AI News Archive: June 22, 2026 — Part 19
Sourced from 500+ daily AI sources, scored by relevance.
- QuantVest Engine
Investing, personal finance
- ReconRecond App
Dealership vehicle reconditioning & servicing, simplified.
- Delta Executor
best roblox game exploits
- Keppio
Find your perfect influencer match.
- NerdBuddy
AI math solver for Chrome that helps solve problems from web
- iZoe
AI- Powered accounting and business management made simple
- TrendRadar AI
Daily AI digest in your Discord never miss a trending topic
- OpenHop
Your AI walks you through code, step by step.
- Why Machines Misread Pedagogical Quality: Human-Machine Alignment in LLM-Based Pretest Question Evaluation
Designing effective pretest questions is challenging at scale: high-quality questions require careful calibration of openness, cognitive depth, and alignment with learning objectives, yet generating and evaluating them manually is time-consuming. We present an AI-assisted workflow for pretest questi...
- Hallucinations in Organization-backed AI advisors: Evidence about Skepticism, Verification, and Reliance in Goal-Directed Use
Generative AI systems are increasingly used by organizations to deliver information to consumers, patients, students, employees, and citizens. These systems can hallucinate, producing plausible but inaccurate responses. A central question for AI-advised decisions is therefore not only whether users ...
- Students' Perception Accuracy of Partners' AI Use and its Relation to Collaboration Performance
Collaborative assignments are a cornerstone of programming education. Effective collaboration during a programming project depends on the formation of reasonably accurate beliefs about how each partner works. Generative AI tools, now widely used by undergraduate students, have introduced a consequen...
- Active non-redundancy and viral orchestration sustain diel microbial successions in the coastal ocean
Ecosystem resilience in dynamic coastal oceans is conventionally ascribed to functional redundancy, where taxonomically distinct microbes buffer environmental fluctuations through interchangeable metabolic roles. In this study, we reveal a deterministic succession architecture sustained by active non redundancy and temporal metabolic coupling, uncovered via autonomous drone array metatranscriptomic sampling in Daya Bay, China. By resolving the transcriptional landscape into six chronometrically phased modules rather than simple time points, we demonstrate a near total renewal of the active gene pool, with greater than 90% of transcribed clusters showing phase exclusivity within approximatively a two-hour window. This radical functional reshaping exposes a tight, scale dependent taxonomic functional coupling where community composition is a strong, linear predictor of metabolic output, peaking at the genus level (p < 0.0001). Functional continuity is maintained not by redundant generalists, but by a precisely sequential relay of specialists, each optimized for transient microniches with minimal overlap between successive phases. Deep learning structural adjudication of the psbA gene pool further reveals viral orchestration of this temporal coupling, where incoming cyanophages introduce structurally distinct protein variants that sustain photosynthetic electron flow under peak irradiance. These findings redefine coastal microbiome stability as a fine-tuned rapid succession of temporal specialists rather than a redundant backdrop. We propose a fundamental revision of marine ecosystem models, shifting from passive buffering frameworks to deterministic, clock driven architectures, which may prove to be critical for forecasting microbiome responses to accelerating short term climate variability.
- Real-world results from a Machine Learning-guided, phenotypic High-Throughput Screen for novel antibiotics
Antimicrobial resistance is an urgent global health threat, with over 2.8 million multidrug-resistant infections killing over 35,000 annually in the US. Machine Learning (ML) has emerged as a potential solution to improve efficiency of antibiotic high-throughput screens (HTS). We report ML-guided high-throughput screening against E. coli. Large-scale Learning-to-Rank models were trained on public and proprietary datasets to maximize phenotypic inhibition and minimize human cell cytotoxicity. We evaluated several pre-plated compound libraries and a set of "cherry-picked", structurally novel compounds. We screened against a hyperpermeable lptD- mutant, followed by hit confirmation, profiling, cytotoxicity counter-screening, and MOA determination. Results demonstrated a doubled hit rate and 3X fewer toxic hits. Additionally, activity improved against both Wild Type E. coli and the lptD- mutant. ML models showed robust predictive power on structurally dissimilar compounds. The combination of large-scale HTS, ML innovation, and both library-wise selection and cherry-picking strategies distinguishes this study in the antibiotic discovery field.
