AI News Archive: May 8, 2026 — Part 17
Sourced from 500+ daily AI sources, scored by relevance.
- ProspectSonar
Find prospects in 3 minutes, not 3 weeks.
- Clearize
Image Enhancer for Real Estate.
- VoxLit
Transform Text into Natural Voice
- AiOverview.com
Track your brand's visibility across the LLM's
- Polymarket Trading Bot
elf-hosted Polymarket bots. Buy once, keep your keys.
- FluxSerp
AI Search Visibility & SEO Content on Autopilot
- aimockinterviews
Master the Interview - Prepare. Practice. Excel
- AI Watermark Remover
Remove Gemini, Doubao & Jimeng watermarks in your browser
- Instaknow
AI study companion that helps students focus and learn
- Ninar AI — AI Visibility Intelligence
Your Brand's Voice Inside Every AI Engine
- Linktrika
AI agents for SEO automation and outreach
- ReditX AI
Create AI images & cinematic videos instantly
- ai image editor free
https://aiimageeditorfree.com
- Openfish
Agent-native prediction markets
- Comniqai
Answers. Leads. Sales.
- Pawsitive AI Learning
Master ChatGPT, Claude 40+ AI tools with gamified lessons.
- XIS (Execution Intelligence system)
Program governance AI software
- GPT Image 2
GPT Image 2 prompts, guides, and image workflows
- Qav
AI analytics that shows you where your business is winning
- StayTrue with Kyaro
AI reflection chatbot for teenagers with safety boundaries.
- Announcing HonestFit.ai
Resumes make claims. HonestFit helps show what supports them
- Dream Ledger
AI-powered dream interpretation on Solana — pay with SOL
- SalesPulse AI
Practice real sales calls. AI tells you what to say instead.
- FlowForce
Close more deals with an AI-powered CRM
- AIBuildz
Showcase for products built with AI.
- Vid2Prompt
Turn any video into actionable prompts and insights.
- Jason AI
AI-powered assistant for B2B sales outreach
- Linking live-cell behavior to transcriptional responses across perturbations using dynamic caging
Single-cell technologies, encompassing molecular, morphological, and functional assays, have emerged as cornerstones of modern biological research and discovery. However, current experimental methods often fail to explicitly link these 'omic' modalities, especially in live cells or longitudinally through time, impeding the study of multi-scale interactions and mechanisms of regulation. CellCage Enclosure (CCE) technology overcomes these limitations by dynamically compartmentalizing cells, allowing for scalable, live-cell, longitudinal exploration and simultaneous analysis of transcriptomic, proteomic, and morphological profiles. Using this novel technology, we generate previously inaccessible insights across various in vitro cellular systems under a diverse set of perturbations, including the discovery of morphological and proteomic features linked to immune suppressive gene set expression in human primary regulatory T cells (Tregs), as well as direct association of morphological and p
- STARMAP: A 3D-informed framework for mapping functional regions in proteins to regulatory and cellular phenotypes
Artificial Intelligence (AI) has transformed biology by revealing patterns in large-scale datasets and predicting regulatory relationships. Yet even the most advanced models often fail to identify biologically meaningful mechanisms from statistical associations. This limitation arises not from algorithmic capacity but from the lack of mechanistically grounded input features. Our structure-informed framework Structure-based Topological Analysis of Regulatory and Molecular Activity Patterns (STARMAP) embeds protein three-dimensional structure and population-scale functional genomics data into a unified representation for mechanistic inference. By mapping over 1.5 million naturally occurring variants across ~1,700 cancer cell lines onto protein structures, STARMAP was able to identify spatial clusters of variation associated with shifts in transcriptional regulatory networks and drug response phenotypes. This approach transforms natural genetic variation into a large-scale, structure-info
- A brain-inspired framework for memory prioritization in neural networks based on valence
Improving long-term memory in artificial neural networks remains an open challenge. To address this, we developed a novel brain-inspired framework for memory prioritization based on the principle of emotional valence. Our framework includes: (i) a valence-weighted cross-entropy loss that scales the learning signal by the valence magnitude, analogous to neuromodulation; (ii) an amygdala-inspired module that learns high-dimensional valence embeddings; and (iii) a hippocampus-inspired module that integrates valence embeddings into the attention mechanism to modulate information retrieval. We demonstrated the generalization of our framework across spatial, episodic, and language-based memory tasks, consistently improving memory prioritization and long-term retention of high-salience information. In addition to improving long-term memory, we also showed that our framework can help mitigate the lost-in-the-middle problem in language modeling. More generally, this research provides further ev
- TopoFuseNet: Hierarchical Graph Representation Learning with Multi-Scale Topological Features for Accurate Drug Synergy Prediction
Accurate prediction of drug synergy is paramount for developing effective combination therapies and advancing personalized medicine. Although methods based on graph neural networks (GNNs) have become a prevalent approach, they often treat molecules as flat graphs of connected atoms, thus overlooking their inherent hierarchical structure (i.e., atoms forming functional groups) and the critical topological information that governs molecular interactions. To address this limitation, we introduce TopoFuseNet, a novel hierarchical graph representation learning framework that integrates multi-scale topological features. The core innovations of TopoFuseNet include: 1) The first-ever application of "Group Centrality" from network science to cheminformatics, enabling the identification and quantification of functional groups crucial to drug activity; 2) A systematic, multi-path strategy to seamlessly integrate node-level (atom) and group-level (functional group) topological features into a Grap
