AI News Archive: July 15, 2026 — Part 11
Sourced from 500+ daily AI sources, scored by relevance.
- Cover First, Disagree Softly: Rethinking Mismatch-First Active Learning for Frame-Level Audio Classification
Sound event detection relies on frame-level strong labels whose annotation is expensive. Active learning addresses this problem by selecting the audio segments whose labels help the classifier most. One of the prevailing acquisition strategies for this task, mismatch-first farthest-traversal (MFFT),...
- Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning
Bioacoustic call-type classification relies on costly expert annotation. Active learning can reduce this burden by selecting a small batch of segments for expert annotation and using the labeled segments for training the classifier. The setting is hard: the target calls are extremely sparse and the ...
- Bring Music The Horizon: Music-Driven 360$^\circ$ Video Generation
Music visualization offers a powerful way to enhance listeners' understanding and experience of music by translating auditory signals into visual forms. However, most existing approaches either rely heavily on lyrics or generate flat, non-immersive videos similar to conventional music videos, which ...
- TMallGS: Scaling Unified Feature and Sequence Modeling for Generative E-commerce Search
In industrial search and ranking systems, Click-Through Rate (CTR) prediction is shifting from traditional Deep Learning Recommendation Models (DLRM) toward unified, compute-intensive Transformer architectures. This transition is driven by the need to improve Model FLOPs Utilization (MFU) and achiev...
- Optimizing Visibility in Generative Engines: A Critical Survey of Generative Engine Optimization (2023-2026)
Generative Engine Optimization (GEO) seeks to increase content's presence, likelihood of citation, or influence in answers produced by generative engines. Since the foundational GEO paper, the field has expanded rapidly, but terminology, metrics, and evidence standards remain heterogeneous. This cri...
- Personalizing Incremental Video Search with Hybrid Text and ID Embeddings
Incremental video search requires high-quality ranking after each keystroke, where intent is often underspecified (e.g., 1-3 character prefixes). We present a personalization system for Apple TV search that combines complementary semantic and collaborative signals at ranking time. Our approach learn...
- Can We Steer the Black-Box? Towards Controllability-Centric Evaluation of Recommender Systems with Collaborative Agents
Recommender systems operate as Black-Boxes, leaving users and regulators unable to steer their outputs toward specific intentions or audit their behavior. This lack of controllability, defined as the system's ability to respond to explicit guidance, remains an unaddressed dimension in existing evalu...
- COB: a comprehensive database of chloroplast outer envelope beta-barrel proteins
Despite their central role in metabolite exchange, lipid trafficking, and protein import, chloroplast outer envelope beta-barrel proteins lack a dedicated comprehensive sequence database spanning many plant proteomes. Here we present COB (chloroplast outer-envelope beta-barrel), comprising 16,586 sequences organized across ten protein categories and an uncharacterized group, constructed using a machine learning classifier that identifies chloroplast beta-barrels from features of evolutionary protein contact maps. COB reveals that Streptophyta have more barrels overall and a greater variety of solute transporters than Chlorophyta. COB shows considerable structural diversity across categories, including variable beta-strand counts and a high prevalence of open barrel conformations not seen in bacterial outer membrane proteins. Structure predictions for Arabidopsis thaliana outer envelope proteins identify candidate hybrid barrel assemblies, with TOC159 family members as universal interaction partners, and single-chain multi-barrel architectures previously described only in Gram-negative bacteria. Chloroplast barrels also have more open topologies and shorter strands than bacterial barrels. COB provides a comprehensive sequence resource for chloroplast outer envelope beta-barrels, establishing a foundation for investigating the evolution, structure, and functions of this essential protein-fold.
- A Deep Learning Framework for Biomarker Segmentation and Classification in Traumatic Brain Injury
Traumatic brain injury (TBI) triggers widespread biomarker activation, including astrocytic markers such as glial fibrillary acidic protein (GFAP) and microglia markers such as ionized calcium-binding adapter molecule 1 (IBA1). Quantifying and analyzing these biomarkers are critical for understanding injury impact; however, current methods are labor-intensive and time-consuming. In this study, we propose an automated deep learning framework for dual-biomarker segmentation and TBI classification using GFAP and IBA1 immunofluorescent images. Four U-Net variants: Baseline U-Net, Nested U-Net (U-Net++), Attention U-Net (MANet), and Residual U-Net (LinkNet) were trained for segmentation. Three classification models, ResNet50, Swin-T, and MaxViT, were trained to distinguish TBI from control images under single- and dual-biomarker conditions. The baseline U-Net achieved the highest segmentation Dice score for GFAP (0.9259), while the U-Net++ achieved the highest Dice score for IBA1 (0.9676). Trained segmentation models demonstrated significantly better performance compared to QuPath alternatives. While GFAP alone supported high classification accuracy, IBA1 alone was less effective. Multimodal fusion of GFAP and IBA1 significantly improved classification performance across all models, with Swin_T achieving the highest overall accuracy (0.9489), and ResNet50 achieving the highest F1-score (0.9499). These findings demonstrate that integrating complementary biomarkers enhances automated TBI classification, and deep learning offers a robust alternative to manual analysis for immunofluorescent brain injury imaging. This framework is scalable to additional biomarkers and injury models, offering a reproducible approach to accelerate biomarker research.
