AI News Archive: June 6, 2026 — Part 5
Sourced from 500+ daily AI sources, scored by relevance.
- EnzOracle: Mechanism-aware prediction of enzyme environmental adaptation via a classification-guided mixture-of-experts framework
Industrial biocatalysis increasingly requires enzymes capable of operating under extreme physicochemical conditions, yet most natural sequence data reflect adaptation to mild environments, leading conventional predictive models to suffer from regression-to-the-mean effects in extremophilic regimes. Here we present EnzOracle, a classification-guided mixture-of-experts framework that enables distribution-aware prediction of enzyme melting temperature (Tm), optimal catalytic temperature (Topt), and optimal pH (pHopt) directly from sequence. EnzOracle demonstrated robust predictive accuracy across diverse benchmarks, achieving RMSE of 5.245 for Tm, 11.458 for Topt, and 0.781 for pHopt. Beyond predictive accuracy, we introduce a trait-resolved molecular simulation strategy to evaluate whether EnzOracle-derived attribution patterns correspond to independent physical mechanisms. Across representative systems, attention hotspots mapped onto rigidity-conferring interaction networks for Tm, dynamically preorganized active-site ensembles for Topt, and pH-dependent electrostatic and hydration networks for pHopt. These orthogonal validations indicate that EnzOracle captures transferable biophysical principles of enzyme environmental adaptation rather than merely exploiting dataset-specific correlations, positioning sequence-based learning as a mechanism-aware framework for discovering stability and activity determinants across diverse catalytic landscapes.
- HOPE: Interpretable Histology Analysis with Spatial Omics-Derived Signatures for Precision Oncology
Hematoxylin and eosin (H&E) stained images are fundamental clinical tools for disease assessment. However, even with advanced computational models, their prognostic capabilities remain limited. Spatial omics characterizes tumor microenvironments (TME) in detail yet remains clinically inaccessible due to cost and complexity. In this study, we present HOPE, a lightweight framework that learns TME signatures from paired H&E and spatial omics data during training, then applies these to H&E alone at inference. Leveraging H&E foundation models, HOPE consistently outperforms identical architectures trained without spatial omics guidance across cancer types and cohorts. It further generates interpretable annotations of TME signature on H&E regions, stratifying patients into biologically coherent groups with different prognostic outcomes. HOPE establishes a practical route to translate high-content spatial omics discoveries into scalable, clinically deployable tools.
- Multimodal neuroimaging approach for cognitive impairment in Alzheimer disease
PURPOSE: Alzheimer disease (AD) is associated with cognitive impairment, brain atrophy, and elevated amyloid-beta and tau. The study aimed to characterize regional atrophy associated with elevated amyloid-beta and tau, as measured by [18F]florbetapir (FBP) and [18F]flortaucipir (FTP) positron emission tomography (PET), respectively, and determine whether combining PET and atrophy data improves the prediction of cognitive impairment. METHODS: Alzheimer Disease Neuroimaging Initiative data (n = 381) were retrospectively analyzed. PET results were correlated with cortical thickness, gray matter (GM) volumes, Mini-Mental State Examination, and Montreal Cognitive Assessment. Linear/logistic regression and area under the curve (AUC) were used to evaluate for significant correlations and compare performances in distinguishing cognitive impairment, respectively. RESULTS: Incremental loss of cortical thickness and GM volume was observed from FBP-/FTP- (n = 205) to single PET-positive (FBP+/FTP-, n = 133; FBP-/FTP+, n = 5) and FBP+/FTP+ (n = 38) groups, particularly in the temporal and parietal lobes. FBP+/FTP+ showed the most severe cortical thickness loss in the entorhinal cortex, temporal lobe GM atrophy, and cognitive impairment. Adding brain atrophy as the third variable resulted in higher odds ratios and improved AUCs for cognitive impairment, with FBP+/FTP+/temporal GM or entorhinal cortical atrophy+ demonstrating the strongest associations with cognitive impairment. CONCLUSION: A multimodal approach combining PET and MRI may help improve the assessment of cognitive impairment in AD.
