AI News Archive: July 12, 2026 — Part 6
Sourced from 500+ daily AI sources, scored by relevance.
- https://budtravels.com
cannabis travel guides budtravel help you stay informed
- Fuelshine
Track work miles automatically and simplify reimbursement
- PapaMarks
Real IGCSE & A-Level past papers, marked by AI in seconds
- Our Lands
Every public land, mapped to how you explore
- explorador
AI native real estate agent
- GasSaver
Compare live gas fees across L2s and bridge ETH in one click
- UrCalculator
Free finance, tax & health calculators for US & Canada
- Centauri
Analyze content beyond AI discoverability spam
- Fitzpot
Run your entire fitness business on one intelligent platform
- GolfScope
Real-time AI golf tracker & live launch monitor.
- Gulfstream G650 Aircraft Model
Custom handcrafted Gulfstream G650 wooden model
- AfriX - Family Tree & Legacy
Preserve the past, empower the future.
- Callout AI
AI phone receptionist for UK businesses — answer every call.
- AIdometer
The odometer for your AI — Claude & Codex limits on Mac
- Eazy PDF Tools
All your PDF needs, right in your browser
- Brandeefy Digital Innovations LLP
Grow Your Business with Performance Marketing
- AI ASMR Video Prompt Library
Create and monetize AI ASMR Videos with the best prompts!
- proposalai
AI-powered proposals optimized for Upwork\'s algorithm.
- eWith.me
AI Writing Assitant and Flashcards for Students
- Plugsky.com
Unlimited AI usage across models, agents, and APIs
- TapeType
AI text snippets that work everywhere.
- Tubetoolkitai
YouTube tools for creators
- Foca AI - White BG E-commerce Images
Reliable white-background product images from casual shots
- Roamwise - AI Travel Planner
No Crowd No Loud Travel Smart
- 8loop
AI-Employees across Voice, Chat and Email
- KwizzEase: DeepWork, Flash Cards, Quiz
App that forces you to pass a quiz to unlock your phone.
- 12 BÍ MẬT DÙNG CHATGPT HIỆU QUẢ
12 BÍ MẬT DÙNG CHATGPT HIỆU QUẢ GẤP 10 LẦN
- CapyAI | Image Studio
AI Image Generation Workspace
- Plugroo
AI-powered EV charging stations finder, built for Australia.
- Eventscape.ai
Visualize your event with photorealistic AI design.
- RemoveGlassesGlare
Remove glasses glare from photos in one click.
- Convly AI Image-to-Prompt
Turn any image into an editable AI prompt — free, no signup.
- Summ-it
Turn everything you meant to read into a playlist.
- Rize Trade
The trading journal that builds discipline.
- SecuredAI
AI assistance without exposing your sensitive data.
- LectMate
Understand every lecture, in any language.
- Lip Reader Pro
Read lips with AI precision no audio required.
- AI Agent Builder by Latenode
Turn workflow descriptions into working AI agents.
- ChatGPT Work
ChatGPT Work
- GPT‑5.6
GPT‑5.6
- SpotScribe
SpotScribe
- Automateed
Automateed
- Leapd
Leapd
- Soku AI
Soku AI
- Spatial Glyco-Codes Define Human Liver Pathology and Progression
Glycosylation is a fundamental process regulating cellular function, tissue organization, and disease progression. However, comprehensive glycan profiling at single cell spatial resolution remains largely inaccessible, particularly in clinical archival tissues. Here we develop spatial GPT, a multimodal platform for simultaneous profiling of glycans, proteins, and/or transcripts in archival formalin-fixed paraffin-embedded (FFPE) tissues. Using a panel of 30 DNA encoded lectins recognizing major mammalian glycan motifs and structural classes, sequencing-based spatial-GPT (DBiT GPT) mapped the spatial glycome, proteome, and transcriptome across 16 human liver specimens encompassing steatosis, fibrosis, cirrhosis, and hepatocellular carcinoma (HCC), leading to identification of spatial glyco codes. combinatorial glycan states associated with distinct cellular identities, tissue features, and pathological processes. Unexpectedly, glyco codes alone were sufficient to resolve major cell types, disease states, and HCC subtypes, revealing a previously unappreciated level of biological information encoded within the tissue glycome. Spatial glycomics uncovered tumor-like glyco-codes in premalignant regions, suggesting that glycan reprogramming may precede overt malignant transformation. Using imaging-based single-cell spatial glycan protein profiling (CODEX GP), we track glyco codes across the whole-tissue architecture of 3 representative HCC samples. We further examined the glyco-codes across more than 300 patient specimens and quantified cell-type- and disease-specific glyco codes as well as glycan-defined immuneevasion, T-cell-exhaustion, and steato fibrotic niches. Together, these findings establish spatial glyco-codes as a previously unrecognized layer of tissue organization that encodes cellular identity, tissue function, and disease progression. The ability of glyco-codes to distinguish major liver pathologies across independent patient cohorts further highlights their potential as a new class of molecular histopathology biomarkers.