- Towards Continuous Home Monitoring for Dementia: A Real-Time mmWave Radar Framework for Activity Classification and Tracking
Millimeter-wave radar can quietly monitor health and behavior at home, which is vital for supporting people living with dementia. Most studies, however, remain limited to short-term testing in controlled spaces. Realworld deployment requires robust activity classification as a prerequisite: vital-sign and behavioral sensing require fundamentally different processing pipelines, and absent periods need to be reliably distinguished from stationary states. Bridging the critical gap between controlled laboratory demonstrations and continuous home monitoring, this paper introduces a self-adapting radar framework that extracts meaningful behavioral segments from massive, unconstrained real-world data. The system performs continuous real-time activity classification (stationary, walking, and absent) and target localization, selectively directing downstream processing to the most informative segments. It addresses key real-world deployment challenges including adaptive thresholding across subje
- Evaluating the Sensitivity of Dry and Gel-Based Wearable EEG for Cognitive Load Estimation
Purpose: We present a large-scale (N=120) comparative study of gel-based and dry electroencephalography systems for cognitive load analysis in tasks involving information visualization stimuli. Although dry systems are increasingly adopted owing to their portability and fast setup, their sensitivity to cognitive-related measurements (as compared to gel-based systems) remains debated. This limits the understanding of whether dry systems provide sufficient sensitivity for cognitive load assessment under controlled task conditions. Methods: We analyzed a diverse set of signal quality metrics, such as signal-to-noise ratio and channel retention, combined with spectral features across frequency bands to evaluate the ability for each device to capture workload-related neural markers during information visualization tasks. Results: Although the gel-based device showed consistently better quality results than the dry one, the effect sizes suggest a small practical significance of the differenc
- Characterizing Resting-State Brain Dynamics with Frequency-Resolved EEG Microstates: Parallel Analyses of Psilocybin Microdosing and Acute Inhaled DMT
Electroencephalographic (EEG) microstates provide a compact framework for characterizing the temporal organization of large-scale brain activity, yet their sensitivity to altered brain states remains insufficiently explored. In this study, we applied broadband and frequency-resolved EEG microstate analysis to resting-state EEG data from two publicly available datasets acquired under markedly different altered-state conditions: psilocybin microdosing and acute inhaled N,N-dimethyltryptamine (DMT). The aim was to determine whether narrowband microstate analysis reveals structured alterations in resting-state brain dynamics beyond those captured by broadband analysis alone. Psilocybin microdosing was associated with relatively subtle effects, including reduced global field power and frequency-specific alterations in delta- and theta-band microstate parameters, while no significant broadband spatiotemporal changes were observed. In contrast, acute inhaled DMT was associated with broader mi
- A Differentiable dFBA Simulator for Scalable Bayesian Inference over Microbial Metabolic Models
Medium optimisation for bioprocess design remains challenging and costly: fermentation recipes typically contain ten or more components, the design space expands combinatorially as ingredients are added, and each batch experiment requires over 24 hours. High-throughput 96-well plate screening can reduce experimental cost, but extracting actionable predictions from growth curves requires a mechanistic model that links medium composition to cellular metabolism. In this paper, we present a differentiable simulator for dynamic flux balance analysis (dFBA) that enables scalable Bayesian inference over microbial metabolic models. A distinguishing feature is that inference is driven entirely by OD600 measurements, a simple optical proxy for biomass, without substrate or product assays; internal fluxes, substrate consumption, and secreted metabolite profiles are recovered as latent variables constrained by the metabolic network stoichiometry. We resolve the core differentiability barrier of cl
- CT Attenuation Map Derived Body Composition Is Associated with Cardiorespiratory Fitness in Multicenter External Validation
Aim: Exercise capacity is a powerful predictor of cardiovascular risk. In patients unable to exercise, body composition analysis can potentially be used to estimate cardiorespiratory fitness. We developed a body composition fitness score, then validated its utility in two external populations. Methods and Results: We included patients from four sites undergoing single photon emission computed tomography (SPECT) and twelve sites undergoing positron emission tomography (PET). We quantified body composition using deep learning. We evaluated associations between body composition and good exercise capacity (defined as completing [≥]7 minutes on a Bruce protocol) then developed a body composition fitness score. We then assessed the associations of fitness score with exercise capacity and all-cause mortality in external populations. In total, 36471 patients were included with median age 67 (interquartile range 58 - 74). Median skeletal muscle density was higher among patients with good exerci