- AutoClip: AI-Guided TEE Semantic Segmentation for TEER A Proof-of-Concept Study
**Abstract** **Background:** Transcatheter edge-to-edge repair (TEER) is an established treatment for mitral regurgitation but remains highly dependent on operator experience and complex transesophageal echocardiography (TEE)-guided intraprocedural imaging. Artificial intelligence (AI)-based semantic segmentation may improve procedural reproducibility and intraprocedural guidance; however, no TEER-specific segmentation framework has been reported. **Objectives:** To develop and evaluate AutoClip, a clinician-driven AI-guided TEE semantic segmentation model designed for simultaneous delineation of mitral valve anatomy and in-vivo TEER device components. **Methods:** A retrospective proof-of-concept study was conducted using 987 intraprocedural TEE frames derived from 10 video clips in 3 patients undergoing MitraClip G4 implantation. Seven semantic labels, including mitral leaflets and device components, were manually annotated using ITK-SNAP. Following standardized preprocessing and region-of-interest extraction, an Attention U-Net architecture was trained frame-wise on bicommissural and corresponding X-plane TEE views. Model performance was assessed using mean intersection-over-union (IoU) and Dice coefficient on an independent test set. **Results:** The Attention U-Net demonstrated improved sensitivity to small device structures compared with conventional U-Net architectures. Preliminary training performance achieved a mean IoU of approximately 0.93, while independent test performance reached a mean IoU of 0.46 across foreground classes. Qualitative assessment demonstrated feasible simultaneous segmentation of mitral leaflets, clip arms, grippers, and delivery shaft during TEER procedures. **Conclusions:** AutoClip represents a proof-of-concept TEER-specific TEE semantic segmentation framework initiated through a clinician-oriented workflow without formal computer science expertise. Although preliminary accuracy remains modest due to limited sample size, this study establishes a reproducible pathway for future AI-assisted intraprocedural guidance systems and larger multicenter development efforts in structural heart interventions.
- Beyond Injection Detection: A Positive-Security Prompt Firewall that Closes the Scope and PHI Gap SOTA Classifiers Miss in Healthcare
Large language models embedded in autonomous agents process trusted instructions and untrusted data in one context window, leaving them open to direct and indirect prompt injection. In healthcare this is not hypothetical: a 2025 JAMA Network Open study found commercial medical LLMs followed injected instructions in 94.4% of simulated patient encounters, including life threatening recommendations . Yet the clinically decisive problem we quantify here is different. Most real clinical threats protected health information PHI exfiltration, cross patient access, bulk export, out of scope advice are fluent, legitimate looking requests that carry no attack signal, so even a state of the art injection detector passes them. Existing runtime guardrails trade safety against latency: model based auditors are accurate but add hundreds of milliseconds of Python inference, while lexical filters are fast but blind to obfuscated or semantically disguised payloads. We present QFIRE, an inline, provider agnostic prompt firewall implemented as a single self contained Rust toolchain proxy, CLI, and benchmark harness. QFIRE combines three mechanisms: (i) positive security scope constraints, which restrict a model call to a declared natural language purpose and block out of scope drift even when no overt attack token is present; (ii) an asynchronous detector graph that runs N rules and their detector nodes concurrently, cheapest checks first; and (iii) a de obfuscation pass that decodes Base64 hex ROT13, folds homoglyphs and leetspeak, and strips zero width characters before detection. QFIRE ships 106 versioned firewall rules and a dedicated HIPAA Safe Harbor 18 identifier PHI panel, and runs a local DeBERTa v3 injection classifier via embedded ONNX Runtime. On 1968 public prompt injection and jailbreak prompts QFIREs deterministic hybrid attains F1 0.86, statistically tied with Metas state of the art PromptGuard 2 0.86 and above protectai DeBERTa v3 0.83; lexical baselines lag 0.16 to 0.50. Our central result is on QFIRE HealthBench, a new 2000 prompt healthcare benchmark we build and release with real garak and Microsoft PyRIT payloads. There the same PromptGuard-2 recovers only 0.40 recall DeBERTa v3 0.57, because most clinical threats carry no injection signal; QFIREs combined scope plus PHI chain reaches 0.83 recall F1 0.87 at a calibrated 0.08 false positive rate. Generic injection detection, even state of the art, is therefore necessary but not sufficient for healthcare agents. A bare LLM judge also closes most of this static corpus gap F1 0.90; QFIREs contribution beyond static accuracy is auditable determinism, bounded latency, and adaptive robustness, where the bare judge falls to 34 to 59% recall section 5.5. End to end, placing QFIRE in front of a tool using agent over a mock EHR sandbox cuts the agents harmful action rate from 0.38 to 0.00 at a 0.13 benign utility cost. All code, rules, corpora snapshots, and scripts are released, and every table regenerates from a single make paper target against local models with no paid API keys.
