AI News Archive: July 7, 2026 — Part 28
Sourced from 500+ daily AI sources, scored by relevance.
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- Information Limits and Attractor Dynamics in Economies of Frontier LLM Agents: A Pre-Registered Test
We report a pre-registered, two-part experiment on small economies of frontier language-model agents (Claude Opus 4.8), testing two quantitative predictions about coupled multi-agent systems: an information-theoretic capacity region for wealth growth under market coupling, and a mean-field residual-...
- MCP-Enabled Agentic AI for Autonomous IPoDWDM Network Lifecycle Automation
This demo presents an MCP-enabled agentic AI architecture for autonomous control of vendor-agnostic IPoDWDM networks. We demonstrate live end-to-end lifecycle multi-layer automation and closed-loop control using GNPy and telemetry, validated on a real testbed.
- The Impact of Security and Privacy Controls on Users' Emotional Engagement with Generative AI Chatbots
Chatbots powered by generative AI (e.g., OpenAI's ChatGPT and Google's Gemini) are increasingly being appropriated for emotional support and companionship. These tools offer a suite of security and privacy (S&P) controls, including model training opt-outs and memory toggles, yet how the presence of ...
- DS-MTNet:Structured Multi-Task EEG Decoding for Human-Machine Collaboration
Current human-machine collaboration (HMC) systems rely on environment-facing sensors to observe visible actions and scene states, but the internal perceptual, intention-related, and state-related processes of operators remain insufficiently integrated into machine perception. Electroencephalography ...
- BlossomPsy: A User-Centric AI System for Adaptive and Engaging MBTI Personality Assessments
There has been growing public interest in understanding personality traits and emotional characteristics, as such knowledge helps individuals better accept themselves and manage negative emotions. While professional personality scales remain the standard tool for assessment, they are often perceived...
- Agents That Teach: Towards Designing Incidental Learning Back into AI-Assisted Software Development
AI coding agents are rapidly reshaping how software is built, with developers increasingly delegating substantial coding tasks to autonomous agents in pursuit of higher productivity. While these gains are real, they come at the cost of incidental learning. Developers historically acquired informal k...
- Prompt Coach: An Empirical Evaluation of an Agentic Tutor for Learning Prompt Engineering in Software Development
Prompt engineering has emerged as a critical yet undertaught skill for software developers, one that traditional learning approaches are ill-equipped to support given its evolving, interactive, and context-dependent nature. In this paper, we introduce Prompt Coach (PC), an agentic tutor that helps d...
- PERSONAJUDGE: Simulating Individual Human Preference Judgments with Evaluator-Specific Demonstration Data
Large language models increasingly serve as judges in AI evaluation, but current approaches rely on consensus preferences that ignore individual evaluator variation. We propose a novel simulation approach that combines categorical judgments with evaluator-specific auxiliary data--retrospective reaso...
- Evaluating Fine-Tuning and Metrics for Neural Decompilation of Dart AOT Binaries
Neural decompilation is increasingly studied as a code-generation problem, yet its evaluation methodology remains underdeveloped for modern languages. We present a systematic empirical study of fine-tuning effectiveness and metric validity for Dart Ahead-of-Time (AOT) neural decompilation. We evalua...
- Context-to-Execution Integrity for LLM Agents
Language-model agents read attacker-writable context to solve tasks. Tool execution needs a separate authority check for protected sink fields, sink-interpreted payloads, and the invocation event. Context-to-Execution Integrity (CXI) is an execution-boundary system for this setting. Policies mark pr...
- Beyond the Syntax: Do Security Experts Trust LLMs for NIDS Rule Engineering?
As network threats evolve, manual NIDS rule engineering has become a critical operational bottleneck. While Large Language Models (LLMs) show promise for automating this process, their ability to produce production-ready rules remains unvalidated. This paper presents a human-centered investigation i...
- Code-Level Cost Function Generation for Spatial Image Steganography Using RAG-Enhanced Large Language Models
Designing cost functions of adaptive steganography traditionally requires extensive manual tuning, while deep learning methods lack interpretability. Although large language models (LLMs) offer an automated alternative via evolutionary generation, they often violate domain specific mathematical cons...
