AI News Archive: May 30, 2026 — Part 4
Sourced from 500+ daily AI sources, scored by relevance.
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- A pilot assessment of avian communities and soundscapes along an Amazonian fluvial corridor
Quantifying biodiversity patterns in remote Amazonian ecosystems remains constrained by the limitations of traditional field surveys. We combined passive acoustic monitoring (PAM), machine learning, and ecoacoustic metrics to assess the taxonomic and functional structure of bird communities along a riparian gradient in the Kapawi River, Ecuadorian Amazon. A total of 2,030 recording hours were acquired using 16 Autonomous Recording Units (ARUs) deployed along a river-to-interior forest gradient (0-800 m from the riverbank). Automated detection with BirdNET yielded 92,137 records corresponding to 379 bird species. Species richness was highest at the river edge (325 species), which also harboured the greatest number of unique taxa (71 species), while interior sites showed lower but more consistent local richness. Multivariate analyses showed clear spatial segregation between riparian and interior communities. Despite this turnover, the trophic structure remained highly homogeneous (>90% similarity), dominated by insectivorous and frugivorous guilds. Generalized linear models (GLMs) indicated strong positive associations between avian species richness and key ecoacoustic metrics, with particularly pronounced effects for the Acoustic Diversity Index (ADI) and the Bioacoustic Index (BI). Spatially explicit analyses further demonstrated marked heterogeneity in acoustic structure along the fluvial gradient, reflecting fine-scale variation in soundscape composition. Together, these findings show that riparian habitats structure avian communities primarily at the taxonomic level, while functional organization remains largely conserved across the gradient. This mismatch indicates that biodiversity components respond unevenly to environmental variation, with taxonomic richness being more sensitive than functional composition. Our results underscore the potential of ecoacoustic approaches as scalable, non-invasive tools for detecting spatial patterns in biodiversity and habitat-driven community assembly in tropical systems.
- Explainable machine learning reveals an RBP regulatory logic of exon skipping
RNA binding proteins (RBPs) regulate the life cycle of an mRNA, often through RBP-RNA interactions. This life cycle includes splicing, whereby the intronic sequence of a pre-mRNA is removed and the exons are joined together. However, the patterns of RBP binding that lead to different splicing outcomes are still incompletely understood. Here, we build machine learning models from RBP-RNA binding and knockdown RNA-seq data for over 168 RBPs in two cell lines (HepG2 and K562) to better understand the binding patterns that predict exon skipping, the predominant form of alternative splicing in humans. We show that models trained exclusively on RBP binding patterns are indeed predictive and that a more sophisticated machine learning model (XGBoost) outperforms simpler linear models. In addition, we are able to extract a biologically interpretable logic embedded in these models. We show that SHAP, a machine learning explainability technique, captures activating and repressive behavior of RBP binding that is position-specific. In addition, we find that SHAP values are predictive of changes in unseen splicing events and that SHAP interactions between pairs of RBPs are predictive of protein-protein interactions. Our results demonstrate that using machine learning with interpretability techniques can reveal a regulatory logic of RBP binding. By estimating the impact of an RBP binding site on a splicing event, the SHAP values also provide a directly testable scientific hypothesis. We anticipate that models designed around biological processes and focused on interpretability will yield actionable biological insights both in splicing and genomics generally.
- Large artery phenotypes, cerebrovascular function, and progression of cerebral small vessel disease
Objective: Cranial artery stenosis and dilatation are distinct large artery phenotypes that often coexist with cerebral small vessel disease (cSVD), yet their downstream microvascular functional correlates remain unclear. Methods: In the prospective Mild Stroke Study 3, we recruited patients with lacunar or mild non-lacunar stroke. At baseline, large artery stenosis (LAS), basilar artery dolichoectasia (BADE), and intracranial arterial diameters were assessed. Multimodal MRI quantified cerebrovascular reactivity (CVR), blood-brain barrier (BBB) permeability, plasma volume fraction, and intracranial pulsatility. cSVD markers were evaluated at baseline and 1 year. Associations between large artery phenotypes and vascular function were examined with multivariable regression. Mediation analyses tested whether vascular dysfunction linked large artery pathology to cSVD progression. Results: Among 224 participants (mean age 66.0, SD 11.2 years; 66.5% men), BADE (n=36, 16.1%) was independently associated with lower CVR in normal-appearing white matter (NAWM; {beta} -0.01, 95% CI -0.016 to -0.004, P=0.003). Larger mean intracranial arterial diameter was associated with lower CVR in NAWM and white matter hyperintensities (WMH), while showing a U-shaped association with BBB permeability. LAS (n=46, 20.5%) was unrelated to CVR, BBB permeability, or pulsatility, but was associated with higher plasma volume in WMH. CVR in NAWM partially mediated the association between BADE and both baseline cSVD burden and 1-year progression. Interpretation: Large artery dilatation may serve as a macroscopic signal of small-vessel dysfunction, being associated with lower CVR and altered BBB permeability. Reduced CVR in NAWM partially mediated the impact of dolichoectasia on cSVD progression and may represent a potential therapeutic target.