- An AI-assisted feasibility evaluation of three photoplethysmography-derived microvascular reactivity signals in MIMIC-IV-WDB v0.1.0
Background. Capillary refill time, an examiner-dependent bedside test of distal microvascular perfusion, has become a resuscitation target in septic shock,1,2,3,4 motivating a continuous surrogate computed from the photoplethysmogram (PPG, the optical waveform the pulse oximeter on every ICU patient already records).5,6,7,8 Objective. We attempted three PPG-derived candidate measures on the MIMIC-IV Waveform Database (MIMIC-IV-WDB v0.1.0) and asked, by inspecting randomly drawn examples, whether each captured its intended physiology before any downstream modeling. Methods. MIMIC-IV-WDB v0.1.09 was linked to MIMIC-IV.10 The signals were a cuff-anchored perfusion-index recovery (reactive hyperemia when the cuff shares an arm with the probe), a slow Mayer-wave-band power ratio of the perfusion index (sympathetic vasomotor tone), and a per-beat diastolic exponential decay time constant (a refill-like recovery time). For each signal we drew 10 random examples at a fixed seed and checked them against a checklist fixed in advance. Each was read by the author and, separately, by MedGemma 1.5, a multimodal medical language model run locally. A synthetic test with a known time constant checked the third signal. Results. The cuff-anchored signal showed the expected occlusion-reperfusion shape on 268 of 6,236 evaluable cuff cycles (4.30%) in 15 of 19 patients, consistent with opposite-limb placement of the probe and cuff. The slow-band ratio returned a stable cohort value, but a clear, stationary peak appeared in only4 of 10 random windows. The per-beat fit met its goodness-of-fit threshold in 10 of 10 beats, yet a cardiac-frequency heuristic flagged a possible fit on the heart-rate oscillation in 7 of 10, and in 5 of 17 patients the time constant lay where an exponential is indistinguishable from a straight line. A 0.5Hz high-pass pre-filter implanted its own approximately 318 ms time constant regardless of truth. The language model tracked the human on clear positives but reported the pattern present on every call it returned, never absent. Conclusions. Two of the three candidate signals did not reflect their intended physiology in most examples, and the third was constrained by sensor placement. Inspecting a few random raw inputs against a checklist written in advance is an inexpensive upstream check before downstream inference on PPG-derived microvascular signals.
- Adapting a Regulation of Craving Magnetic Resonance Imaging Task to Generate Functional Repetitive Transcranial Magnetic Stimulation Targets for the Ventromedial and Dorsolateral Prefrontal Cortex in Treatment-Seeking Participants with Cannabis Use Disorder
Background: Repetitive Transcranial Magnetic Stimulation (rTMS) is a promising treatment across addictive disorders including Cannabis Use Disorder (CUD). Targeting incentive-salience circuitry via the ventromedial prefrontal cortex (vmPFC) and central-executive circuitry via the left dorsolateral prefrontal cortex (LDLPFC) are both promising treatment approaches; however, to date structural targets have predominated whereas functional targeting may allow for more precision. In this pilot trial we adapted a functional Magnetic Resonance Imaging (fMRI) Regulation of Craving (ROC) task to generate fMRI-based rTMS targets in the vmPFC and LDLPFC. Methods: We recruited treatment-seeking participants with moderate or severe CUD as a part of an open-label trial and administered an adapted ROC-task during fMRI following 24-hours of cannabis abstinence. We identified sub-portions of maximal activation of the LDLPFC when participants thought of long-term consequences of cannabis use (Later) and of the vmPFC when participants thought of short-term positive aspects of cannabis use (Now). We hypothesized that our task would generate acceptable rTMS targets in >66% of baseline fMRI scans. Results: A total of 20-participants enrolled in the trial (50%F, age=33.3+9.8) and completed the baseline fMRI. The adapted ROC-task elicited group level activation in the LDLPFC and precuneus in the Later>Now and in the bilateral vmPFC, ACC, and striatum in the Now>Later contrast. Acceptable functional targets resolved in both the vmPFC and LDLPFC in 19 of 20 participants (one participant did not tolerate MRI). Conclusions: The adapted ROC-task elicits activation in incentive salience and central executive circuitry and can feasibly generate rTMS targets when using a cluster selection algorithm.