- Prototype-based AI triage for 3D pathology
Non-destructive 3D pathology enables high-resolution slide-free imaging of intact clinical specimens, providing comprehensive visualization of tissue structures beyond what conventional slide-based 2D histopathology can provide. However, the scale and complexity of volumetric datasets make exhaustive manual review impractical, motivating AI-assisted triage methods to select a small number of high-risk 2D slices for pathologist review. While prior triage models have shown promise, interpretability is poor and performance can be suboptimal, especially in the nascent field of 3D pathology in which labeled data is limited. We present SCOPE, a Segmentation-guided CrOss-slice PrototypE learning framework for comprehensive risk assessment of 2D levels within 3D pathology datasets. SCOPE combines (i) clustering-based pretraining on large-scale unlabeled volumetric data to initialize morphology-aware prototypes, (ii) segmentation-derived structural priors from publicly available models to guide prototype learning, and (iii) cross-slice (2.5D) prototype aggregation across neighboring slices to generate slice-level risk predictions. In prostate and esophageal data cohorts, SCOPE consistently outperforms attention-based and prototype-based multiple instance learning baselines for both binary and multiclass prediction tasks, enabling depth-resolved risk profiling for 3D triage based on morphological prototypes that are interpretable to pathologists.
- Low-latency neuromorphic closed-loop control of hippocampal ripples in vivo
Real-time closed-loop neuromodulation, in which stimulation is precisely timed to ongoing brain dynamics, holds transformative potential for treating neurological disorders and probing neural circuit function. However, it requires low-latency, energy-efficient processing of high-bandwidth neural signals that conventional computing architectures struggle to deliver. Neuromorphic computing, which emulates the event-driven and massively parallel operation of biological neural circuits, offers a compelling alternative. Yet, its integration into closed-loop frameworks validated in vivo for fast, transient oscillations has not been demonstrated. Here, we present a fully integrated neuromorphic framework for real-time detection and manipulation of hippocampal ripples: brief (30-100 ms), high-frequency (100-250 Hz) oscillations that are critical for memory consolidation and implicated in neurological disorders. We train compact spiking neural networks comprising 41 neurons and 530 parameters using surrogate-gradient backpropagation, achieving detection performance competitive with deep learning models across 23 recording sessions while consuming up to 200-fold less energy when deployed on SpiNNaker neuromorphic hardware. Integration with the open-source Open Ephys platform yields total closed-loop latencies of approximately 50 ms, enabling intra-event stimulation in up to 80% of ripples. Validating the complete sensing-processing-stimulation pipeline in awake, head-fixed mice, we demonstrate that neuromorphic-triggered optogenetic inhibition significantly alters ripple dynamics and reduces oscillatory energy. This work establishes a practical and accessible neuromorphic framework for low-latency closed-loop control of fast brain dynamics in vivo.
- Brain Structural and Resting-state Functional Network Changes Following Expiratory Musculature Targeted Resistance Training in Healthy Young Adults: A Pilot Study
Multimodal imaging approaches that combine structural and functional neuroimaging provide a robust framework for examining neuroplastic adaptations that may not be captured by any single modality. The present study investigated the effects of a four-week expiratory muscle strength training (EMST) program on structural and resting-state functional connectivity in healthy young adults. Five healthy young adult males (aged 19-35 years) completed a standard four-week EMST protocol and underwent pre- and post-training imaging assessments. Structural neuroimaging included T1-weighted and diffusion-weighted MRI, which were analyzed using voxel-based morphometry, surface-based morphometry, and white-matter structural connectivity. Functional neuroimaging consisted of resting-state fMRI to assess training-related changes in functional architecture, network connectivity, and global network measures. Structural MRI analyses revealed no significant changes in gray or white matter volume, cortical morphology, or white-matter structural connectivity following EMST (all FWE- or FDR-corrected p > .05). In contrast, resting-state fMRI demonstrated a significant increase in whole-brain functional connectivity (FDR-corrected p = .036), accompanied by greater network integration, reflected in increased local efficiency and transitivity and reduced modularity. Network-level analyses showed enhanced within- and between-network connectivity in sensorimotor and cognitive circuits. Our findings demonstrate robust functional reorganization following EMST, despite the absence of detectable macrostructural or large-scale white-matter connectivity changes, at least within the timescale and sample characteristics of the current study. These results reflect early-stage neuroplasticity, both globally and within the networks underlying speech and swallowing control and suggest that functional reorganization occurs early in training and likely precedes longer-term structural modifications in these networks.
- A geometric and dynamical theory of latent computations in biological neural networks
Many neural recordings have revealed low-dimensional sets of behaviorally relevant variables encoded within large-scale neural activity patterns. However, dimensionality reduction analyses alone cannot yield causal explanations for how networks stably implement computations that are resilient to the substantial variability of single neuron dynamics. Further, existing methods for dimensionality reduction often rely on simplifying assumptions about network structure that limit their applicability and explanatory power. To provide a theoretical framework describing the dynamics of low-dimensional computation in high-dimensional neural networks, here we introduce the concept of latent processing units (LPUs), which are architecture-agnostic computational elements operating within biological neural circuitry. Six theorems governing coding and computation by LPUs collectively provide explanations for a range of common biological findings: low-dimensional sets of coding variables can generate high-dimensional neural dynamics; many neurons have activity patterns that represent behaviorally relevant variables but exert little influence on downstream circuits; linear readouts of neural population activity commonly permit near-optimal decoding; the drift of neural representations is often substantial even while network computations remain intact. Overall, our treatment of LPUs, as enacted in network dynamics, unifies the geometric and dynamical views of neural computation under a joint framework and provides systems neuroscience with a causal account of how the brain executes reliable computations.