- Beyond Refusal: A Same-Lineage Study of Aligned and Abliterated LLMs for Vulnerability Analysis
Large language model (LLM)-assisted software security operates at a difficult boundary: the vulnerability-analysis terminology needed for legitimate code review, triage, and repair can closely resemble terminology associated with misuse. Existing safety and cybersecurity evaluations are difficult to...
- FDIFormer:Protocol-Aware Transformer Learning for False Data Injection Attack Detection in Smart Grid Networks
Smart grids use communication networks and intelligent electronic devices for reliable, automated power system operation. As these systems become more interconnected, they are increasingly exposed to cyberattacks such as message tampering, false command injection, and denial-of-service attacks. A pa...
- From Regression to Prior-Aware Inference: Solving the ILWE Family in Randomness Leakage Attacks against ML-DSA
ML-DSA is a representative lattice-based signature scheme standardized by NIST. It relies on signing randomness and rejection sampling to ensure that released signatures are statistically independent of the secret key. Practical implementations, however, may leak partial information about this rando...
- MSCENet: A Multi-Scale Correlation Enhanced Network for Anomaly Detection
In the field of multivariate time series anomaly detection, against the backdrop of increasing data complexity and complex dependencies across multiple temporal scales, traditional methods often struggle to simultaneously capture temporal dynamic features and intricate inter-series correlations. To ...
- Unicode TAG-Block Concealment of Tool-Metadata Payloads in the Model Context Protocol: An Approval-View Fidelity Gap Across Three Independent Server Implementations
The Model Context Protocol (MCP) is the dominant way coding agents discover and invoke external tools. A server advertises each tool through a tools/list handshake that returns a name, a natural-language description, and a JSON input schema. The client renders this metadata once, in a one-time appro...
- The Balkanization of Execution-Security Research for AI Coding Agents: Isolation, Access Control, and Time-of-Check-to-Time-of-Use Vulnerabilities
AI coding agents now read repositories, call tools, and execute shell commands with limited human oversight, and a fast-growing body of work studies whether the execution layer around them is actually safe. That literature is scattered. Papers on sandbox isolation, capability and access control, pol...
- AgentTether: Graph-Guided Diagnosis and Runtime Intervention for Reliable LLM Agent Operation
Large language model (LLM) agents are increasingly used for multi-step, stateful tool-use tasks, yet production reliability remains limited. Unlike static software repair, agent repair must recover dynamic trajectories whose early decisions can propagate into later errors and external state changes....
- LogicHunter: Testing LLM Agent Frameworks with an Agentic Oracle
Large Language Model (LLM) agent frameworks such as LangChain, LlamaIndex, and CrewAI have become critical infrastructure powering production AI systems, yet they remain severely under-tested due to fundamental challenges in automated testing. Unlike traditional software, where crashes serve as reli...
- Collaborative Multi-Agent Testing for Emergent Failure Discovery in Autonomous Driving Systems
Autonomous Driving Systems (ADS) can fail because of faults within individual modules as well as from interactions across perception, planning, and control. Yet existing ADS testing research often treats key testing functions, such as perturbation generation, behavioural assessment, and test case se...
- Automating Quality Assessment with NLP of LLM-Generated Defeaters
High-integrity systems, such as autonomous vehicle fleets and large-scale energy infrastructures, rely on structured assurance cases to justify safety claims. To remain valid under evolving operational conditions, such cases must be examined against potential challenges, known as defeaters. While la...
- Large Language Models Have Unreliable Understanding of Software Engineering Terminology
Large Language Models (LLMs) are increasingly used in software engineering (SE), yet there is no systematic study that determines to which degree these LLMs actually understand standardized SE terminology. Lack of such understanding can lead to miscommunication and misunderstanding, both by LLMs con...
- SCOPE: Leveraging Subgoal Critiques for Code Generation
Code generation with large language models (LLMs) remains unreliable because generated programs can appear correct while still violating key semantic requirements in the natural language specification. Existing feedback-based methods improve over coder-only generation, but they often rely on unstruc...
- Detecting Vulnerability-Inducing Commits via Multi-Stage Reasoning with LLM-Based Agents
Detecting vulnerability-inducing commits (VICs) at submission time is critical for improving the security and reliability of software systems. However, this task is highly challenging because it requires reasoning about the semantic impact of code changes from heterogeneous information sources, incl...