- Multimodal single-cell analyses reveal subclinical dysfunction and limited metformin efficacy in placentas of women with PCOS
Polycystic ovary syndrome (PCOS) is linked to adverse pregnancy outcomes and increased cardiometabolic risk in offspring, yet the placental mechanisms underlying these risks remain poorly understood. Metformin is prescribed during PCOS pregnancies despite limited mechanistic justification. Using multi-modal molecular analyses of placentas from healthy controls and women with PCOS randomized to placebo or metformin (PregMet trial), restricted to uncomplicated pregnancies, we characterized direct PCOS associated placental alterations independent of confounding complications. PCOS placentas showed transcriptional downregulation across multiple cell types and shifts in cell type proportions. Specifically, syncytiotrophoblasts exhibited reduced expression activity of growth hormone receptor signaling and glycosaminoglycan biosynthesis. Endothelial cells displayed diminished receptor tyrosine kinase pathway activity, including VEGFC, despite increased cell proportion and hypervascularity. Intercellular communication networks were globally suppressed, including reductions in PDGF signaling from Hofbauer cells to fibroblasts. Notably, metformin did not reverse most PCOS-associated molecular alterations and induced transcriptional changes correlated to birth weight and childhood BMI. These findings indicate that PCOS-associated placental features are driven by cell type specific dysregulation of growth factor, angiogenic signaling pathways that are largely unresponsive to metformin. This underscores the need to develop mechanism based, placenta targeted therapeutic alternatives for future pregnancy management.
- The Verification Gap: Artificial Intelligence Adoption, Hallucination Awareness, and Verification Practices Among Early Career Medical Researchers in Pakistan
Artificial intelligence (AI) tools have been rapidly adopted by medical researchers, yet whether early career researchers in low and middle income countries possess the awareness and habits needed to use these tools safely remains poorly documented. This study characterized AI adoption patterns, hallucination awareness, and verification and disclosure practices among early career medical researchers in Pakistan. A cross sectional anonymous online survey was conducted among medical students, house officers, residents, physicians, and faculty involved in research or academic work across Pakistan (May 2026). Descriptive statistics and chi square tests were applied to 373 eligible responses. AI use was near universal (99.7%), with 60.3% using AI tools daily. The most commonly reported tool in this sample was Claude (40.5%), followed by ChatGPT (29.2%) and Perplexity (26.0%), though this ranking likely reflects sampling characteristics. Despite high adoption, 59.2% typically did not verify AI outputs before use, and 40.2% had never heard that AI can generate fabricated scientific references. In behavioral vignettes, 36.5% assumed convincing AI generated references were authentic, and 54.2% would continue using remaining AI content after discovering one fabricated reference. Formal research training was strongly associated with consistent disclosure (51.7% vs. 17.1%; chi square=48.43, p less than 0.001). Role, daily use frequency, and research training were not significantly associated with verification behavior. Early career medical researchers in Pakistan demonstrate high AI adoption alongside incomplete hallucination awareness and infrequent verification, a pattern that may carry implications for research integrity. Formal training was the only factor significantly associated with consistent disclosure. Integration of AI literacy into medical curricula and institutional governance frameworks merits consideration.