- A robust, sensitive phylogenetic method enables gene-level metagenomic analyses
A key goal in the microbiome field is to move from taxonomic associations towards mechanistic hypotheses about microbial gene function. However, most methods for linking microbiome changes to specific genes are biased towards finding marker genes, with weak evidence for functional relevance. Phylogenetic regression can address this issue and has been previously applied to changes in microbial prevalence, but many environments (such as the gut in health vs. disease) are characterized more by changes in abundance, which presents unique statistical challenges. We show that when applied to real differential abundances from metagenomes, phylogenetic regression has an anti-conservative bias, indicating inflated false positives. We develop an alternative non-parametric method called "robust permutration," designed specifically for differential abundance data, and evaluate its performance against phylogenetic regression as well as several other phylogenetic comparative methods in realistic simulations of metagenomic data. These results show that robust permutration is the most powerful method that appropriately controls the false positive rate. We further apply robust permutration to a human case-control study of liver cirrhosis, revealing that Lachnospiraceae abundance in disease is linked to a previously uncharacterized iron-sulfur transcription factor encoded near homologs of the butyryl-CoA oxygen oxidoreductase system, a recently discovered system for oxygen detoxification. This illustrates how robust, sensitive phylogenetic methods can enable the generation of new molecular hypotheses directly from metagenomic case-control data.
- Partial epithelial-to-mesenchymal transition mediates profound gap closure through growth and fluidization
Epithelial gap closure is essential for maintaining tissue integrity during development and wound healing. Previous studies have shown that closure of small gaps is driven by actomyosin purse-string contraction and traction forces generated at the gap edge. Here, we show that millimeter-scale circular gap closure in mouse epicardial (MEC1) monolayers is driven primarily by growth-mediated compressive stresses. Compared with MDCK monolayers, MEC1 cells close gaps more rapidly with reduced undulation near gap edge through coordinated tissue-wide extension-contraction. The collective closing dynamics can be modulated by partial epithelial mesenchymal transition induction and Rho kinase inhibition. By integrating tissue and cell kinematic analyses, traction force mapping, and a continuum framework that decomposes tissue strain rates into growth, elastic, and fluidity related contributions, we reveal that growth-generated compression drives inward tissue flow, while elastic cell elongation and fluid-like tissue remodeling through cell cell intercalation act synergistically to accommodate deformation and promote robust collective gap closure.
- From Hummingbird to Elephant: Amyloid Formation in Natural Transthyretin Variants
Transthyretin (TTR) is a secreted protein associated with cardiac and other amyloid diseases via misfolding. We have previously shown that agitation of human TTR solutions at neutral pH results in aggregation and fibril formation. Here we report that agitation-induced aggregation of TTR from species with very different heart rates (Anna's hummingbird, hbTTR, and African elephant, aeTTR) differs from that of human TTR (huTTR). Aggregation of hbTTR is slow and favors formation of smaller, fibrillar aggregates, while aeTTR aggregation is rapid and favors larger, more amorphous particles. Spherical, early-stage oligomeric intermediates were found for all variants by mass photometry and electron microscopy. The slow aggregation of hbTTR matches its resistance to denaturation by 8 M urea. The widely different aggregation behavior exhibited by these naturally occurring TTR variants in response to mechanical agitation under close to physiological conditions provides insight into how small sequence differences can contribute to the evolutionary fitness of different animals.
- Before the eye moves: microsaccade preparation expands spatial integration at the center of gaze
Fixation is often treated as a period of stable visual processing. Yet, fixation is often punctuated by frequent microsaccades that occur during tasks involving complex foveal stimuli. These small eye movements are preceded by changes in visual sensitivity, both at the upcoming movement goal and at the currently fixated location. However, previous work has largely focused on isolated stimuli, leaving unclear whether pre-microsaccadic modulations reflect changes in sensitivity alone or also alter the spatial interactions that govern object recognition. Visual crowding provides a direct test of this question because it depends on the integration and segregation of nearby features and constrains recognition even within the foveola. Using high-precision Dual Purkinje Image eye tracking with retinally contingent stimulus delivery, we measured acuity and crowding thresholds at the preferred locus of fixation (PLF), the starting point of the impending gaze shift, while observers either maintained fixation or prepared to execute a microsaccade to a cued location. Unflanked acuity at the PLF remained stable across conditions. In contrast, crowding strength increased during the pre-microsaccadic interval, indicating an expansion of the foveal crowding zone. These results show that microsaccade preparation alters spatial integration at the starting point of the movement, increasing crowding even when sensitivity to isolated stimuli remains unchanged. Thus, microsaccades reshape foveal vision not only by modulating visual discrimination at the movement goal, but also by changing how nearby features are integrated and segregated before the eyes move.