- BodyMAE: A Surface-Area Aware Masked Autoencoder for Body Composition Estimation from 3D Body Scans
Accurate assessment of body composition is important to risk stratification and management of metabolic, musculoskeletal, and aging-related diseases, yet reference modalities such as Dual-energy X-ray absorptiometry (DXA) are costly and impractical for frequent monitoring. Commodity 3D body scans offer a low-cost, radiation-free alternative, but extracting meaningful and predictive shape features from scans remains challenging due to nonuniform point density, variable body size and cross-device differences. We introduce BodyMAE, a self-supervised, surface-area aware masked autoencoder for metric-scale 3D body scans. The pipeline integrates area-adjusted sampling, a long-range focused encoder, and a lightweight decoder regularized to promote locally uniform reconstructions. Trained and evaluated on 917 paired 3D body scans paired with clinical DXA reports, BodyMAE achieves strong accuracy on fat percentage (root-mean-square error (RMSE) 3.825 percentage points, R^2 0.908), fat mass (RMSE 3.694 kg, R^2 0.968), and lean mass (RMSE 3.608 kg, R^2 0.901), with competitive performance on bone mineral content (RMSE 0.284 kg, R^2 0.754).We also assess feature stability across pretrained baselines, finding higher retrieval accuracy for our representations (Top-1 90.131%). These results indicate that combining metric-aware sampling, long-range relational encoding, and local geometric regularization enables accurate body composition estimation from 3D body scans, as validated by comparisons to DXA-derived measurements.
- Trump's latest memo puts 'most advanced AI in the world' into the military's hands
The memo also prevents companies from altering AI models being used by the military without prior approval.
- Researchers Successfully Trial AI-Designed Vaccine for the First Time
Researchers Successfully Trial AI-Designed Vaccine for the First Time PCMag Australia
- OpenAI and the White House have competing visions for regulating artificial intelligence
OpenAI's new AI guidelines are clashing against the guidance written by the Trump administration.
- What we know about the plan to give Americans an equity stake in AI
OpenAI has proposed a sovereign-wealth-style fund to ease public anxiety about the impact of artificial intelligence
- Claude Mastery: 9 Workflow Hacks
Turn Claude smarter, faster, and sharper instantly
- US says it will speed development and use of AI for national security
The Trump administration said earlier this week that it would ask leading AI developers to voluntarily submit their most capable models for government cybersecurity tests before releasing them to the public, as security fears mount in Washington over powerful new AI systems.
- Almanac Seed
Ship the spec, not the code. An AI builds the app.
- Webstorio
AI Web Builder Platform with Everything Built-in
- OpenAI rolls out Lockdown Mode to protect against prompt injection attacks
OpenAI rolls out Lockdown Mode to protect against prompt injection attacks
- OpenAI Rolls Out Lockdown Mode to Fight Prompt Injection Attacks
OpenAI Rolls Out Lockdown Mode to Fight Prompt Injection Attacks PCMag Australia
- Sriram Krishnan is leaving his role as White House AI advisor
Krishnan is reportedly starting a new institution to continue shaping Trump's AI policy.
- The Trump administration might take an equity stake in OpenAI
President Donald Trump said he's discussing deals "where the American people can benefit from the success of AI."
- Trump AI Policy Adviser Krishnan Is Giving Up White House Role
Sriram Krishnan, a top White House adviser on artificial intelligence, is stepping down from the post.
