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Amyotrophic lateral sclerosis (ALS) is a severe disease with a typical survival of 3-5 years after symptom onset. Current treatments offer only limited life extension, and the variability in patient responses highlights the need for personalized care. However, research is hindered by small, heterogeneous cohorts, sparse longitudinal data, and the lack of a clear definition for clinically meaningful patient clusters. Existing clustering methods remain limited in both scope and number. To address this, we propose a clustering approach that groups sequences using a disease progression declarative score. Our approach integrates medical expertise through multiple descriptive variables, investigating several distance measures combining such variables, both by reusing off-the-shelf distances and employing a weak-supervised learning method. We pair these distances with clustering methods and benchmark them against state-of-the-art techniques. The evaluation of our approach on a dataset of 353 ALS patients from the University Hospital of Tours, shows that our method outperforms state-of-the-art methods in survival analysis while achieving comparable silhouette scores. In addition, the learned distances enhance the relevance and interpretability of results for medical experts.

British patients are set to receive Elon Musk's Neuralink brain chips as part of the first UK clinical trial. Neuralink is partnering with University College London Hospitals Trust and Newcastle Hospitals for the project, the company said in an announcement. Seven participants who cannot walk will be fitted with an implant about the size of a 10p coin, allowing them to control a smartphone with their mind. Those living with paralysis due to conditions such as spinal cord injuries and a nervous system disease called amyotrophic lateral sclerosis qualify for the study, the company revealed in a post on X. This comes after a paralysed woman in the US revealed the shocking effect the brain implant has already had on her life. Audrey Crews, who has been paralysed since she was 16, became one of five people in the US who have already been implanted with the brain chip.

--Conventional augmentative and alternative communication (AAC) systems and language-learning platforms often fail to adapt in real time to the user's cognitive and linguistic needs, especially in neurological conditions such as post-stroke aphasia or amyotrophic lateral sclerosis. Recent advances in noninvasive electroencephalography (EEG)-based brain-computer interfaces (BCIs) and transformer-based large language models (LLMs) offer complementary strengths: BCIs capture users' neural intent with low fatigue, while LLMs generate contextually tailored language content. Objective: We propose and evaluate a novel hybrid framework that leverages real-time EEG signals to drive an LLM-powered language rehabilitation assistant. This system aims to: (1) enable users with severe speech or motor impairments to navigate language-learning modules via mental commands; (2) dynamically personalize vocabulary, sentence-construction exercises, and corrective feedback; and (3) monitor neural markers of cognitive effort to adjust task difficulty on the fly. All individuals have the right to self-expression, social participation, and the agency to impact their environment. For individuals with complex communication needs, augmentative and alternative communication (AAC) systems provide critical tools to facilitate communication. However, traditional AAC methods--such as printed communication boards or eye gaze devices--may not be accessible for individuals with severe speech and physical impairments (SSPI).

Amyotrophic Lateral Sclerosis (ALS) is a rare neurodegenerative disease, and high-quality EEG data from ALS patients are scarce. This data scarcity, coupled with severe class imbalance between ALS and healthy control recordings, poses a challenge for training reliable machine learning classifiers. In this work, we address these issues by generating synthetic EEG signals for ALS patients using a Conditional Wasserstein Generative Adversarial Network (CWGAN). We train CWGAN on a private EEG dataset (ALS vs. non-ALS) to learn the distribution of ALS EEG signals and produce realistic synthetic samples. We preprocess and normalize EEG recordings, and train a CWGAN model to generate synthetic ALS signals. The CWGAN architecture and training routine are detailed, with key hyperparameters chosen for stable training. Qualitative evaluation of generated signals shows that they closely mimic real ALS EEG patterns. The CWGAN training converged with generator and discriminator loss curves stabilizing, indicating successful learning. The synthetic EEG signals appear realistic and have potential use as augmented data for training classifiers, helping to mitigate class imbalance and improve ALS detection accuracy. We discuss how this approach can facilitate data sharing and enhance diagnostic models.

Amyotrophic lateral sclerosis (ALS) is a degenerative disorder of motor neurons that causes progressive paralysis in patients. Current treatment options aim to prolong survival and improve quality of life; however, due to the heterogeneity of the disease, it is often difficult to determine the optimal time for potential therapies or medical interventions. In this study, we propose a novel method to predict the time until a patient with ALS experiences significant functional impairment (ALSFRS-R<=2) with respect to five common functions: speaking, swallowing, handwriting, walking and breathing. We formulate this task as a multi-event survival problem and validate our approach in the PRO-ACT dataset by training five covariate-based survival models to estimate the probability of an event over a 500-day period after a baseline visit. We then predict five event-specific individual survival distributions (ISDs) for each patient, each providing an interpretable and meaningful estimate of when that event will likely take place in the future. The results show that covariate-based models are superior to the Kaplan-Meier estimator at predicting time-to-event outcomes. Additionally, our method enables practitioners to make individual counterfactual predictions, where certain features (covariates) can be changed to see their effect on the predicted outcome. In this regard, we find that Riluzole has little to no impact on predicted functional decline. However, for patients with bulbar-onset ALS, our method predicts considerably shorter counterfactual time-to-event estimates for tasks related to speech and swallowing compared to limb-onset ALS. The proposed method can be applied to current clinical examination data to assess the risk of functional decline and thus allow more personalized treatment planning.