- Effect of Immunosuppressive Drugs on Glucose-Stimulated Insulin Secretion: Concentration-Response Studies in Dynamic Perifusion Assays
Immunosuppressive drugs, which are required to maintain graft function in transplant recipients, are associated with many unavoidable side effects including posttransplant diabetes mellitus (PTDM) that involves both peripheral insulin resistance and impairment of insulin secretion. To characterize in detail the concentration-dependency of the effect of well-known immunosuppressive drugs on glucose-stimulated insulin secretion (GSIS), we performed dynamic perifusion studies with human pancreatic islets. The effect on the time-profile of GSIS has been assessed over a wide concentration range for several clinically relevant immunomodulatory therapies, including small-molecule drugs (cyclosporine, sirolimus, tacrolimus, prednisolone acetate, and loteprednol etabonate) and biologics (abatacept and anti-CD40L), plus a prospective {beta}-cell proliferation-inducing agent (harmine). While biologics showed no significant detrimental effects after one-day treatment even at relatively high concentrations (5 M), all small-molecule drugs inhibited insulin secretion in a concentration-dependent manner, although glucocorticoids showed a distinct response pattern. Calcineurin and mTOR inhibitors preserved GSIS within their therapeutic ranges but progressively distorted its time-profile at higher concentrations and completely suppressed secretion at the highest levels. Cyclosporine exhibited the least, only about 35-fold, separation between its therapeutic target (Ctarg) and half-maximal GSIS inhibitory (IC50) concentrations. Glucocorticoids did not alter the shape of the time-profile but inhibited overall insulin secretion even at therapeutic levels. Their inhibitory effect only increased slowly with concentration and did not follow a classic sigmoid pattern that has unity Hill slope. These findings establish quantitative benchmarks for immunosuppressant-induced {beta}-cell toxicity and provide a framework for optimizing immunosuppressive regimens to reduce the risk of PTDM.
- SIGLEC1 FACILITATES MACROPHAGE-CD8+ T CELL INTERACTIONS AND CORRELATES WITH CANCER IMMUNOTHERAPY RESPONSE
Antigen-presenting cell (APC) interactions with cytotoxic T cells are critical for anti-tumour immunity and response to immune checkpoint blockade (ICB), yet context-specific regulators in the tumour microenvironment remain not fully defined. Here we identify the lectin receptor SIGLEC1 as a key mediator of macrophage-T cell interactions in human melanoma. In a well-characterized patient cohort, SIGLEC1 was selectively upregulated in inflammatory macrophages physically associated with activated/exhausted CD8+T cells. Imaging and functional analyses revealed that SIGLEC1 accumulates at the macrophage-T cell interface and promotes cell clustering. SIGLEC1 ligands were enriched on activated T cells, and their blockade reduced cytotoxic cytokine production ex vivo. Single-cell (spatial) transcriptomics across independent ICB-treated melanoma cohorts showed that SIGLEC1+ macrophages localize near CD8+T cells and are enriched in responders, where they also associate with T cells expressing activation/exhaustion markers. These findings define a SIGLEC1-dependent macrophage-T cell niche linked to effective immunotherapy.
- The CD19-4-1BBL antibody fusion protein unleashes the immune system against high-risk chronic lymphocytic leukemia
CD19-4-1BBL is a bispecific antibody fusion protein that targets CD19 and costimulates 4-1BB on T cells and other immune cells. Its antitumor activity has been reported in B-cell non-Hodgkin lymphoma with emphasis on its T-cell mediated cytotoxic activity. Its effect on other 4-1BB expressing immune cells is unexplored. Here, we investigated the molecular mechanisms and the antileukemic effect of CD19-4-1BBL in chronic lymphocytic leukemia (CLL), a B-cell malignancy profoundly marked by the immunosuppressive activity of myeloid-derived suppressor cells, tumor-associated macrophages and CD4+ regulatory T cells. We demonstrated that CD19-4-1BBL simultaneously mitigates the immunosuppressive phenotype and transcriptome machinery of these cells and promotes antitumor CD8+ T-cell immunity. Finally, in a preclinical, patient-derived xenograft model of CLL, we observed a favourable survival impact, especially in mice transplanted with immune cells from patients with high-risk/progressive leukemia. Our findings provide evidence that the CD19-4-1BBL treatment is a multifaceted, immune-based strategy that should be clinically explored in patients with chronic lymphocytic leukemia.
- Audiovisual stimulation using wearable shutter glasses robustly evokes 40 Hz neuronal activity but does not modulate associative memory
Audiovisual stimulation is a promising approach for studying and modulating human neuronal gamma (>30 Hz) oscillations and associated memory processes. Portable setups can increase ecological validity and therapeutic potential, but options remain limited. See-through shutter glasses are a new mobile technology that adds a visual flicker effect to what the user naturally sees. Here, we validated this method in a multisensory, cognitively relevant setting. We leveraged a previous experimental design from our lab, aiming to A) characterise the neuronal gamma activity evoked by shutter glasses, and B) conceptually replicate the previously reported effect of audiovisual gamma stimulation on memory accuracy, in line with a Spike Timing Dependent Plasticity model. We recorded high-density Electroencephalography (EEG) from 24 healthy participants during an associative memory task. Video-sound pairs were presented with the sound amplitude-modulated at 40 Hz and 40 Hz visual flicker elicited by the shutter glasses, with a phase offset between both modalities. The visual flicker preceded the auditory modulation by 90 or 270 degrees. Participants were asked to remember the video-sound associations. They also underwent a visual-only condition and an electrically equivalent control condition. EEG evoked power and phase coherence were reconstructed at source level and analysed along with behavioural accuracy. As expected, the shutter glasses robustly increased EEG evoked power and phase coherence at 40 Hz compared to the control condition. Effects were widespread and stronger than in a previous study not using shutter glasses. However, we did not replicate the previously reported effects of audiovisual phase offsets on memory accuracy. This could be due to reduced statistical power or methodological differences. Nonetheless, the validation of shutter glasses in a multisensory setting and the EEG analysis software, now improved and open source, enable important further investigations of audiovisual gamma stimulation in research and clinical settings.