- Reduced dopaminergic reinforcement, not learning capacity, limits operant learning in aging Drosophila
Aging is associated with a progressive decline in cognitive function, including the ability to adapt behavior based on its consequences. While classical conditioning in Drosophila melanogaster has provided key insights into reinforcement learning, how aging impacts operant learning and adjusting behavior based on action outcomes remains unclear. Here, we used a closed-loop optogenetic paradigm to test how aging affects operant learning and the role of dopaminergic neurons (DANs; PPL1 and PAM) in this process. Activation of distinct DAN subsets revealed that both young and aged flies retain PPL1-dependent avoidance learning, indicating preserved action-outcome learning with age. However, learning in aged flies depended on prolonged reinforcement: unlike young flies, they failed to learn under shorter optogenetic stimulation durations, suggesting an aging-associated reduced dopaminergic reinforcement rather than a loss of learning capacity. Moreover, reducing mitochondrial antioxidant capacity via SOD2 knockdown in PPL1 neurons phenocopied the aging-related deficit, implicating oxidative stress in impaired reinforcement signaling. In contrast, broad PAM neuron activation drove robust learning across ages and stimulation regimes. Nevertheless, functional dissection of PAM subpopulations revealed subtype-specific aging-associated vulnerability within dopaminergic circuits. In line with the behavioral data, PPL1 but not PAM neurons exhibited age-dependent reductions in cell size. Together, our findings suggest that aging selectively reduces dopaminergic reinforcement in a DAN subtype-dependent manner while preserving the capacity for operant learning. Increasing reinforcement length rescues this deficit, indicating that altered dopaminergic signaling, rather than impaired learning capacity, is a key driver of age-related cognitive decline.
- HTS-Oracle X: AI-Guided Prospective Discovery of Small Molecule Immune Checkpoint Binders
Targeting immune checkpoint protein-protein interactions (PPIs) using small molecules remains limited by the shallow, featureless binding surfaces of co-stimulatory and co-inhibitory receptors and the characteristically low hit rates of conventional high-throughput screening against these interfaces. Here we report HTS-Oracle X, a multimodal deep learning platform that integrates bidirectional cross-attention fusion of ChemBERTa SMILES embeddings with extended RDKit descriptors, trains on continuous biophysical binding signals rather than binary labels, and employs Monte Carlo Dropout uncertainty quantification for uncertainty-adjusted compound selection. Trained on 45,760 Dianthus TRIC-screened compounds per target under scaffold-aware cross-validation, HTS-Oracle X was applied prospectively to a 100,160-compound Enamine library against CD28, TIM-3, and VISTA. From 150 model-selected compounds, 45 dose-response confirmed binders were identified (30.0% overall hit rate), yielding enrichment factors of 234-408x over experimentally established random prospective baselines and 16 sub-micromolar hits. The top hits, HX-CD28-1 (KD = 233 nM), HX-TIM3-1 (KD = 249 nM), and HX-VISTA-1 (KD = 345 nM), demonstrated on-target functional activity in immune cell and tumor co-culture assays. HTS-Oracle X represents a scalable AI-guided framework for small molecule discovery against non-enzymatic immune checkpoint targets.
- Drug-Prot: A query system for statistical inference of drug effects and interactions in dynamic proteomic networks
Understanding drug effects and drug-drug interactions is essential for developing combination therapies. We present Drug-Prot, a computational framework that leverages large-scale perturbation proteomics to quantify causal drug effects, drug-drug interactions, and dynamic protein relationships. Using data from 63 single drugs and 59 drug combinations applied to 18 breast cancer cell lines at 6, 24, and 48 hours, Drug-Prot estimates drug effects on protein expression and reconstructs directed temporal protein dependency networks. The publicly available software enables targeted analyses of user-defined protein sets, substantially reducing the multiple-testing burden. Through an interactive web application, users obtain corrected p-values for single-drug and combination effects, directed temporal dependency networks, and downloadable results without requiring access to the underlying proteomic dataset. As a use case, we apply invariance-regularized Random Forests to triple-negative breast cancer cell lines to identify proteins associated with drug response. Querying these proteins in Drug-Prot reveals drug-specific and interaction effects at the protein-network level, illustrating how the framework links candidate causal protein features to actionable drug combinations.
