AI News Archive: May 3, 2026 — Part 5
Sourced from 500+ daily AI sources, scored by relevance.
- varaa.ai
AI based cloud design and architecture tool
- Nompt
Framer For Ai Media And Systems !!
- SeenZone
Stop getting left on seen — AI texting coach for Indian guys
- Noesis
Understand Everything.
- Poly
Hi! My name is Polly and I am your financial assistant.
- Han Xue
Learn Chinese through AI-powered HSK stories
- NestUI
All-in-One AI: Video, Image, Audio & Editor Suite
- FableGM
Understand anything easily
- Pluck
AI shopping extractor no API key required
- ViralSceneAI
Your ideas are raw. Our agents make them elite
- VariantIQ
genomics, variant-analysis, AlphaGenome, bioinformatics, AI
- BallonsTranslator Pro
Open-source manga/comic translation with "SOTA" workflows
- Claude Design
Turn prompts into polished web design work.
- Ai remove text from image
Remove Text & Objects from Images with AI
- PixVerse C1
Cinematic AI video generation
- AnumTechno
Most AI content fails. This fixes it.
- Utrly.ai
AI Mock Interview Practice-Advanced,Adaptive & Personalized
- Aira CRM
The Spanish CRM that closes deals via WhatsApp, email & AI
- Meet Alto AI
Grow your email list and boost sales on social media.
- AI GROWTH BOX
The First Autonomous Social Protocol for AI Agents
- Rose
AI agents that turn every website visit into a pipeline
- TaskMind: Your AI Productivity Employee
Hire an AI agent to organize, prioritize, and manage tasks.
- Happy Horse
Happy Horse AI Video Generator
- ScanTrade
Trading AI Chart Analysis
- Nites AI: Homework Helper
Struggling with homework? Let Nites AI solve it instantly
- AI Desktop Assistant Starter Kit
opensource, sideproject, tooling, productivity, webdev, css
- TryBestSpecs
AI finds the glasses that fit your face — in 30 seconds
- WaGraph
Generate stunning AI images from text in seconds
- AI Social Post Generator
Generate viral social posts in 1s with multi-model AI.
- ChatGPT Image 2 Prompts by BgRemovit
Curated ChatGPT Image 2 prompts you can copy instantly
- Gnix
Your terminal, in plain English.
- tappedin.ai
Custom AI news tailored to your interests in your DM daily.
- SEO for AI
Get cited by ChatGPT, Perplexity and Google AI Overviews
- Oratrixa Phronosis
Agents don't act. They propose. Governance decides.
- NEO AI
The AI that doesn't answer. It acts.
- Wavee
Wave your voice & get text at your cursor
- 1000+ Ultimate AI Prompts
Save 100+ hours in Marketing, SEO & Copywriting 🚀
- I Can Affirm
Affirmations that adapt to you — not repeat at you
- Maquete AI
Photorealistic AI renders in under 30 seconds.
- SkyGen Plus | AI Image Generator
Generate, remix and refine images with multiple AI models.
- AI Personal Color Analysis
Discover your perfect colors with AI-powered analysis.
- Grok
Conversational AI for understanding the universe.
- A 37-million-particle dataset from over 250 experiments to accelerate data-driven cryo-EM analysis
Cryogenic Electron Microscopy (cryo-EM) has revolutionized structural biology by enabling near-atomic-resolution structure determination of biological macromolecules. Central to cryo-EM analysis are particles, namely 2D projections of biomolecules extracted from micrographs, which serve as the primary input for 3D reconstruction. While data-driven methods have transformed other scientific domains, their impact on cryo-EM remains limited because existing particle datasets are too small, too narrow in protein diversity, and lack rich per-particle annotations. We introduce cryoPANDA (cryo-EM Particles ANnotated DAtaset), comprising over 37 million annotated particles from 252 experiments spanning a wide range of protein types, more than 10-fold larger than prior collections. Each particle is accompanied by detailed annotations covering acquisition, classification, and reconstruction metadata, alongside the corresponding 3D electrostatic potential map, the published EMDB map, and, where av
- Influence of distractors on spatial working memory and neural activity in marmoset prefrontal cortex
The prefrontal cortex (PFC) plays a critical role in maintaining working memory (WM) representations while filtering irrelevant distractors. In macaques, PFC neurons exhibit persistent delay period activity that is robust to distractor interference. The common marmoset has emerged recently as a complementary primate model for investigating the neural basis of cognitive processes including WM, in part because the relatively lissencephalic cortex of this species enables laminar recordings which could provide substantial insight into the microcircuit basis of these functions. It remains unknown however, whether marmoset WM performance is robust to distractors presented during delay periods of WM tasks, and how such distractor filtering may be implemented in PFC circuits. Here, we addressed this gap by conducting wireless recordings of PFC in freely moving marmosets performing a touchscreen-based delayed-match-to-location (DML) task in which a salient visual distractor was presented during
- Central versus peripheral neural control of a coordinated walking pattern in Drosophila.