- Automated Brain and CSF Volume Assessment in Infant Hydrocephalus Using Deep Learning
Accurate brain and cerebrospinal fluid (CSF) volume assessment is essential for pediatric hydrocephalus management. Current clinical practice relies on linear measurements that fail to capture complex three-dimensional ventricular morphology, while quantitative volumetric assessment remains limited by laborious processing and lack of clinically optimized automated tools. This study developed a rapid, automated AI-based intracranial segmentation model suitable for clinical workflows. We retrospectively analyzed 167 T2-weighted MRI scans from infants with hydrocephalus, randomly split into training (60%), validation (20%), and hold-out test (20%) sets. All scans were manually segmented into CSF, brain parenchyma, and background. Our model integrates DenseNet and U-Net architectures with feature smoothness regularization to enhance generalizability. Performance was evaluated using Dice scores and absolute relative volume error (ARVE) compared with state-of-the-art methods. The AI model ac
- Simpler is not always better: Phylodynamic misspecification and deep-learning corrections
Phylodynamics bridges the gap between epidemiology and pathogen genetic data by estimating epidemiological parameters from time-scaled pathogen phylogenies. Multi-type birth-death (MTBD) models are phylodynamic analogies of compartmental models in classical epidemiology. They serve to infer the average number of secondary infections R and the infection duration d. Moreover, more complex MTBD models add extra parameters, such as the average length of the incubation period or the proportion of superspreaders in the infected population. However, these additional parameters come at an important computational cost: Apart from the simplest, BD, model, MTBD models do not have a closed-form solution and require numerical methods for their likelihood computation. This leads to increased computational times and potential numerical errors. Therefore, the BD model remains the favorite researchers' choice for real dataset analyses, and is often applied even in cases where more complex epidemiologic
- DentaCoPilot: An LLM-Augmented Next-Procedure Recommender for General Dentistry, Designed for Dentist Augmentation
Background. Commercial dental artificial intelligence in 2026 is overwhelmingly diagnostic: caries, calculus, periapical, and bone-level detection on radiographs. The clinically harder question that follows every diagnosis-given a patient's chart and most recent procedure, what should the dentist do next-remains unsolved at general-dentistry scale. The closest published system, MultiTP (Chen et al., 2024), is a CNN-RNN restricted to partial-edentulism cases and provides neither calibrated uncertainty, structured rationale, nor an evaluation that treats the model as decision support instead of an autonomous classifier. Methods. We introduce DentaCoPilot, a recommender that, given a structured chart, returns (i) a calibrated top-K probability distribution over Current Dental Terminology (CDT) codes for the next procedure,(ii) a verbalised confidence label, (iii) an explicit abstain flag when context is insufficient, and (iv) a chart-grounded rationale. We compare four classical baselines
- Transforming Semi-structured Variant Assessments into Computable Clinical Assertions: A Pilot Study for AI-Assisted Curation
Genomic medicine relies on expert evaluation of genomic variants, but this process is dramatically slowed by a lack of readily-accessible genomic knowledge. Although genomic knowledge resources such as ClinVar and CIViC support structured data sharing and provide interfaces for adding structure, much of the variant interpretation data generated upstream of these resources is not readily interoperable with these resources, limiting the ability of clinical labs to share data and creating knowledge silos. Here we evaluate a strategy for breaking down these knowledge silos in a pilot study to transform semi-structured variant classification knowledge into computable clinical assertions leveraging the Global Alliance for Genomics and Health (GA4GH) Genomic Knowledge Standards specifications. We programmatically mapped previously captured somatic cancer clinical significance classifications from spreadsheets to the GA4GH Variant Annotation specification. For diagnostic classification data, t
- Reproducible Biochemical Clusters Embedded Within a Continuous Neurochemical Landscape of Autism Spectrum Disorder Revealed by NeuroCLAD
Abstract Background Autism Spectrum Disorder (ASD) is marked by pronounced biological heterogeneity, yet most neurochemical studies have relied on single-analyte comparisons that cannot capture coordinated variation across neurotransmitter systems. Whether ASD blood neurotransmitter profiles reflect discrete subtypes, a continuous landscape, or something in between remains unresolved. Methods We applied NeuroCLAD, a structured multivariate analytical framework, to peripheral blood neurotransmitter profiles from 261 children with ASD (mean age 6.98 [SD 3.13 years]; 78.5% male). The pipeline incorporated z-score normalisation, natural cubic spline residualisation for age and sex, principal component analysis, k-means clustering, consensus stability assessment, Gaussian mixture modelling, Cohen's d enrichment analysis, and clinical symptom mapping. Cross-compartment consistency was explored using urine neurotransmitter profiles from the same cohort. Results Twelve reproducible biochemical
- Toto
Context rich tasks sent to the best model.