- Multivariate Random Forests for Cross-Modal Multi-Omics Integration
Multi-omics studies are widely used across many areas of biomedical research. In many diseases, some signals are shared across data types, while others are strongest in a single omics layer. Current multi-omics clustering methods often either merge all data types into a single representation, which can blur biology that is strong in one layer, or rely on linear structure that may miss more complex relationships across data types. We introduce multiRF, a random-forest-based method that handles complex data types and separates shared and modality-specific structure for multi-omics data. multiRF learns sample similarities across omics layers from multivariate random forests, combines them across data types, and uses the resulting weights to estimate the part of each omics layer that is predictable from the others. The remaining residual is treated as modality-specific signal, allowing shared and modality-specific similarities to be clustered separately. In simulations, multiRF recovered shared clusters as well as or better than established integrative methods while more reliably separating modality-specific signal under nonlinear data structures. In TCGA head and neck squamous cell carcinoma, the shared component aligned with the main subtype structure across established reference classifications, while gene- and miRNA-specific components revealed additional immune and developmental biology. In the ADNI cohort with matched blood DNA methylation and structural MRI, the shared cross-modal aging signal was associated with future conversion to mild cognitive impairment or Alzheimer's disease, and a DNAm-specific residual signal showed exploratory additional information. These results show that multiRF can recover a common disease axis while retaining biologically meaningful signals specific to one data type. multiRF is available as an open-source R package at https://github.com/novawz/multiRF.
- Symptom-based phenotype discovery in motor neuron disease using natural language processing of electronic health records
Background: Motor neuron disease (MND) is a fatal neurodegenerative condition with significant clinical heterogeneity that is incompletely captured by existing phenotype classifications based on onset site. Electronic health records (EHRs) contain detailed symptom documentation in clinical narratives that may enable data-driven discovery of clinically meaningful patient subgroups. Methods: We developed a natural language processing (NLP) pipeline using MedCAT to extract symptoms from clinical notes of 2,361 people with a confirmed diagnosis of MND at a tertiary neurology center. MND cohort confirmation used three complementary methods: clinic attendance records, text-based diagnosis detection, and NLP extraction with negation detection. Extracted symptoms were filtered to Unified Medical Language System semantic type T184 (Sign or Symptom) with removal of negated concepts. Patients were clustered using latent class analysis on binary symptom profiles. Survival differences were assessed using Kaplan-Meier analysis, log-rank tests, and Cox proportional hazards regression. Results: From the first clinical notes, we identified four clusters of symptoms among 872 patients and 76 symptoms: Motor-Bulbar (n=373), Motor-Tremor (n=154), Sensory-Pain (n=222), and Motor-Respiratory (n=123). When extended to all clinical notes (n=2,065; 184 symptoms), these reorganized into three clusters: Autonomic-Respiratory (n=472), Nocturnal-Respiratory (n=338), and Classic Motor (n=1,255). Survival differences were significant across all clusters in both the first notes and all notes analyses (log-rank p < 0.001). Conclusions: NLP-based symptom extraction from EHRs identifies clinically meaningful MND subgroups that extend beyond traditional onset-site classifications. Autonomic-respiratory symptom burden is associated with poorer survival while a newly identified Sensory-Pain subtype with a better prognosis. These data-driven phenotypes may improve prognostication and inform targeted supportive care.
- handa
The easiest and fastest way to find your unclaimed royalties
- Digital Blueprint
Scan your email footprint & stop hidden subscription traps
- QuantX
Learn financial markets, the smart way
- Streamy — Fair chance
streamers
- Mimo - Your Personal AI Assistant
Stop switching tabs. Ask Mimo instead.
- Comperge
The infinite-scaling, 100% client-side code diffing engine.