- Trait Resilience Modulates the Association Between Cortisol and Aperiodic Neural Dynamics
Cortisol, our stress hormone, exerts widespread influence on neural activity. However, its influence on the aperiodic component of the electroencephalography power spectrum remains to be investigated. Given individual differences in the capacity to cope with stress and adversity, it also remains unclear whether trait resilience moderates this relationship. Hence, the present study examined whether individual differences in trait resilience moderates the association between resting cortisol and aperiodic activity. Participants (N=145) completed various self-report questionnaires (e.g., trait resilience). Electroencephalography was recorded over a 20-minute baseline period, followed by salivary cortisol collection. The results revealed a significant moderating effect of trait resilience in the occipital scalp region. Specifically, higher cortisol concentration was associated with flatter 1/f slopes amongst individuals with low trait resilience, whereas this association was reversed amongst those with high trait resilience. Overall, our findings highlight the role of individual differences in trait resilience in shaping hypothalamic-pituitary-adrenal axis-related neural dynamics.
- Neurobehavioural correlates of changing one's mind in ADHD and OCD
Cognitive flexibility is an executive function that allows individuals to adjust behaviour in response to changing environmental demands. We assessed volitional switching under uncertainty, without rule-based learning, in the 'Change Your Mind' task. Nineteen patients with obsessive-compulsive disorder (OCD), 19 patients with attention-deficit hyperactivity disorder (ADHD) and matched control participants (20 per group) completed the task whilst undergoing a functional MRI scan. The task was a two-alternative forced choice paradigm where each stimulus was presented twice successively, with spurious feedback following the first presentation. This allowed participants the opportunity to repeat or change their response. Participants with ADHD changed their response more frequently than controls following a previously correct response, associated with reduced accuracy on the second trial. This was accompanied with smaller differences between change and repeat trials in the superior frontal gyrus, paracingulate gyrus and frontal pole compared to controls. Participants with OCD did not differ from healthy controls in their performance but exhibited greater activity on both change and repeat trials in the pre- and postcentral gyri than controls. These results point to distinct neurobehavioural differences in patients with ADHD and OCD underlying what is often termed more broadly inflexible behaviour.
- Hyperbolic Brain Modelling and Neurocognitive Decline Analysis for Disease Detection
Mapping hierarchical brain networks within traditional Euclidean space causes significant structural distortion, undermining neuroimaging diagnostic frameworks. While hyperbolic models like the Poincaré ball preserve these nested topologies, they demand heavy computational overhead due to intricate Möbius operations and curved geodesics. This paper introduces a highly efficient non-Euclidean framework for analyzing neurocognitive decline utilizing the Beltrami-Klein ball model. By projecting hyperbolic geodesics as Euclidean straight lines, this approach converts complex distance calculations into simple dot products, radically reducing processing demands. We validated our methodology against state-of-the-art Poincaré and Lorentz baselines using datasets for Schizophrenia, Parkinson's Disease, and Alzheimer's Disease. The Klein-based framework demonstrates superior performance, delivering both higher diagnostic precision and accelerated processing velocities across all three neurocognitive disorders.
- Allosteric modulation of β1 integrin through the hybrid domain reverses articular cartilage injury and functional impairment in a murine model of inflammatory arthritis
Rheumatoid arthritis is a chronic inflammatory joint disease in which progressive destruction of cartilage and bone drives long-term disability. Current disease-modifying therapies target the immune and cytokine networks that sustain synovial inflammation, but none is directed at the chondrocyte, the resident cell responsible for maintaining cartilage matrix. Chondrocyte survival and matrix homeostasis depend on beta1 integrin-mediated adhesion to the extracellular matrix, and dysregulated integrin signalling has been implicated in cartilage injury. Here we test the hypothesis that allosteric modulation of beta1 integrin, rather than simple adhesion blockade, is chondroprotective. Using the monoclonal antibody JB1a, which binds an epitope in the hybrid domain of beta1 integrin and stabilises the receptor in a low-affinity conformation, we show that intra-articular administration produces both functional and structural amelioration of Freund complete adjuvant (FCA)-induced arthritis in mice. JB1a abolished the FCA-induced increase in joint diameter and hyperalgesia and markedly reduced synovial inflammation, pannus formation and cartilage erosion, with no effect on the contralateral joint and no observed adverse effects. These changes were accompanied by a reduction in chondrocyte apoptosis in vivo. In primary human articular chondrocytes, JB1a abolished interleukin-1beta (IL-1beta)-induced caspase 3/7 activation, reduced IL-8 secretion, and restored the sinusoidal oscillation of intracellular ATP that was otherwise abrogated by IL-1beta. In contrast, the adhesion-blocking, integrin-clustering antibody 6S6 activated caspase 3/7 and amplified IL-1beta-induced IL-8 secretion, indicating that the therapeutic effect is a property of the specific mode of receptor engagement rather than of adhesion blockade per se. These findings identify beta1 integrin conformational state as a determinant of chondrocyte energy homeostasis and survival, and nominate allosteric beta1 integrin modulation as a mechanistically distinct, chondrocyte-directed therapeutic strategy in inflammatory arthritis.
- Galangin and Caffeic acid inhibit Methylglyoxal-induced Advanced Glycation End Product formation in Bovine Serum Albumin
Glycation, a non-enzymatic reaction occurring between sugars and biological macromolecules, plays a critical role in ageing and disease pathogenesis. Methylglyoxal (MG) is a highly reactive -oxoaldehyde that leads to the formation of endogenous advanced glycation end products (AGEs). These AGEs are associated with diabetes and many other diseases, including neurodegeneration and cancer. This is often through interactions with the receptor for advanced glycation end products (RAGE). Inhibition of glycation/AGEs formation using natural products to target cancer is an area of recent interest. In vitro AGEs formation was observed by browning of samples, increased fluorescence, and carbonyl stress. MG induced changes in the structure of BSA were analysed using electrophoresis, spectroscopy, TEM, AFM, DLS, and CD spectroscopy. Our results show that AGEs form random structures, oligomeric aggregates, and {beta}-sheets. Thioflavin T and Congo red staining further validated these findings. Galangin and Caffeic acid demonstrated significant antiglycation activity, suppressing AGEs formation in vitro.