- Articulating Assumptions in AI-Generated Scientific Analyses through Task Decomposition
Scientific results produced by LLM generated analysis code must be understandable and reproducible. However, uncertainty can arise at different stages of the process, both in the original natural language specification and in the generated implementation. As a result, even executable code may not pr...
- What Resolve Rate Hides: Trajectory Structure Diagnostics for Coding Agents
Coding agents are ranked almost entirely by resolve rate: whether their final patch passes the target tests. Yet two agents can reach the same outcome through very different processes, and a single pass/fail label says nothing about why a run failed or why an accepted run spent extra steps, time, or...
- SWE-Review: Closing the Loop on Issue Resolution with Agentic Code Review
Coding agents increasingly generate pull requests (PRs) for real-world software issues, yet one-shot PR generation remains open-loop: the PR is proposed without systematic review, diagnosis, or revision. We introduce \textbf{SWE-Review}, a framework for closing this loop with agentic code review. Gi...
- Can Large Language Models Generate Observability-Aware Code?
Recent advances in coding agents have enabled the generation of increasingly complex software systems. While existing evaluations primarily focus on functional correctness, production systems must expose failure evidence to support observability. In this paper, we present a systematic study of obser...
- Learning-based Physics-Constrained Neural Kernel for Sound Field Estimation With Source-Position-Dependent Directional Weighting
A learning-based physics-constrained neural kernel for sound field estimation is proposed. Sound field estimation aims to estimate the spatial distribution of an acoustic field from a discrete set of microphone measurements, which have a wide range of applications. Among existing sound field estimat...
- Goodbye Equal Error Rate, Hello Local Information Disclosure: Evaluating Voice Anonymisation against 1-to-N Linkage Threats
Voice anonymisation aims to protect speaker identity. Currently, its empirical privacy evaluation heavily relies on the Equal Error Rate (EER). Originally designed for biometric verification, EER aggregates scores globally, implicitly assuming an attacker is only trying to verify if two specific voi...
- Few-Shot Class-Incremental Audio Classification Using Pseudo-Incrementally Trained Embedding Learner and Continually Updated Stochastic Classifier
Few-shot Class-incremental Audio Classification (FCAC) aims to progressively recognize incremental classes with few tagged samples and meanwhile memorize base classes. To achieve satisfactory FCAC performance, the model needs to have high stability (memorizing base classes) and strong plasticity (ad...
- Learn to Pool: Lightweight Fine-Tuning for Flexible Multi-Vector Compression
Late interaction models have shown strong generalization capabilities, often outperforming much larger dense embedding models. One challenge to their widespread deployment is the large number of token vectors they produce per document and the associated storage and memory costs. Pooling tokens at in...
- Faithful or Findable? Evaluating LLM-Generated Metadata for RDF Dataset Search
Dataset search depends heavily on metadata, making LLM-generated metadata a consequential form of synthetic content in retrieval systems. We study six metadata-generation settings for RDF datasets, ranging from simple rewriting to profile-grounded and agentic graph-based generation, and evaluate the...
- Retrieving a Set, Not Independent Passages: Set-Level Compatibility Learning for Efficient Set Exploration
Multi-hop question answering and retrieval-augmented reasoning require selecting evidence passages that are jointly useful for answering a query. However, most retrievers still score passages independently or make locally supervised sequential decisions, which can fail when evidence usefulness depen...
- Uncertainty-Aware Cross-Modal Remote Sensing Image-Text Retrieval via Evidential Learning
In cross-modal remote sensing image-text retrieval (CMRSITR), test-time remote sensing (RS) images and textual descriptions may deviate from well-curated benchmark conditions due to sensor- and atmosphere-related image degradations and text-side RS-vocabulary heterogeneity. Under such non-ideal cond...
- Quantifying and Expanding the Theoretical Capacity of Late-Interaction Retrieval Models
Late-interaction retrieval models that use the MaxSim similarity function have shown strong empirical performance, often outperforming single-vector dense and sparse retrieval models. Despite these empirical findings, little is known about the theoretical representation power of MaxSim and how it co...