- Trump Signals Interest in US Owning Stakes in Top AI Labs
President Donald Trump expressed interest in the US government holding equity stakes in leading artificial intelligence developers, saying that he planned to discuss the idea of a partnership with AI companies’ executives as soon as next week. Bloomberg Surveillance Co-Host and Chief Political Correspondent Annmarie Horden joined David Gura and Christina Ruffini on Bloomberg This Weekend to discuss. (Source: Bloomberg)
- White House AI policy adviser Krishnan to leave position
White House AI policy adviser Krishnan to leave position Reuters
- White House AI policy adviser Krishnan to leave position
Krishnan has been involved in Trump administration efforts to create a national framework for regulating AI developments.
- White House AI Policy Advisor Sriram Krishnan to Leave Position
White House AI Policy Advisor Sriram Krishnan to Leave Position The Information
- White House AI policy adviser Sriram Krishnan announces departure from role without giving reason
His departure comes as the president looks at the possibility of the U.S. government acquiring stakes in AI firms
- Ejentum - Reasoning Harness
Stop your AI agent drifting, flattering, and fabricating.
- Trump: U.S. stake in AI giants "could be a beautiful thing"
President Trump surprised tech CEOs by suddenly pushing the idea of the U.S. taking a small ownership stake in AI giants, so the American people share in the upside of what will be trillion-dollar companies. "There's something very interesting about it, where it almost becomes a partnership with the American public," Trump told reporters aboard Air Force One yesterday. "It's like you make them [partners] in this revolution. It would be a beautiful thing. ... It would make 'em rich." Why it matters: OpenAI CEO Sam Altman has pushed this idea with the Trump administration over the past year. Sen. Bernie Sanders (I-Vt.) reignited the conversation this week when he proposed giving the public a "direct ownership stake" in top AI companies via a one-time 50% tax, paid in stock. Of course, industry advocates of the idea would favor giving up much less for an AI public wealth fund — 1-5% stakes have been kicked around. Between the lines: AI is broadly unpopular in the U.S. Some industry leaders, and now clearly Trump, think the technology's image would improve if all Americans participated in this mind-boggling wealth creation. Ahead of the expected stock offerings by Anthropic, SpaceX and OpenAI, Trump said there's " so much money, and it's so big, that there are concepts where pieces could be given to the American public, where the American public essentially becomes a partner ... with the companies." "We'll look into that," Trump said. "We're talking about it, where the American people can benefit from the success of AI. And by doing that, they're gonna like it better ... We're leading China. We're leading everybody in the world with AI, and we want to keep it that way." The backstory: Altman has pushed the concept in private conversations with administration officials, then in a proposal for an AI New Deal, then on Capitol Hill this week when he visited Sanders and leaders of both parties. A " Public Wealth Fund " was one of the provocative ideas in OpenAI's "Industrial Policy for the Intelligence Age," out in April. Between the lines: When a reporter asked Trump about the incongruity of embracing a proposal by Sanders, a democratic socialist, the president touted his economic populism. "As far as economics is concerned," Trump said, "we have certain things that aren't that far apart. People are surprised."
- White House AI policy adviser Krishnan to leave position: Report
Sriram Krishnan, a White House AI policy adviser, is set to depart his role by June's end. He is reportedly considering establishing a new policy institution. This venture aims to bolster the Trump administration's future AI initiatives. The institution is expected to be staffed by engineers. This move signals a significant shift in AI policy focus.
- Sriram Krishnan, Trump's Indian-origin AI brain, to exit White House
Sriram Krishnan, Trump's Indian-origin AI brain, to exit White House
- Trump AI policy adviser Sriram Krishnan to leave position
A top White House artificial intelligence policy adviser on Saturday said he will leave his position at the end of June, marking the exit of a leading figure helping craft policies for frontier technologies. “This journey has been the privilege of a lifetime,” the adviser, Sriram Krishnan, posted on social media. Krishnan did not give a reason for leaving, but wrote in the post he intends to help “tackle some of the large challenges facing America” related to AI. Krishnan has been involved in...