- Evidence for a Nod-like signalling system in cyanobacterial symbiosis with O. sativa
Symbiotic interactions between plants and nitrogen-fixing microorganisms are essential for sustainable agriculture, yet the molecular mechanisms underlying plant - cyanobacterium symbiosis remain poorly understood. In particular, the nature of the signalling mechanisms mediating partner recognition in associations involving Nostoc species is largely unknown. Recent proteomic analyses have identified proteins homologous to rhizobial Nod factors biosynthetic enzymes in Nostoc punctiforme, suggesting the existence of a Nod-like signalling system. However, the functional role of these components has not been experimentally validated. Here, we investigate the contribution of nod-like biosynthetic and regulatory genes to symbiosis by analysing mutants of N. punctiforme affected in genes with homology to nodB and nodD. Phenotypic characterization revealed that disruption of nodB-like genes does not impair free-living growth but affects early stages of plant association and colonization. Specifically, the nodB1 mutant is impaired in plant association and shows a mild defect in colonization, whereas the nodB3 mutant exhibits a severe defect in colonization. In contrast, nodD-like mutants exhibited altered symbiotic phenotypes, with specific regulators differentially affecting interaction and colonization efficiency in rice (Oryza sativa). In particular, mutation of nodD2 and nodD3 reduced plant association and severely compromised colonization in Oryza sativa, with a more pronounced phenotype in nodD3 mutant. Altogether, our results provide genetic evidence supporting the involvement of Nod-like components in cyanobacterial symbiosis and suggest the existence of a regulatory and biosynthetic module contributing to plant colonization. These findings shed new light on the evolution and diversity of symbiotic signalling mechanisms across plant-microbe interactions.
- Coordination Failures Generate Selection Gradients in Animal Collectives
Collective animal behavior occurs in high-stakes contexts where failing to coordinate effectively with group-mates can spell disaster for individuals. Yet, identifying instances of coordination failure is challenging, meaning their evolutionary effects remain mysterious. Synchronous calls in alternating frog choruses (i.e., inadvertent signal collisions) are unambiguous failure events that impose steep attractiveness costs. We modeled tungara frog chorusing dynamics to reveal the sensorimotor and social mechanisms underpinning synchrony. Ultimately, inter-male variation in two key sensorimotor attributes, the periods of male calling rhythms and call latencies, generated divergent synchrony engagement patterns. Modeling female preferences revealed that these varied behavioral outcomes then yielded disparate attractiveness consequences. By mechanistically linking the causes and consequences of coordination failure, we demonstrate that non-random failure patterns in collectives generate selection gradients that refine sensorimotor tuning.
- Boredom and the representation of information content in the neocortex
Boredom - a pervasive mental state - promotes the pursuit of novel information by assigning negative value to monotonous conditions. Yet, how the brain extracts and represents the information content of ongoing sensory experience remains poorly understood. Here, we combine behavioral assays, neurophysiological recordings and computational modeling across humans and mice to investigate how sensory information shapes boredom-related behavior. In a cross-species choice task, both humans and mice robustly avoid monotonous sources of sensory stimulation. We formalize perceived monotony using empirical entropy as a measure of information content and show that monotony avoidance scales directly with low entropy and in humans correlates with boredom experience. Human electroencephalography and mesoscopic calcium imaging in mice reveal that the recruitment of neocortical activity tracks stimulus entropy. Two-photon calcium imaging in the auditory cortex of mice further uncovers a stimulus-invariant population code for entropy, supported by neurons tuned to information content. A recurrent network model reproduced this code through an interplay of afferent depression and recurrent facilitation. Together, we demonstrate how the information content of sensory experience is represented in cortical population activity, providing a basis for boredom-related avoidance behavior. Thus, our findings link synaptic and neuronal dynamics to boredom, acting as a safeguard mechanism to ensure high information input to the brain.
- Effects of acute intranasal allergen exposure on resident immune cells and sensory neurons in the mouse olfactory epithelium
The main olfactory epithelium (MOE) is the primary site of olfaction and consists of multiple cell types including olfactory sensory neurons (OSNs), sustentacular cells, and immune cells. Neuroimmune interactions in epithelial tissues are critical in maintaining tissue function, but how OSNs and immune cells interact in the MOE in healthy and diseased states is largely unknown. Cellular responses in the MOE determine how and whether OSNs maintain olfactory function and are repaired or replenished following inflammatory environmental exposures. We hypothesized that acute nasal aeroallergen exposure alters immune cell function in the MOE to elicit a neuroprotective response, thereby preserving OSN function. We developed an environmental aeroallergen exposure consisting of one week of daily intranasal house dust mite extract (HDM) instillations. Spectral flow cytometry indicated only subtle changes in resident immune cells proportions and phenotypes in the MOE. Immunohistochemical evaluation did not reveal extensive changes in immune cell distribution in the sensory epithelium or lamina propria, but instead we observed increases in axonal olfactory marker protein (OMP) expression in the lamina propria, where resident immune cells are most abundant. To evaluate the effects of HDM exposure on OSN function, we performed live ex vivo Ca2+ imaging of MOEs from HDM- and sham-exposed transgenic mice using objective-coupled planar illumination (OCPI) microscopy. OSN responses to multiple odorants revealed increased chemosensory sensitivity and decreased across-trial adaptation in HDM-treated epithelia. These results indicate that short-term nasal aeroallergen exposure minimally alters immune cell phenotypes, and instead induces functional changes in OSN physiology that preserve olfactory function.