- SCOReD: Student-Aware CoT Optimization for Recommendation Distillation
Chain-of-thought (CoT) distillation in the recommendation domain is a necessary precursor to RL training, but raw teacher traces are ill-suited to this task. Large teachers approach the recommendation task with unusually high reasoning uncertainty, repeatedly rechecking their answers without revisin...
- Deep-Interact Studio: An Interactive Deep Learning Model Building Platform for Biomolecular Interaction Prediction
Motivation: Deep learning has rapidly become essential for predicting biomolecular interactions; however, most web-tools expose only a single, pre-built model with a fixed, non-configurable architecture that users cannot redesign, retrain on their own data, or compare; they are typically dedicated to one interaction type and often one species, and report prediction scores with little interpretability. These constraints force researchers across several disconnected, single-purpose tools and limit the flexibility, reproducibility, and long-term usability of existing platforms. Results: We present Deep-Interact Studio, a unified, web-based deep-learning platform that addresses these limitations by shifting interaction prediction from a model-centric to a user-driven, comparative, and interpretable paradigm. Within a single interface spanning all four interaction classes, namely protein-protein, drug-target, RNA-protein, and protein-DNA, users design their own model architectures layer by layer, configure training hyperparameters, and train them on their own data, including custom, species-specific datasets. Multiple user-built models can then be trained under identical conditions and compared side by side at both the training and inference levels, while integrated interpretability, including SHAP-based feature attribution, embedding-space visualization, and interaction hub analysis, turns predictions into auditable, mechanistically grounded results. Deep-Interact Studio is, to our knowledge, the only such platform to combine fine-grained per-layer model customization with multi-model comparison and interpretability, offering a flexible and transparent alternative to fixed, single-purpose tools.
- Neural correlates of glioma progression using implanted neural interfaces
High-grade glioma is an incurable brain cancer with a median survival of approximately 14 months. Over the last 50 years, small improvements in patient outcomes have been overshadowed by significant progress in most other cancers. Yet, emerging research has revealed that neural circuits play an active and central role in driving glioma growth and proliferation, highlighting the nervous system as a promising avenue for both disease monitoring and therapeutic intervention. Here, we present a platform for chronically monitoring tumor progression using neural recordings in freely behaving mice with gliomas. Using this platform across multiple mouse strains and glioma models, we show that neural recordings can accurately track tumor progression in vivo. Cancer progression was consistently associated with elevated gamma-band neural activity in the tumor microenvironment across both adult glioblastoma (GBM) and pediatric diffuse intrinsic pontine glioma (DIPG) cancer models. Interestingly, lower frequency neural activity exhibited distinct, cell-line specific changes over time: GBM models exhibited decreases in low frequency neural activity whereas DIPG models exhibited increases over time. Finally, using machine learning models applied to chronic neural recordings from tumor-bearing mice treated with or without standard-of-care chemotherapy (temozolomide for GBM), we accurately predicted tumor burden as inferred through in vivo bioluminescence imaging. By fitting low-dimensional mathematical models to gamma-band neural trajectories, we could further predict individual tumor growth rates over a 5-week period with high accuracy. These results establish that pathological neural-tumor interactions can be harnessed to monitor glioma progression in vivo. Coupling this monitoring capability with therapeutic electrical stimulation in the same device could open up a new class of implantable, closed-loop neurotechnologies with the potential to transform glioma treatment.
- Proliferative and Motile Cell Interplay in Glioma Invasion: Go-or-Grow Switching Caps the Invasion Speed
Diffuse gliomas are deadly because the individual tumor cells invade - they travel far from the imageable mass, so it is impossible to remove the tumor completely. On the cellular level, glioma cells seem to be in either a "go" state (in which they do not divide) or a "grow" state (in which they do not migrate). We investigate what this tiny choice has to say about the large-scale speed of the invasion front and whether the implication is sufficiently strong to rule out the classical description of the Fisher-Kolmogorov-Petrovsky-Piskunov (Fisher-KPP) type, in which a single phenotype migrates and proliferates. We derive a two-phenotype reaction-diffusion model with density-dependent switching, and we prove the cooperative (quasi-monotone) structure and the associated comparison principle and study travelling-wave solutions of the model. A leading-edge linearization gives minimal front speed as minimizer of an explicit dispersion relation, and direct simulation verifies the predicted speed. In the experimentally relevant fast switching limit, we find a closed-form expression for the speed, that is, we obtain an effective Fisher-KPP equation with rescaled diffusivity and growth rate, with the fractions of the phenotypes. The "go-or-grow" (GoG) front can move at a maximum speed of half the Fisher speed for the same single-cell motility $D$ and proliferation rate $r$, which occurs only when the cells divide their time equally between the two phenotypes. This bound is directly testable: measurement of the front speed, plus independent determination of $D$ and $r$, discriminates the two hypotheses, and in the GoG case, yields recovery of the phenotype balance. We then extend the result to anisotropic (DTI-informed) invasion along white-matter tracts and discuss implications for understanding clinical measurements of growth rate.