- reAItro
Remote sprint retros your team shows up for
- Planify AI
The AI planner for overwhelmed minds
- 5sdesign
Curated AI design prompts for building sites with AI
- Lexacore
AI-powered legal intelligence for Indian businesses.
- RankLLM
Rank Higher in ChatGPT, Gemini, Claude & Perplexity
- MailShot
#newsletter #emails #builder
- AI legal firm wins court battle in UK first
AI legal firm wins court battle in UK first The Telegraph
- AI law firm wins UK court case for first time
Freelancer paid about £400 for technology to draft documents for £7,000 claim
- Nivroo
Build an AI dropshipping store in 60 seconds - and trade too
- AI Resumma
AI Resumma
- Development of a Novel Risk Prediction Model for Rheumatoid Arthritis-Associated Interstitial Lung Disease (RA-ILD): A Longitudinal Study
Background: Interstitial lung disease (ILD) is one of the most common and potentially most devastating extra-articular complication of rheumatoid arthritis (RA) and is associated with substantial morbidity and mortality. However, reliable tools for the early identification of ILD in patients with RA remain limited. This study aimed to identify plasma protein biomarkers of RA-ILD and develop an interpretable machine learning model for risk prediction using data from the UK Biobank. Methods: We first evaluated the association between baseline RA and the risk of incident ILD in the UK Biobank using Cox proportional hazards models. Mendelian randomization analysis was then performed to investigate the potential causal relationship between RA and ILD. Finally, we analyzed 2,920 plasma proteins measured using the Olink platform in 781 eligible RA patients. Proteins associated with ILD risk were identified using Cox proportional hazards models and subsequently used to construct eight machine learning models. Model performance was assessed using the receiver operating characteristic curve (ROC) and decision curve analysis. The best-performing model was further interpreted using Shapley additive explanations (SHAP) to evaluate feature importance. Results: Compared with participants without RA, Patients with baseline RA had a significantly higher risk of developing ILD (Hazard ratio: 4.425, 95% CI: 3.549,5.518). The MR supported a potential causal association between RA and ILD (Odds ratio: 1.227, 95% CI: 1.121,1.343). Among the eight machine learning models, the CatBoost model showed the best performance, achieving an area under the curve (AUC) of 0.884 (95% CI: 0.773,0.996). The SHAP analysis identified LAG3, NPC2, and LAMP3 are the three most important plasma protein predictors of ILD development in patients with RA. Conclusion: Plasma proteomics combined with machine learning may provide a promising approach for identifying biomarkers and predicting ILD risk in patients with RA. LAG3, NPC2, and LAMP3 may serve as candidate biomarkers for RA-ILD and warrant further validation. Keywords: Rheumatoid arthritis, Interstitial lung disease, Mendelian randomization, Machine learning, Plasma proteins.
- Evidence-guided AI regularization for suicidal ideation prediction in pediatric bipolar disorder
Background: Suicide prediction models in psychiatry often rely on purely data-driven feature selection, which can produce unstable and clinically opaque predictor sets in modest-sized samples. We developed Evidence-Based AI LASSO (EBAL), an evidence-guided regularization framework that incorporates curated clinical evidence into feature-specific penalty factors for interpretable prediction. Methods: Baseline data from 136 youth with confirmed bipolar spectrum disorder in the Greater Houston Area Bipolar Registry were analyzed using 20 candidate clinical predictors. Forty higher-level evidence documents on suicidality and related predictor domains were curated through a structured evidence synthesis workflow and indexed as an auditable evidence corpus. An open-weight large language model assigned feature-specific penalty factors using a prespecified scoring rubric, and these penalties were used to fit a weighted LASSO model. EBAL was compared with a standard evidence-agnostic LASSO using nested leave-one-out cross-validation. Results: For suicidal ideation, EBAL achieved an AUROC of 0.768, balanced accuracy of 0.757, sensitivity of 0.758, and specificity of 0.757. The standard LASSO achieved an AUROC of 0.760 and balanced accuracy of 0.715. EBAL improved balanced accuracy (+0.042, p=0.010) and Matthews correlation coefficient (+0.079, p=0.010), while retaining fewer stable predictors than standard LASSO (11/20 vs 18/20). The strongest positive predictors were current depressed mood, duration of mood disorder illness, and comorbid generalized anxiety disorder. For suicidal behavior, both models performed near chance and retained all candidate predictors. Limitations: The study was cross-sectional, single-site, and modest in sample size, with no external validation cohort. Conclusions: EBAL produced a sparser and more clinically coherent model for suicidal ideation in pediatric bipolar disorder, but did not improve prediction of suicidal behavior. These findings support evidence-guided regularization as a transparent strategy for aligning psychiatric prediction models with prior clinical knowledge while preserving interpretability.