- Flexible predictive control in human interception under visual occlusion and altered gravity
Interception of moving objects requires the nervous system to compensate for sensory delays and uncertainty, yet how behavior is controlled remains debated. Key questions concern whether predictive processes play any role at all and, if so, whether they rely on simple motion extrapolation or incorporate internalized physical priors, such as gravity. Another open question is whether observers adopt a single control strategy or flexibly switch between predictive and reactive control - or between different predictive strategies - depending on task demands. To address these questions, we developed a virtual interception task in which participants intercepted moving targets under systematically varied conditions. We manipulated gravity (1g vs. 0g), visual availability (occluded vs. non-occluded), target velocity, and the initial spatial configuration of the ball and paddle (same vs. opposite side). Results indicate that interception is supported by predictive mechanisms across conditions. Behavioral patterns during occluded 0g trials suggest that participants extrapolate target motion using expectations consistent with gravity. Target velocity, visual occlusion, and task geometry modulated movement strategies, indicating that predictive control is flexibly adapted to task demands. These findings support the view that interception relies on predictive internal models incorporating structured physical priors while revealing flexible, context dependent adaptations to sensory and task constraints
- LARP4 is a B cell-specific metabolic checkpoint for plasma cell differentiation and a therapeutic target in systemic lupus erythematosus
RNA-binding protein LARP4 plays an important role in T cell activation and differentiation, but its role in B cell biology and the pathogenesis of systemic lupus erythematosus (SLE) remains unclear. This study found that LARP4 was specifically highly expressed in B cells of SLE patients and was positively correlated with disease activity. By constructing T cell-specific and B cell-specific conditional knockout mice, we found that deletion of LARP4 in B cells, but not in T cells, significantly alleviated pristane-induced and Bm12-induced lupus nephritis. Further analysis showed that LARP4 deletion selectively inhibited B cell differentiation into plasma cells, but did not affect germinal center B cell formation. Integrated transcriptomic and metabolomics analyses revealed that this effect is due to reduced phosphatidic acid synthesis and decreased mTORC1 activity caused by mitochondrial oxidative phosphorylation dysfunction. Furthermore, we used LIPEP, a LARP4 inhibitory peptide that effectively mimicked the therapeutic effects of LARP4 gene knockout in the MRL/lpr spontaneous lupus model and outperformed cyclophosphamide in reducing glomerular immune complex deposition and improving extrarenal dermatitis. These results indicates that LARP4 is a key metabolic checkpoint regulating B cell differentiation into Plasma cells and suggest that it may be a potential therapeutic target for SLE.
- Pediatric traumatic brain injury elicits acute neuroinflammation and long-term changes in social, cognitive, and decision-making behaviors in male and female rats
Traumatic brain injury (TBI) is one of the leading causes of emergency room visits in children under 10. Children are potentially more vulnerable to the adverse effects of TBI, given that their brains are still developing at the time of injury. Indeed, early life TBI has been linked to cognitive, social, and mood-related impairments later in life. The neuroimmune system has been implicated in adult TBI mechanisms and plays numerous key roles in brain development, making it an interesting candidate for linking pediatric TBI and prolonged behavioral alterations. Here we establish a rat model of mild pediatric TBI to investigate the relationship between early life TBI, acute responses of neuroimmune cells, and chronic behavioral dysregulation. At postnatal day 15, which is roughly equivalent to toddler age, male and female rat pups received a TBI via lateral fluid percussion injury. At 3 days post injury, TBI increased microglia and astrocyte coverage locally in the Perilesional Cortex but not in more distant corticolimbic regions. However, the hippocampus and prefrontal cortex did exhibit increased expression of the phagocytic marker CD68 in microglia, suggesting widespread glial activation even in the absence of gross coverage change. TBI also impacted mast cells, early-response innate immune cells, increasing their number and degranulation in multiple regions. In the juvenile and early adult periods, TBI impaired cognitive function, reduced sociability, and increased avoidance, with no change in anxiety-like behavior. Later in adulthood, TBI continued to impact cognitive behavior, increasing risky decision-making and impairing optimization months after injury. Together, these results suggest that pediatric TBI causes lasting cognitive and social dysregulation, possibly via acute neuroimmune alterations following injury at a critical period of brain development.
- From Real-World Data to Virtual Intervention: A Probabilistic Neural Network for Simulating Kidney Function Preservation via Proteinuria Reduction
Predicting the long-term kidney function decline is critical for timely intervention but remains challenging. While the urinary protein-to-creatinine ratio (uPCR) is a potential surrogate endpoint, its short-term reduction's link to long-term nephroprotection requires investigation. This study aimed to develop a probabilistic neural network model to capture both the estimated glomerular filtration rate (eGFR) slope and its uncertainty based on baseline clinical characteristics. Using a retrospective dataset, we designed a neural network to output a predictive distribution (mean and standard deviation {sigma}) for the eGFR slope. SHAP (SHapley Additive exPlanations) was used for model interpretation, and a simulation study quantified the impact of uPCR reduction. In the validation set, the model achieved a Pearson's correlation coefficient of 0.56 and an RMSE of 2.81 ml/min/1.73m^2/year between predicted and actual slopes. SHAP analysis identified uPCR as the most potent predictor, with higher baseline levels associated with a more rapid eGFR decline. Furthermore, a simulated 62% uPCR reduction demonstrated a significant improvement in the predicted eGFR slope, an effect most pronounced in patients with high baseline uPCR. This proof-of-concept study reinforces the critical role of uPCR in predicting eGFR slope and suggests its reduction may contribute to long-term kidney function preservation, warranting validation in larger, diverse real-world datasets.