- A geometric atlas of how ESM3 organizes modalities across depth
Protein language models learn general-purpose representations from large collections of protein sequences and structures, and have advanced the prediction of protein structure and function. ESM3 is a multimodal protein language model that ingests a protein through several channels at once, including amino-acid sequence, three-dimensional structure, secondary structure (SS8), solvent accessibility (SASA), and discrete functional annotations, summing their embeddings into a single residual stream. Little is known about whether these modalities occupy separate subspaces and the depth at which they fuse. The present analysis examines ESM3 (esm3-sm-open-v1; 1.4 billion parameters; 48 transformer layers) once per modality in isolation and applies representational-similarity analysis across all 48 layers. The four physical modalities (sequence, structure, SS8, SASA) begin in distinct subspaces, remain maximally separated through roughly the first half of layers, and then fuse into a shared low-dimensional subspace between layers 25 and 35. The fusion is ordered. The structure-derived modalities (structure, SS8, SASA) are mutually aligned from the input, whereas sequence joins last, after layer 28. The functional-annotation modality never fuses; instead, it remains representationally orthogonal to the physical modalities at every layer, and this orthogonality holds whether the annotation is supplied as whole-protein or per-residue, suggesting that it is content-driven rather than a tokenization artifact. The fusion is a learned property, absent in a randomly initialized model of the same architecture, holds at the residue level below the mean-pool, and reorganizes variance, converting between-condition variance into within-condition variance while the stream never approaches isotropy. Fusion depth is independent of protein length but is delayed by structural disorder. The phenomenon is universal across diverse organisms. Across 5,555 proteins from 12 organisms spanning eukaryota, bacteria, and archaea, every superkingdom (and every individual organism) reaches peak modality fusion at the same network depth (layer 35).
- OCellus: A Language-Model Framework for Single-Cell, Spatial, and Perturbation Biology with Natural-Language Reasoning
Computational modeling of cellular behavior - the virtual cell - has emerged as a stated grand challenge at the intersection of artificial intelligence and biology, yet existing foundation models remain specialized: single-cell models process dissociated transcriptomes only, spatial models require dedicated spatial-aware architectures, and perturbation predictors depend on manually curated knowledge bases that cap generalization. Here we introduce OCellus, a single nine-billion-parameter language model (Qwen3.5-9B) fine-tuned on twenty-two biological tasks that simultaneously addresses all three limitations through three coordinated technical contributions on a shared backbone. First, EvenClock encodes two-dimensional spatial coordinates as eighteen clockface sectors of text, enabling spatial reasoning on a vanilla language model without architectural modification; on ten spatial transcriptomics tasks OCellus attains 77 percent spatial-neighborhood accuracy, 96 percent spatial-cellchat accuracy, and 0.70 proportion-cosine similarity on spatial deconvolution, all without any spatial-aware architectural components. Second, per-gene language-model embeddings replace the Gene Ontology annotations that GEARS depends on, achieving Pearson correlation 0.945 on the Replogle 2022 perturbation benchmark versus 0.84 for GEARS across 457 completely unseen knockout genes. Third, OCellus-Agent provides a Planner-Router-Verifier natural-language interface that achieves 75 percent pipeline accuracy on eighty multi-task queries. Removing language-model embeddings collapses perturbation Pearson to 0.06, confirming that learned functional representations - not graph topology - drive the gain. As a cell-type encoder, OCellus ranks first among fourteen foundation models in linear-probe accuracy at 95.1 percent across four benchmark datasets, and reaches 72.6 percent average across twenty-two evaluated biological tasks - a 57-percentage-point absolute gain over the strongest baseline configuration. As a language model, OCellus uniquely generates natural-language explanations of its predictions, a capability absent from all competing methods. Code, pre-trained model weights, the graph-neural-network module, and the agent system will be made available upon publication.
