data. This survey comprehensively reviews current ML methodologies used in classifying Parkinsonian tremors, evaluating various tremor data acquisition methodologies, signal preprocessing techniques, and feature selection methods across time and frequency domains, highlighting practical approaches for tremor classification. The survey explores ML models utilized in existing studies, ranging from traditional methods such as Support Vector Machines (SVM) and Random Forests to advanced deep learning architectures like Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM). We assess the efficacy of these models in classifying tremor patterns associated with PD, considering their strengths and limitations. Furthermore, we discuss challenges and discrepancies in current research and broader challenges in applying ML to PD diagnosis using wearable sensor data. We also outline future research directions to advance ML applications in PD diagnostics, providing insights for researchers and practitioners.

Deep learning holds tremendous potential in healthcare for uncovering hidden patterns within extensive clinical datasets, aiding in the diagnosis of various diseases. Parkinson's disease (PD) is a neurodegenerative condition characterized by the deterioration of brain function. In the initial stages of PD, automatic diagnosis poses a challenge due to the similarity in behavior between individuals with PD and those who are healthy. Our objective is to propose an effective model that can aid in the early detection of Parkinson's disease. We employed the VGRF gait signal dataset sourced from Physionet for distinguishing between healthy individuals and those diagnosed with Parkinson's disease. This paper introduces a novel deep learning architecture based on the LSTM network for automatically detecting freezing of gait episodes in Parkinson's disease patients. In contrast to conventional machine learning algorithms, this method eliminates manual feature engineering and proficiently captures prolonged temporal dependencies in gait patterns, thereby improving the diagnosis of Parkinson's disease. The LSTM network resolves the issue of vanishing gradients by employing memory blocks in place of self-connected hidden units, allowing for optimal information assimilation. To prevent overfitting, dropout and L2 regularization techniques have been employed. Additionally, the stochastic gradient-based optimizer Adam is used for the optimization process. The results indicate that our proposed approach surpasses current state-of-the-art models in FOG episode detection, achieving an accuracy of 97.71%, sensitivity of 99%, precision of 98%, and specificity of 96%. This demonstrates its potential as a superior classification method for Parkinson's disease detection.

Accurate assessment of Parkinsonian tremor is vital for monitoring disease progression and evaluating treatment efficacy. We introduce a pixel-based deep learning model designed to analyse postural tremor in Parkinson's disease (PD) from video data, overcoming the limitations of traditional pose estimation techniques. Trained on 2,742 assessments from five specialised movement disorder centres across two continents, the model demonstrated robust concordance with clinical evaluations. It effectively predicted treatment effects for levodopa and deep brain stimulation (DBS), detected lateral asymmetry of symptoms, and differentiated between different tremor severities. Feature space analysis revealed a non-linear, structured distribution of tremor severity, with low-severity scores occupying a larger portion of the feature space. The model also effectively identified outlier videos, suggesting its potential for adaptive learning and quality control in clinical settings. Our approach offers a scalable and objective method for tremor scoring, with potential integration into other MDS-UPDRS motor assessments, including bradykinesia and gait. The system's adaptability and performance underscore its promise for high-frequency, longitudinal monitoring of PD symptoms, complementing clinical expertise and enhancing decision-making in patient management. Future work will extend this pixel-based methodology to other cardinal symptoms of PD, aiming to develop a comprehensive, multi-symptom model for automated Parkinson's disease severity assessment.

The advancement of Parkinson's Disease (PD) diagnosis through speech analysis is hindered by a notable lack of publicly available, diverse language datasets, limiting the reproducibility and further exploration of existing research. In response to this gap, we introduce a comprehensive corpus from 108 native Castilian Spanish speakers, comprising 55 healthy controls and 53 individuals diagnosed with PD, all of whom were under pharmacological treatment and recorded in their medication-optimized state. This unique dataset features a wide array of speech tasks, including sustained phonation of the five Spanish vowels, diadochokinetic tests, 16 listen-and-repeat utterances, and free monologues. The dataset emphasizes accuracy and reliability through specialist manual transcriptions of the listen-and-repeat tasks and utilizes Whisper for automated monologue transcriptions, making it the most complete public corpus of Parkinsonian speech, and the first in Castillian Spanish. NeuroVoz is composed by 2,903 audio recordings averaging $26.88 \pm 3.35$ recordings per participant, offering a substantial resource for the scientific exploration of PD's impact on speech. This dataset has already underpinned several studies, achieving a benchmark accuracy of 89% in PD speech pattern identification, indicating marked speech alterations attributable to PD. Despite these advances, the broader challenge of conducting a language-agnostic, cross-corpora analysis of Parkinsonian speech patterns remains an open area for future research. This contribution not only fills a critical void in PD speech analysis resources but also sets a new standard for the global research community in leveraging speech as a diagnostic tool for neurodegenerative diseases.

Data-driven approaches for remote detection of Parkinson's Disease and its motor symptoms have proliferated in recent years, owing to the potential clinical benefits of early diagnosis. The holy grail of such approaches is the free-living scenario, in which data are collected continuously and unobtrusively during every day life. However, obtaining fine-grained ground-truth and remaining unobtrusive is a contradiction and therefore, the problem is usually addressed via multiple-instance learning. Yet for large scale studies, obtaining even the necessary coarse ground-truth is not trivial, as a complete neurological evaluation is required. In contrast, large scale collection of data without any ground-truth is much easier. Nevertheless, utilizing unlabelled data in a multiple-instance setting is not straightforward, as the topic has received very little research attention. Here we try to fill this gap by introducing a new method for combining semi-supervised with multiple-instance learning. Our approach builds on the Virtual Adversarial Training principle, a state-of-the-art approach for regular semi-supervised learning, which we adapt and modify appropriately for the multiple-instance setting. We first establish the validity of the proposed approach through proof-of-concept experiments on synthetic problems generated from two well-known benchmark datasets. We then move on to the actual task of detecting PD tremor from hand acceleration signals collected in-the-wild, but in the presence of additional completely unlabelled data. We show that by leveraging the unlabelled data of 454 subjects we can achieve large performance gains (up to 9% increase in F1-score) in per-subject tremor detection for a cohort of 45 subjects with known tremor ground-truth.