- Three multimodal large language models fail at clinically actionable breast pathology in three different directions
Background. Breast cancer treatment depends on histopathological features, such as grade and receptor-defined subtype; however, specialist pathologist access is constrained when the workforce is limited. Commercial multimodal large language models (MLLMs) accept hematoxylin and eosin (H&E) image tiles through paid interfaces without local hardware or fine-tuning. However, prior pathology evaluations addressed only coarse tasks. Whether they reach treatment-determining accuracy and whether vendors agree remain unclear. Methods. We aimed to evaluate three vendor-designated flagship MLLMs (Claude Sonnet 4.6, Gemini 2.5 Pro, GPT-5.5) in 427 invasive breast cancer cases. Each case went to all three with identical H&E tiles and prompts, and the subtype was inferred in the second call. The reference was an institutional sign-out report of an immunohistochemistry-derived subtype. We calculated the concordance, sensitivity, specificity, Cohen's kappa, and pairwise McNemar and Bowker tests. Findings. Claude ranked highest by raw histologic-type concordance but lowest by kappa, classifying all 23 lobular and seven micropapillary carcinomas as invasive breast carcinoma of no special type. The models anchored the Nottingham grade to three modal grades. None of the models reliably identified human epidermal growth factor receptor 2-positive disease. The failure direction was vendor-specific: Claude and GPT-5.5 were under-detected, whereas Gemini was over-called. Twelve prompt variants (4,056 calls) did not recover sensitivity. Interpretation. No current commercial MLLM reaches deployment-ready accuracy for any treatment-determining feature of breast pathology. As each vendor fails in its own fixed direction, changing vendors alters the type of error rather than removing it; therefore, the value of these models is assistive rather than autonomous. At USD 0.20-0.50 per case, they may serve as supervised draft generators that leave the diagnosis with the pathologist.
- AI Gantt chart maker
AI Gantt chart maker
- Between Patterns and Predictions: Interpretable Latent EEG Representations for Clinical Insights
Electroencephalography (EEG) captures rich brain dynamics, yet in clinical practice this complexity is often reduced to simplified summaries or categorical labels, limiting its interpretability for decision-making. We tested the hypothesis that a pretrained latent embedding framework, the Universal Map of EEG (UM-EEG), can preserve clinically meaningful structure across heterogeneous datasets and provide a generalizable representation of brain states. We applied UM-EEG, without retraining, to three independent cohorts spanning distinct clinical contexts: long-term EEG recordings from cardiac arrest patients (n = 576), subarachnoid hemorrhage (n = 100), and routine clinical EEG recordings containing physiological and pathological patterns (n = 141). EEG segments were projected into a shared 128-dimensional space anchored by expert-derived reference states, including wakefulness, sleep stages, ictal-interictal continuum activity, and burst suppression. Across datasets, favorable outcome or physiological recordings were consistently located closer to healthy reference states, whereas poor outcome and pathological recordings shifted toward pathological regions of the embedding space. Trajectory-derived geometric and temporal features discriminated outcome in cardiac arrest (ROC-AUC 0.83) and subarachnoid hemorrhage (ROC-AUC 0.76), and distinguished physiological from pathological routine EEGs (ROC-AUC 0.93). In routine EEG, similarity relationships derived from embedding trajectories correlated with those derived from structured clinical reports, indicating that the latent space recapitulates clinically relevant organization. These findings show that a fixed, semantically structured EEG embedding generalizes across etiologies and recording settings, enabling prognostic stratification and contextual interpretation while preserving the relational structure of brain states.