- ProtBLIP2-SST: Protein Function Prediction via BLIP2 with Sequence, Structure, and Text
Protein function prediction traditionally relies on structured gene ontology (GO) labels or multi-label classifiers. However, these labels or classifiers cannot flexibly describe molecular function, biological process, cellular component, and free-text functional narratives in a single output. In comparison, generation-based approaches offer an intuitive paradigm for flexible free-text protein annotation, with large language models (LLMs) as a representative method for protein-text modeling. Recent efforts on utilizing LLMs for protein semantic understanding and annotation generation have adopted sequence-only encoding or sequence-text contrastive alignment paradigms, yet without explicit consideration of three-dimensional structural information. To address these limitations in current protein function prediction methods, we present ProtBLIP2-SST, a two-stage framework built on the BLIP2 model architecture that bridges protein sequence, structure, and text for open-ended protein functional caption generation. Specifically, we first integrate sequence and structure information through SaProt, a protein language model (PLM) with a structure-aware vocabulary that fuses residue tokens with Foldseek-derived 3Di structural tokens. To empower the LLM to understand protein semantics, we employ a Q-Former (a querying transformer in BLIP2) with learnable query tokens as the cross-modal projector to align protein features from the frozen SaProt encoder and text features from a frozen BiomedBERT via protein--text contrasting, protein--text matching, and protein captioning objectives. After alignment, the protein features are linearly projected and prepended to the prompt embeddings of the LLM for protein captioning fine-tuning with LoRA. Trained on 441k protein--text pairs from Swiss-Prot with corresponding structures from the AlphaFold Database, our ProtBLIP2-SST outperforms sequence-only and sequence-text alignment baselines on protein captioning metrics, with ablation studies demonstrating the effectiveness of integrating structure with sequence information for improved protein understanding. Through a unified two-stage alignment-and-generation pipeline, ProtBLIP2-SST integrates protein sequence and structural information, overcomes the rigidity of traditional GO-centric classification, generating open-ended captions that jointly describe molecular function, subcellular location, and homology context in one single output.
- In-Hand Salary Calculator
Calculate your actual take-home salary after PF.
- EcoMorph: Universal morphological trait quantification from natural language prompts for ecological research
1. Morphological traits such as floral area and body size are fundamental to ecological research, serving as inputs for studies of pollinator-plant interactions, habitat quality, and biodiversity monitoring. However, accurately measuring these traits from images remains challenging, particularly in complex field conditions where existing tools exhibit reduced accuracy and limited generalizability across taxa. 2. We present EcoMorph, a modular morphological measurement system that leverages the Segment Anything Model 3 (SAM3) to quantify traits across diverse ecological contexts. Unlike task-specific segmentation models requiring domain-specific training data, SAM3's prompt-based architecture enables segmentation of arbitrary biological structures from natural-language prompts, using the same underlying model across flowers, insects, and other targets without retraining. From the resulting segmentations, EcoMorph extracts three classes of measurement: area, linear dimensions, and object counts. 3. We validated EcoMorph across two ecological scales. At the intermediate scale, EcoMorph-derived floral area agreed closely with manual ImageJ measurements (R2 = 0.935, n = 74) under simple-background conditions and (R2 = 0.928, n = 58) under complex-background conditions, with valid predictions for 95% of images. At the fine scale, EcoMorph-derived insect body area was strongly correlated with hand-measured intertegular distance (r = 0.810, n = 349), capturing body-size variation across species from the small Bombus impatiens to the large Xylocopa virginica. Object counts matched manual counts almost exactly for well-separated insects in an insect box (R2 = 0.9997, n = 12). 4. By combining prompt-based segmentation with modular measurement, EcoMorph enables high-throughput quantification of area, size, and abundance from heterogeneous image sources without taxon-specific training. This generality supports a broad range of ecological applications, including pollinator and plant trait research, biodiversity and abundance monitoring, and allometric biomass estimation.