- OpenAI and the Trump administration are negotiating a government stake in the AI startup
OpenAI and the Trump administration are negotiating a direct government stake in the AI startup. The idea is a "Public Wealth Fund" that would pay out directly to American citizens. Senator Bernie Sanders wants to push through a 50 percent tax on AI shares by law. Critics fear the arrangement could create a "too big to fail" dynamic similar to the 2008 financial crisis. The article OpenAI and the Trump administration are negotiating a government stake in the AI startup appeared first on The Decoder .
- Trump wants the American public to own a piece of OpenAI. Nobody knows how that would work.
President Donald Trump said on Thursday that he will likely meet with AI companies at the White House next week to discuss what he called a federal government “partnership” that would let the American public profit from the industry’s success. “There are concepts where pieces could be given to the American public, where the American […] This story continues at The Next Web
- President Trump Seeks Govt. Stake in the AI Pie to Expand Ownership
US President Donald Trump believes that it wouldn’t be a bad idea for all AI companies headquartered in the country to give a stake in their enterprises to the general public. He shared this idea with reporters last night, barely days after signing an executive order that allows the government and some private entities to […] The post President Trump Seeks Govt. Stake in the AI Pie to Expand Ownership appeared first on CXOToday.com .
- White House AI adviser to leave position as Trump weighs stakes in AI firms
White House AI adviser to leave position as Trump weighs stakes in AI firms Nikkei Asia
- Trump says his team will ‘look into’ US taking stake in AI companies
Trump says his team will ‘look into’ US taking stake in AI companies The Straits Times
- Trump AI policy adviser Krishnan is giving up White House role
Trump AI policy adviser Krishnan is giving up White House role The Straits Times
- Donald Trump, Bernie Sanders and Sam Altman are all talking about public ownership in AI
Donald Trump, Bernie Sanders and Sam Altman are all talking about public ownership in AI Toronto Star
- The Trump administration is reportedly in talks about taking a stake in OpenAI
Details for a potential deal haven't been finalized yet.
- MAI-Image-2.5
Generate and edit images with precise scene control
- White House AI policy adviser Sriram Krishnan announces departure from role without giving reason
His departure comes as the president looks at the possibility of the U.S. government acquiring stakes in AI firms
- Donald Trump, Bernie Sanders and Sam Altman are all talking about public ownership in AI
Donald Trump, Bernie Sanders and Sam Altman are all talking about public ownership in AI San Francisco Chronicle
- Donald Trump, Bernie Sanders and Sam Altman are all talking about public ownership in AI
Donald Trump, Bernie Sanders and Sam Altman are all talking about public ownership in AI The Boston Globe
- Donald Trump, Bernie Sanders and Sam Altman are all talking about public ownership in AI
OpenAI CEO Sam Altman has met with Sen. Bernie Sanders to discuss public ownership in AI companies — a meeting that highlighted the tension between AI powerhouses and policymakers.
- Donald Trump, Bernie Sanders and Sam Altman are all talking about public ownership in AI
Donald Trump, Bernie Sanders and Sam Altman are all talking about public ownership in AI Houston Chronicle
- Donald Trump, Bernie Sanders and Sam Altman are all talking about public ownership in AI
Donald Trump, Bernie Sanders and Sam Altman are all talking about public ownership in AI Dallas News
- Donald Trump, Bernie Sanders and Sam Altman are all talking about public ownership in AI
Donald Trump, Bernie Sanders and Sam Altman are all talking about public ownership in AI AP News
- Donald Trump, Bernie Sanders and Sam Altman are all talking about public ownership in AI
OpenAI CEO Sam Altman has met with Sen. Bernie Sanders to discuss public ownership in AI companies — a meeting that highlighted the tension between AI powerhouses and policymakers
- Donald Trump, Bernie Sanders and Sam Altman are all talking about public ownership in AI
Donald Trump, Bernie Sanders and Sam Altman are all talking about public ownership in AI Austin American-Statesman
- Donald Trump, Bernie Sanders and Sam Altman are all talking about public ownership in AI
Donald Trump, Bernie Sanders and Sam Altman are all talking about public ownership in AI Boston Herald