Imagine losing your ability to speak or move, yet still having so much to say. For Brad G. Smith, this became his reality after being diagnosed with ALS, a rare and progressive disease that attacks the nerves controlling voluntary muscle movement. But thanks to a groundbreaking Neuralink brain implant, Smith is now able to communicate with the world using only his thoughts. Join The FREE CyberGuy Report: Get my expert tech tips, critical security alerts and exclusive deals -- plus instant access to my free Ultimate Scam Survival Guide when you sign up! Before receiving the Neuralink implant, Smith relied on eye-tracking technology to communicate.

-- Amyotrophic lateral sclerosis (ALS) is a fatal neu-rodegenerative disorder characterized by motor neuron degeneration, with alterations in neural excitability serving as key indicators. Recent advancements in induced pluripotent stem cell (iPSC) technology have enabled the generation of human iPSC-derived neuronal cultures, which, when combined with multi-electrode array (MEA) electrophysiology, provide rich spatial and temporal electrophysiological data. Traditionally, MEA data is analyzed using handcrafted features based on potentially imperfect domain knowledge, which while useful may not fully capture all the useful characteristics inherent in the MEA data. Machine learning, in particular deep learning has the potential to automatically learn relevant characteristics (features) from raw data, without solely relying on handcrafted feature extraction. However, handcrafted features remain critical for encoding domain knowledge and improving model interpretability, especially in scenarios with limited or noisy data, as is often the case in most experimental studies. This study introduces FRAME-C, a knowledge-augmented machine learning pipeline that combines domain knowledge, raw spike waveform data, and deep learning techniques to classify MEA signals and identify ALS-specific phenotypes. FRAME-C leverages deep learning to learn important features from spike waveforms, while also incorporating handcrafted features such as spike amplitude, inter-spike interval, and spike duration, thus preserving key spatial and temporal information. We validate FRAME-C on both simulated and real-world MEA data from human iPSC-derived neuronal cultures, demonstrating its superior performance compared to existing methods for MEA classification. FRAME-C performs significantly better, showing more than a 11% improvement on real-world data and up to 25% improvement on simulated data in terms of the test accuracy. Moreover, we show that FRAME-C can be used to evaluate the importance of each of the handcrafted features, and thereby contributing to the interpretation of the classification results. Permutation feature importances are calculated for these handcrafted features, providing further insights into the phenotypes of ALS. Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease that leads to a progressive loss of motor neurons. At the onset of ALS, symptoms may include limb weakness and difficulty in swallowing. However, the disease invariably progresses towards paralysis and respiratory failure within three to five years [1]. A small portion of ALS patients (5 - 10%) are familial (fALS) in nature and can be linked to a family history of ALS. However, the majority (90 - 95%) are sporadic (sALS) and do not have any known family history.

Understanding cell identity and function through single-cell level sequencing data remains a key challenge in computational biology. We present a novel framework that leverages gene-specific textual annotations from the NCBI Gene database to generate biologically contextualized cell embeddings. For each cell in a single-cell RNA sequencing (scRNA-seq) dataset, we rank genes by expression level, retrieve their NCBI Gene descriptions, and transform these descriptions into vector embedding representations using large language models (LLMs). The models used include OpenAI text-embedding-ada-002, text-embedding-3-small, and text-embedding-3-large (Jan 2024), as well as domain-specific models BioBERT and SciBERT. Embeddings are computed via an expression-weighted average across the top N most highly expressed genes in each cell, providing a compact, semantically rich representation. This multimodal strategy bridges structured biological data with state-of-the-art language modeling, enabling more interpretable downstream applications such as cell-type clustering, cell vulnerability dissection, and trajectory inference.

Individuals with upper limb movement limitations face challenges in interacting with others. Although robotic arms are currently used primarily for functional tasks, there is considerable potential to explore ways to enhance users' body language capabilities during social interactions. This paper introduces an Augmented Body Communicator system that integrates robotic arms and a large language model. Through the incorporation of kinetic memory, disabled users and their supporters can collaboratively design actions for the robot arm. The LLM system then provides suggestions on the most suitable action based on contextual cues during interactions. The system underwent thorough user testing with six participants who have conditions affecting upper limb mobility. Results indicate that the system improves users' ability to express themselves. Based on our findings, we offer recommendations for developing robotic arms that support disabled individuals with body language capabilities and functional tasks.