- High-dimensional Characterization of Genome-Environment Fitness Landscapes in Klebsiella pneumoniae
Background Bacterial fitness is shaped by interactions between genome variation and environmental context, yet how these interactions determine its predictability and heritability remains unclear. In the clinically important pathogens of Klebsiella pneumoniae, a leading cause of hospital-acquired infections, this question is particularly pressing. Despite extensive genomic characterization, we still lack a systematic understanding of how genome-wide variation translates into fitness across diverse environments in K. pneumoniae. Methods We filled this gap by profiling a systematic collection of 1,462 clinical K. pneumoniae isolates across 214 diverse environmental and pharmacological stress conditions using high-throughput chemical genomics. Fitness was quantified from colony growth and integrated with whole-genome sequencing data. Genome-wide association analyses identified genetic determinants of fitness, and machine learning models incorporating genomic features were used to predict fitness.Results Fitness exhibited a strongly environment-dependent genetic architecture, with modest but significant concordance between genetic background and phenotypic variation. Under antibiotic and stress-combination conditions, fitness was driven by discrete, high-effect determinants, including known resistance genes, resulting in stronger signals and improved predictability. In contrast, non-antibiotic environments showed more polygenic and distributed architectures with weaker associations. Genome-wide analyses identified both established and previously uncharacterized genes linked with fitness across conditions. Resistance and virulence determinants exhibited clear context-dependent trade-offs, conferring fitness advantages under selection but imposing costs in non-selective environments. Consistent with this, plasmid carriage showed environment- and genotype-dependent fitness effects, with benefits under antibiotic pressure and measurable costs otherwise. Genomic variant-based models for fitness prediction achieved moderate performance (Mean Spearman correlation ({rho}) = 0.36 (95% CI: 0.18-0.67) for predicted versus observed values in unseen data) across conditions, with improved accuracy under strong antibiotic selective pressures, and produced well-calibrated prediction intervals with high coverage. Despite strong population structure effect on predictions, models captured predictive gene and SNP biomarkers for fitness. Conclusion These findings highlight that bacterial fitness is an emergent property of genome-environment interactions rather than a fixed attribute of genotype. This work establishes a unified high-dimensional genotype-phenotype framework linking genomic variation to fitness across diverse conditions in a major pathogen, with broader implications for other pathogenic bacterial species.
- Locally adaptive conformal prediction intervals for polygenic score-based phenotype prediction via residual normalization and data-driven stratification
Quantifying uncertainty in polygenic score (PGS)-based phenotype prediction is crucial for the integration of genomic data into precision medicine. While the PGS provides a fundamental pivot for point estimation, clinical decision-making necessitates the construction of well-calibrated prediction intervals that reliably encompass the true phenotypic values. However, phenotypic residuals are frequently characterized by complex heteroscedasticity and stratified variance structures across diverse demographic contexts. Existing approaches often rely on global calibration mechanisms, which fail to account for such localized variance structures and lead to systematic miscalibration within specific subpopulations. To bridge this gap, we propose Clustering-based Split Conformal Prediction with Normalized Residuals (C-SCNR), a versatile framework based on Split Conformal Prediction. By adopting residual normalization and incorporating a repetitive `split-and-cluster` mechanism, C-SCNR dynamically identifies latent error strata and applies fine-grained adjustments to the resulting intervals. Our framework requires no distributional assumptions regarding the phenotype, is compatible with any PGS method, and flexibly accommodates biologically-informed grouping. Simulation studies demonstrate that our framework consistently outperforms existing methods across diverse error distributions. In real-data applications analyzing Body mass index (BMI), Low-density lipoprotein (LDL) cholesterol, and High-density lipoprotein (HDL) cholesterol in the UK Biobank, C-SCNR effectively resolves the coverage deficiencies of existing methods in specific subgroups and consistently yields superior localized calibration. Overall, C-SCNR represents a flexible and powerful framework for constructing high-resolution context-specific prediction intervals, thereby facilitating more reliable clinical interpretations of polygenic risk.
- Subtype Dynamics Reveal Horizon-Dependent Structure in Influenza Predictability
Accurate forecasting of seasonal influenza is critical for public health preparedness, and data-driven models are central to this effort. However, most approaches rely on aggregate indicators of influenza-like-illness (ILI), which can obscure heterogeneity and limit predictability at longer horizons. While subtype dynamics are well established, their role in data-driven forecasting remains incompletely understood. Here, we integrate subtype-resolved surveillance data into diverse data-driven frameworks using over a decade of U.S. surveillance records to evaluate and decompose predictive signal in influenza forecasting. Across pre- and post-COVID-19 periods, subtype-informed models consistently improve over baseline models trained on aggregate ILI alone, with the largest gains at longer horizons. Decomposition reveals a horizon-dependent reorganization of predictability: autoregressive persistence in recent aggregate incidence dominates at short horizons but declines with lead time, while predictive signal shifts toward subtype-derived structure. Within this structure, interaction-related features among co-circulating subtypes grow systematically with forecast horizon, indicating that longer-term predictability is driven increasingly by interaction structure rather than marginal subtype composition alone. Together, our results show that subtype information provides non-redundant predictive signal and extends the effective forecasting window of data-driven models. More broadly, our findings suggest that aggregation of heterogeneous subtype processes can obscure latent predictability, supporting subtype-resolved surveillance.