- SupeRJump: Determining normal and leukemic differentiation fate through semi-supervised jump diffusion modeling
Single cell RNA-seq (scRNA) has provided unprecedented resolution into cellular and clonal heterogeneity. Computational approaches have enabled recovery of differentiation dynamics, yet current approaches do not evaluate discontinuous differentiation processes present in malignant leukemia. To address these gaps, we developed SupeRJump: a jump-drift-diffusion based supervised cell-fate model (https://github.com/namwob44/SupeRJump/). We deploy this approach in human bone marrow, murine aging hematopoiesis, and lentivirally barcoded mouse models of acute myeloid leukemia. Our framework introduces a semi-supervised pseudotime strategy to fit a jump-drift-diffusion model and batch correction for lineage fate predictions from absorbing Markov chains. We introduce metrics to quantify cell skewness toward particular lineages, transitions through intermediate progenitor states toward terminally differentiated states, and discontinuous transition dynamics. We use these metrics to identify cells preferentially biased for differentiation, their underlying transcriptional networks, and gene programs responsible for differentiation discontinuity.
- Judging the reasons for fixations: A direct experimental method to assess the contribution of saliency and semantic factors to gaze control
Current debates regarding the relative contribution of saliency versus semantics to gaze control often rely on comparing the predictive power of saliency and meaning maps. We argue that such indirect, global approaches are fundamentally limited because fixations arise from heterogeneous, local causes that are conflated in whole-scene comparisons. To substantiate this claim, we used a direct method where participants explicitly identified the reasons for fixation at specific clusters of high fixation density, distinguishing between low-level saliency and various semantic categories, as well as the most important one. The obtained judgments revealed that multiple factors contribute simultaneously to gaze control. Although their influence varied across fixation clusters, semantics generally dominated saliency. Notably, abstract semantic categories, particularly "unknown/unusual," proved important, highlighting the role of prior knowledge and novelty besides personal relevance in guiding attention. To interpret these findings in the context of existing models, we propose a framework distinguishing between processes highlighting interesting locations in the image from a sampling strategy translating this information into scanpaths. Within this framework, classic saliency and meaning maps are viewed as restricted inputs to the strategy, whereas deep learning-based models (e.g., DeepGaze IIE) are more general and may also implicitly encode aspects of the strategy itself. Consistent with this, we found that the predictive performance of DeepGaze IIE varied less significantly with the specific reasons for fixation than that of classic saliency and meaning map approaches.
- Opioid- and NMDA-receptor-dependent neural plasticity mediates long-term analgesia from motor cortical stimulation
Exogenous opioids that activate mu-opioid receptors (MORs) in nociceptive circuits mediate transient pain relief lasting minutes to hours but have more limited utility for treating chronic pain. By comparison, electrical or magnetic stimulation of the motor cortex can induce pain relief lasting weeks, for which the underlying mechanisms have remained unclear. Here we report an unconventional role for endogenous opioidergic signaling in the rapid induction of long-lasting analgesia from motor cortical stimulation, which triggers opioid-peptide-dependent neural plasticity in the rostral ventromedial medulla (RVM), a key node in the brain's descending pain control pathways. To dissect the circuit and cellular bases for these effects, we created a miniaturized, millimeter-sized device allowing focal, non-invasive transcranial magnetic stimulation (TMS) of the mouse motor cortex. In mice with chronic neuropathic pain, reflexive and affective pain behaviors diminished for 1-2 weeks after one session of TMS treatment. Chemogenetic and optogenetic manipulations showed that motor cortical layer 5 pyramidal neurons with axonal projections to the RVM mediated TMS-induced pain relief. High-density electrophysiological recordings revealed that TMS treatment shifted the balance of RVM activity between pain-ON and pain-OFF neurons to a state promoting greater suppression of pain. Genetic and neuropharmacological manipulations revealed that NMDA-receptor-dependent signaling and MOR activation by endogenous opioid peptides in the RVM jointly mediate the long-lasting analgesia induced by a transient bout of TMS. Strikingly, enkephalinase inhibition in the RVM during TMS treatment enhanced the amplitude and duration of analgesia, showing that transiently boosting endogenous opioidergic signaling during TMS increases analgesia-conferring plasticity. In accord, re-analyses of data from human subjects with chronic pain support the idea that opioid administration amplifies analgesia from motor cortical TMS. Overall, our results showcase miniaturized TMS devices as versatile tools for basic and translational neuroscience and detail a hybrid, long-range neural network and NMDA- and opioid-receptor-dependent plasticity mechanism for durable pain relief. These findings point the way to mechanistically grounded, synergistic neurostimulation and drug therapies for brain diseases and disorders that jointly target neural circuit and molecular signaling pathways.