- An integrated AI-microfluidic platform reveals the broad persistence and developmental potential of rare sperm in non-obstructive azoospermia
Non-obstructive azoospermia (NOA) represents the most severe form of male infertility, severely limiting a patient's prospects for biological fatherhood when surgical retrieval fails. However, the true biological limits of NOA remain obscured by the inherent limitations of conventional gamete recovery protocols: standard centrifugation frequently causes substantial cell loss, masking extremely rare sperm, while surgical interventions are constrained by spatial sampling biases. Here we report SpermSeek, an integrated AI-guided microfluidic platform for real-time, non-destructive isolation of single sperm directly from semen. Operating at scalable throughput (0.36 mL/h), the system achieves 98.3% detection precision and a 95.5% target encapsulation efficiency, suppressing background debris. In a 59-patient NOA cohort, SpermSeek detected morphologically identifiable sperm in 64.4% (38/59) of cases, spanning diverse genetic etiologies, including AZFb/c microdeletions, and severe histopathological phenotypes, such as Sertoli-cell-only syndrome (SCOS). Notably, among a sub-cohort of 41 patients who remained consistently sperm-negative despite prior medical or micro-TESE interventions, our platform identified gametes in 53.7% (22/41) of these cases. Comprehensive safety profiling in healthy human donors and wild-type mice confirmed that processed sperm retain high DNA integrity and epigenomic concordance (r=0.98), supporting transgenerational developmental stability in mice. Furthermore, in a 26-patient validation cohort, SpermSeek recovered rare sperm in 11 cases. Utilizing gametes from a subset (n=5), we demonstrated their capacity to support early human embryogenesis, yielding high-quality cleavage-stage embryos with confirmed genomic euploidy. This work establishes a highly sensitive framework for re-examining the biological limits of human spermatogenesis, laying the foundation to expand autologous reproductive options for patients refractory to conventional retrieval protocols.
- CVClaw AI
Convert job description to ATS-optimized Resume in seconds.
- MyPhotoStudio
AI-powered photo sharing & studio CRM platform
- Groq Raises $650 Million to Aid in Pivot After Nvidia Deal
Groq Inc. raised $650 million in a new funding round aimed at expanding its data center capacity and helping the one-time chip startup become a provider of artificial intelligence computing.
- Inference chip startup Groq raises $650M to grow its cloud platform
Seven months after inking a $20 billion chip licensing deal with Nvidia Corp., Groq Inc. today announced that it has raised $650 million in funding. Growth investment firm Disruptive and hedge fund Infinitum led the round. Groq has developed a chip design called the LPU that’s specifically optimized for artificial intelligence inference workloads. In December, […] The post Inference chip startup Groq raises $650M to grow its cloud platform appeared first on SiliconANGLE .
- Google DeepMind signs AI research deal with film studio A24
Google DeepMind signs AI research deal with film studio A24 Reuters
- Google Deepmind and A24 team up on AI filmmaking research
Google Deepmind and film studio A24 are entering a long-term research partnership. Google is also investing roughly $75 million in A24, according to the Wall Street Journal. The article Google Deepmind and A24 team up on AI filmmaking research appeared first on The Decoder .
- Google DeepMind and A24 announce first-of-its-kind research partnership
Today, Google DeepMind and A24 are announcing a first-of-its-kind partnership focused on research. The collaboration pairs a world-leading research lab with the industry…
- Google’s $75M stake in A24 follows a 50% funding increase in AI content creation startups
Google’s $75M stake in A24 follows a 50% funding increase in AI content creation startups PitchBook
- Google invests $75 million in A24 as DeepMind launches AI filmmaking research partnership
Google is investing roughly $75 million in A24, the independent studio behind recent hits including Backrooms and Obsession, as part of a new AI research partnership between the studio and Google DeepMind. The deal, first reported by the Wall Street Journal, marks Google’s first equity stake in a film studio. The partnership gives A24 filmmakers […] This story continues at The Next Web
- Indie Darling A24 Takes $75 million From Google for ‘AI Research’
Whatever that means.