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- Prevotella stercorea links gut microbiome ecology to respiratory infection protection through a host-context-dependent, species-autonomous pathway
Using a longitudinal cohort of 633 Gambian children (IHAT-GUT, NCT02941081), we resolve two mechanistically distinct ecological pathways linking Prevotella stercorea to infection risk. Its abundance positively predicts gut microbiome richness, consistent with community-level colonisation resistance for enteric outcomes. However, its association with reduced acute respiratory infection (ARI) persists unchanged after richness adjustment, identifying a species-autonomous pathway independent of community diversity. Weight-for-age z-score (WAZ) is uncorrelated with microbiome richness within strata, supporting WAZ as a proxy for host immune-metabolic reserve rather than a determinant of microbiome composition. In Low-WAZ children, P. stercorea at Day 1 associates with suppressed CRP, whereas in higher-WAZ children, elevated Day 1 inflammation predicts subsequent P. stercorea colonisation at Day 85, consistent with host-context-dependent immune selection. ARI and fever protection is richness-independent and concentrated in Low-WAZ children. P. copri does not retain an independent protective association when modelled jointly. These findings have direct implications for microbiome-directed interventions.
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- SoftBank says it will invest up to €75 billion to build French data centers
The goal, the firm said, is to develop and operate up to 5 gigawatts of additional data center capacity.
- SoftBank Plans Up to €75 Billion Investment in French AI Centers
SoftBank Group Corp. plans to invest as much as €75 billion ($87 billion) to build 5 gigawatts of artificial intelligence data center capacity in France, saying the country is poised to become a top European hub for AI infrastructure.
- SoftBank to build up AI data centres in France with major investment
SoftBank to build up AI data centres in France with major investment Reuters
- SoftBank to Plow $52 Billion Into French Data Centers
The Japanese company’s initial investment aims to deliver up to 3.1 gigawatts of computing capacity across France by 2031.
- Softbank to Invest Up To 75 Billion Euros on AI Data Centers in France
Softbank to Invest Up To 75 Billion Euros on AI Data Centers in France The Information
- SoftBank plans $88bn AI data hub in France as it expands beyond US
SoftBank plans $88bn AI data hub in France as it expands beyond US Nikkei Asia
- Meta is reportedly developing an AI pendant
Meta seems to be making big bets on AI-powered hardware.
- Meta plans AI pendant, 'wearables for work' in hardware boost: Report
The Facebook and Instagram owner plans to significantly expand its selection of AI glasses and add a business-focused service called "Wearables for Work" the report said, citing an internal memo by Alex Himel, Meta's vice president of wearables.
- Meta eyes AI pendant as part of its ambitious new hardware strategy
Meta eyes AI pendant as part of its ambitious new hardware strategy
- Meta plans AI pendant, ‘wearables for work’ in hardware boost, The Information reports
Meta plans AI pendant, ‘wearables for work’ in hardware boost, The Information reports The Straits Times
- Meta plans to make an AI pendant and more smart glasses soon
Meta is looking to expand its AI wearables business into the world of clip-on pendants, with more glasses coming soon, too.
- After smart glasses, Meta apparently wants you to wear its all-listening AI pendant
After putting AI in glasses, Meta reportedly wants to bring it even closer. Not everyone will be comfortable with the idea.
- I put Google’s 24/7 AI assistant Gemini Spark to work, and it’s actually pretty useful
Gemini Spark helps automate everyday tasks, from inbox summaries to local event planning, but it’s unclear why Google made it a separate product.
- Google's Agentic AI Tool Gemini Spark Is Now Available
Google's Agentic AI Tool Gemini Spark Is Now Available PCMag Australia
- Google's Agentic AI Tool Gemini Spark Is Now Available
Google's Agentic AI Tool Gemini Spark Is Now Available PCMag UK
- Google's Agentic AI Tool Gemini Spark Is Now Available
Google's Agentic AI Tool Gemini Spark Is Now Available PCMag Middle East
- Gemini Spark is now rolling out and it hopes you will trust an AI more than apps
Google's latest Gemini feature wants to do more than answer questions. It could change how you interact with apps altogether.
- Britain signs underwater drone deal with US and Australia
Britain signs underwater drone deal with US and Australia The Telegraph
- UK, US and Australia to develop 'cutting-edge' underwater drone technology
The new technology is expected to be ready by 2027 and is designed to help defend critical subsea cables and pipelines.