- Opposing GIPR brainstem circuits differentially control feeding behaviour
Central glucose-dependent insulinotropic polypeptide receptor (GIPR) signalling is required for the efficacy of GIP-based obesity therapeutics, yet how distinct subpopulations of GIPR neurons shape appetite remains undefined. Here we show that GIPR neurons in adjacent brainstem nuclei, the area postrema (AP) and nucleus tractus solitarius (NTS), exert opposing control over ingestion. We find GIPRAP neurons dampen post-ingestive satiation, permitting hyperphagia, whereas GIPRNTS neurons are anorectic. In line with this model, we show Gipr expression in AP, but not NTS, neurons is necessary for appetite suppression following GIPR antagonism. Additionally, we reveal that GIPR neurons in the AP and NTS occupy distinct gut-brain circuits, and are differentially sensitive to obesity-driven circuit remodelling. These data offer a framework for understanding how current GIPR agonist and antagonist strategies elicit weight loss.
- ScaleSurfer: multi-scale anatomical segmentation and parcellation of the human brain
Human brain magnetic resonance imaging (MRI) revolutionized our ability to non-invasively probe individual differences in neuroanatomy. These anatomical scans, in turn, also allow us to accurately localize functional MRI (fMRI) activity. However, extracting anatomical labels and structural characteristics, such as cortical surface area or thickness, is a computationally demanding task, taking on the order of hours per brain volume. This is an intrinsically multi-scale problem given that local image structure defines fine boundaries, whereas accurate assignments depend on broader anatomical context. Here, we introduce ScaleSurfer, a three-dimensional convolutional vision transformer model based on multi-scale learning. Convolution blocks capture local anatomical detail and a transformer bottleneck integrates the distributed spatial context. This approach provides rapid, whole-brain morphometric feature estimation, including volume, cortical thickness, surface area, and curvature. Importantly, ScaleSurfer accomplishes this nearly five orders of magnitude faster than current pipelines, taking 150-500 ms instead of ~5 hours. We validated ScaleSurfer on multiple datasets, showing stable learning across heterogeneous MRI collections, and demonstrate feasibility by training an interpretable Alzheimer's disease classifier that identifies reductions in primarily medial temporal lobe subregions compared to healthy controls. ScaleSurfer positions multi-scale representation learning as a practical route toward faster, anatomically faithful structural MRI processing, whose speed paves the way for nearly real-time anatomical quality control during scanning.