Walking involves coordinate rhythmic movements at every joint of every leg. Central pattern generator (CPG) circuits in the spinal or ventral nerve cord provide such a rhythmic drive to all the legs. In turn, each leg provides rhythmic sensory feedback through peripheral proprioceptive neurons. Disentangling contributions from these two rhythmic drives, has been a long-standing hurdle in uncovering both the structure and function of neural-circuits governing the generation of a coordinated walking output. Here, using the highly tractable Drosophila model and a novel sensory-deprivation paradigm, we uncovered central and peripheral neural pathways underlying walking pattern generation. We provide evidence that each leg is governed by its own CPG module with an inherent cycle period that is unmasked when proprioceptive feedback is reduced. We find that contact driven load inputs and descending brain inputs are critical for coordinating the intra-leg movements and shaping the microstructu
- Maturation of Cognitive Control in the Inferior Frontal Junction: A Combined Systematic Review and Coordinate-Based fMRI Meta-Analysis
Cognitive control is fundamental to goal-directed behavior, and its protracted maturation is a hallmark of adolescent brain development. In adulthood, the inferior frontal junction (IFJ) is functionally characterized as a critical region for updating task representations to guide the implementation of cognitive control. Yet, how its domain-general control functions emerge and mature across development remains largely underexplored. Specifically, it is unclear whether the IFJs capacity for cognitive control enhances uniformly as a single construct, or if this region matures asynchronously for distinct control processes like inhibition, switching, and working memory. To address this gap, we conducted a combined systematic review and coordinate-based neuroimaging meta-analysis. Applying multilevel kernel density analyses to fMRI studies of inhibition, switching, and working memory in youth and adults, we synthesized data from 72 contrasts (779 foci; N = 1,913). The results revealed a stag
- Use of Large Language Models by U.S. Adults to Support Exercise: A Survey Study
Background: Large Language Model (LLM) chatbots are increasingly used for exercise and fitness topics, yet users' experience with these tools remains understudied. Methods: This study is a national survey of U.S. adults who have used an LLM chatbot for exercise-related topics in the past month. Participants answered questions about the exercise-related topics for which they used LLM chatbots, their perceptions of these chatbots' value for exercise-related questions, and how chatbot use had changed their exercise behaviors and use of other exercise-related resources. Results: Participants (n=258) were majority male (n=138, 53.5%) and white (n=146, 56.6%) with a mean age of 41.7 (SD=14.9) years. The most endorsed topics for LLM chatbot use were making an exercise plan (n=137, 53.1%), nutrition related to exercise (n=132, 51.2%), advice on amount of exercise (n=122, 47.3%), specific exercises to try (n=120, 46.5%), and motivation or emotional support for exercise (n=112, 43.4%). On averag
- Transforming Patient Voices into Early Predictors of Survival Using Nonlinear Mixed-Effect Models and AI/ML for Patient-Centered Decision-Making
Patient-reported outcomes (PROs) capture the patient voice and have been associated with improved clinical outcomes in oncology, but their prognostic and predictive value remains underutilized due to challenges in interpreting these highly variable and noisy PRO data. Here, we developed a quantitative modeling framework integrating nonlinear mixed-effects (NLME) and item response theory (IRT) to characterize symptom-level PRO trajectories and transform them into clinically actionable predictors. Using longitudinal PRO data from 589 patients with metastatic cancers in the PRO-TECT trial, we modeled 332,920 symptom responses to estimate patient-specific PRO trajectory parameters while accounting for variability and noise. IRT-NLME modeling captured heterogeneous symptom-level PRO dynamics and is more informative than modeling with composite PRO scores. PRO trajectory parameters were strongly associated with overall survival, acute care utilization, and treatment modifications. Machine le
- Predicting first-onset depression in adolescents: Do general population models generalize to youth with ADHD?
Background: Most studies seeking to identify youth at increased risk for depression have developed prediction models using a limited set of risk factors in general population samples. It is unclear whether these models generalize to high-risk youth. Here, we developed machine learning algorithms to predict first-onset depression in youth from the general population and high-risk youth with attention-deficit/hyperactivity disorder (ADHD). Methods: Participants were 4803 unrelated children from the ABCD study with no prior mood disorder and complete data at baseline (age 9-10 years) and 2-year follow-up. Support Vector Machine, Random Forest, and Elastic Net models were used to predict first-onsets from clinically-relevant risk factors spanning mental and physical health, cognitive, dispositional, interpersonal, and socio-environmental domains. Predictive performance was evaluated in the full sample and separately in participants with ADHD (N=584, 12.16%). Results: Models trained on the
- An Efficient and Interpretable Learning Approach for Large-Scale Histopathology Data
Prostate cancer (PCa) remains one of the leading causes of cancer-related mortality among men, and histopathological analysis of prostate biopsy specimens is central to diagnosis and risk stratification. Whole-slide Images (WSIs) capture rich morphological information, but their gigapixel scale and the large number of extracted tissue patches make exhaustive annotation and model training computationally expensive. Attention-based Multiple Instance Learning (MIL) has emerged as an effective weakly supervised framework for WSI analysis, enabling slide-level prediction without requiring patch-level annotations. However, training MIL models on large histopathology cohorts remains resource intensive because many extracted patches are non-informative, and some patches are often processed repeatedly during training. To address these challenges, we propose an efficient and interpretable learning framework for large-scale histopathology analysis. Our method combines a pathology-pretrained UNI e