- Determinants of Blood Group Antigen Expression and Prediction of Phenotypes by Machine Learning
Blood group antigens, defined by epitopes on the erythrocyte surface, are central to transfusion safety and maternal-fetal compatibility. While the genetic basis of many clinically relevant blood group antigens is well established, which structural and biophysical parameters determine whether a single-nucleotide variant gives rise to an antigenic phenotype remains unclear. Here, we integrate structural, biophysical, and evolutionary analyses to systematically evaluate features associated with single amino acid substitutions across 24 human protein-based blood group systems. We analyse 319 variants with curated phenotypic annotations alongside 481 control variants, identifying key determinants of null and antigenic phenotypes. Null variants are characterized by high evolutionary conservation, burial within the protein core, loss of hydrophobicity, increased polarity, and a propensity for arginine substitutions. Antigenic variants are also enriched in arginine; however, in contrast to null variants, they tend to occur at less conserved, more solvent-accessible, and structurally flexible sites. Supervised machine learning models trained on structural and biophysical descriptors were applied to distinguish (i) null and (ii) antigenic variants from controls, achieving balanced accuracies of 0.82 and 0.63, respectively. Feature importance analysis identified predicted pathogenicity, solvent accessibility, and evolutionary conservation as the most predictive determinants of null variants, whereas hydrophobicity, conservation, and flexibility dominated antigen prediction. This work establishes a framework linking molecular variation to blood group phenotypes and provides a foundation for predicting the impact of novel missense mutations in transfusion medicine and beyond.
- Clinically derived micro- and nanoplastics uptake drives spatiotemporally confined metabolic stress revealed by bond-selective imaging
Although microplastics and nanoplastics (MP/NP) are pervasive environmental contaminants, our understanding of cellular toxicity remains incomplete, as adverse effects are often attributed to long-term intracellular accumulation, while the spatiotemporal onset of cellular damage remains poorly defined. Here, we employ chemical-bond-selective stimulated Raman scattering (SRS) microscopy and cell models that decouple continuous exposure from intracellular retention to directly visualize clinically derived MP/NP-cell interactions. Cellular stress occurs primarily during MP/NP exposure, accompanied by alterations in lipid droplet (LD) composition. In contrast, following extracellular removal, intracellularly retained MP/NP become largely inert, with recovery of lipid metabolism and cellular functions. Lipidomics identifies arachidonic acid (AA) as a key dysregulated metabolite, and SRS imaging further reveals transient, spatially confined AA enrichment in MP/NP-proximal LDs during uptake. Importantly, phospholipid coating of MP/NP attenuates LD alterations and cytotoxicity while preserving particle internalization, establishing uptake-driven metabolic stress, rather than long-term intracellular retention, as primary source of MP/NP-induced damage.
- A Procoagulant Peptide Analog of the SARS-CoV-2 Nucleocapsid C-terminal Domain
Background: Uncontrolled bleeding complicates trauma, surgery and many medical conditions. While currently available procoagulant therapies (e.g., plasma-derived factors, recombinant proteins, antifibrinolytics) have crucial limitations. Methods: N389 (CQQTVTLLPAADLDDFSC) was synthesized by Fmoc solid-phase chemistry, characterized by HPLC and LC-MS, then tested in normal human pooled plasma in microplate mechanical clot-formation assays using incubated and immediate addition formats. Kinetic parameters (plasma recalcification, PRT; maximum absorbance, Amax) were obtained from absorbance curves fit to four-parameter logistic models. Mixing studies with modified (i.e., aged, adsorbed) plasma probed factor dependence. Results: In plasma coagulation assays activated with 25 mM CaCl2, baseline clotting showed a PRT of 23.74 +/- 0.27 min and Amax of 0.1813 +/- 0.0043 (n = 3), whereas N389 significantly reduced PRT to 8.442 +/- 6.0395 min without incubation (p = 0.0012), further decreased PRT after incubation (p < 0.0001), increased Amax to 0.2523, and retained comparable activity across normal, adsorbed, and aged plasma, in contrast to S1255 which showed a faster but incubation-labile effect with PRT 2.353 +/- 1.3685 min (p = 0.0007) and marked attenuation in factor-depleted and aged plasma. Mixing studies showed N389 activity persisted across normal, aged and adsorbed plasma, consistent with a mechanism that does not require intact plasma coagulation factor profiles (specifically factors II, V, VIII, VII, IX, X). Discussion: Collectively with prior evidence on anionic surfaces, Ca2+-binding Gla domains, and peptide-modulated fibrin polymerization, these results support a model in which N389 functions as a stable, charge-based scaffold that coordinates divalent cations and/or directly nucleates fibrin(ogen), while highlighting limitations of bulk clotting assays and the need for targeted thrombin generation, binding, aggregation, and contact-activation studies. Conclusions: The aspartate-rich peptide N389 is a sustained, factor-independent procoagulant at least in vitro. N389 thus merits further mechanistic and translational evaluation as a synthetic hemostatic agent.