- A Multimodal Benchmark for Evaluating Cause-of-Death Inference Using Child Health and Mortality Data
Accurately attributing causes of death is vital for global health, yet fewer than 5% of deaths in resource-constrained regions are medically certified. To assign causes to these unlabeled deaths at scale, practitioners traditionally rely on verbal autopsy, using supervised statistical models to classify based on structured survey data. However, modern mortality surveillance increasingly collects rich, unstructured multimodal data, such as free-text caregiver narratives and postmortem diagnostics, which traditional supervised statistical models struggle to seamlessly integrate. In this paper, we present a comprehensive, multimodal benchmark for cause-of-death classification using data from the Child Health and Mortality Prevention Surveillance (CHAMPS) network, a unique surveillance platform spanning nine countries across South Asia and Sub-Saharan Africa. Using this dataset, we introduce an evaluation framework designed to rigorously assess diagnostic reasoning, moving beyond traditional metrics that fail to capture complex clinical realities. We demonstrate the utility of this benchmark by evaluating zero-shot large language models against supervised baselines across various data modalities. Our results reveal distinct differences in how these modeling approaches synthesize unstructured medical evidence. This benchmark provide a rigorously defined resource for assessing clinical reasoning in next-generation mortality surveillance.
- The Swiss Integrated Care (INCA) Study: Description of a Novel Prospective Cohort of Patients and Caregivers in Reimbursed Informal Care
Methods INCA is a prospective, single-center cohort study with nationwide recruitment. Participation is open to adult patients and informal caregivers who provide paid informal care through home care agencies (Spitex organizations) in Switzerland. Eligible participants are enrolled consecutively. The cohorts primary outcome is health-related quality of life of patients, assessed monthly through patient-reported outcome measures. Secondary outcomes include home care needs, including the overall health and well-being of patients (measured semi-annually), the type, amount, and quality of care (recorded daily), and caregiver burden and resilience (measured quarterly). Additional analysis will include structured medical data, extracted from patient-provided documents using Optical Character Recognition (OCR) technology and analysed using Large Language Models (LLM). Results Since recruiting started in July 2025, the cohort has enrolled 855 patients and 851 caregivers. Among patients, 53% are female, with a median age of 73 years. Caregivers are predominantly female (72%) with a median age of 56 years. Most patients experience impairments in physical functioning and participation in social roles. Among them, 85% require less than two hours of care per day, though care needs vary considerably. This is further reflected in the multi-attribute utility, where the overall PROMIS-Preference (PROPr) score is very low for most patients, with a median of 0.102, indicating a substantial need for medical assistance and care. With a median of 9 comorbidities, health-related quality of life is overall low for most patients. Cardiovascular and endocrine & metabolic diseases are amongst the most prevalent, affecting 69% and 65% of patients with available diagnoses (n=771). Certain diagnostic pairs occur more frequently than expected by chance, suggesting underlying links between disease categories. Conclusions INCA responds to a growing policy need for robust evidence on how new models of informal care are associated with the health and well-being of patients and their caregivers. Its longitudinal design, combining patient- and caregiver-reported data with medical records and innovative data extraction methods, will lay the groundwork for a better understanding of new informal care models in real-world settings. INCAs findings are expected to have significant policy relevance and contribute to evidence-based policies on long-term care at home in Switzerland.
- The topology of adolescent mental health
The increased vulnerability to mental health problems in adolescence is frequently reported but poorly understood, hampered by a rigid diagnostic system which fails to capture intertwining symptoms and only loosely aligns with biological axes of variability. Here, we reconceptualised the mental health symptoms of young adolescents in the ABCD cohort (N=11862) as a latent topology of overlapping symptom dimensions, using an unsupervised machine learning algorithm to establish how transdiagnostic dimensions co-occur and overlap within individuals. Combining this with a novel classification approach, we delineated zones within this landscape, within which specific profiles of symptoms were robustly represented. These data-driven profiles were leveraged to establish associated resting-state functional connectivity and genetic characteristics. In doing so we recaptured the commonly reported p-factor axis as well as further symptom-subtype dimensions. Gene ontology analysis revealed that shared neurobiological and cellular mechanisms embedded in both the genome and transcriptome may confer risk for psychopathology.
- Evaluation of four large language models on complex, infectious disease case scenarios
Objectives: Large language models (LLMs) are increasingly used in medicine, but evaluation is often on multiple choice questions and management of common conditions. Infectious diseases (ID) can present complex scenarios that require considerations beyond guideline-based responses. We assessed LLM performance in these situations including with ID-specific criteria to consider infection control or antimicrobial stewardship (AMS). Methods: We evaluated four LLMs (Claude 3.5 Sonnet, GPT-4o, GPT-o1, and a local instance of Llama 3.1 8B) in October 2024, on five complex ID vignettes. The LLM responses were each evaluated for 18 items by two board-certified ID clinicians and pairwise comparisons were performed between LLMs. Results: There was no significant difference between performance of GPT-o1, GPT-4o and Claude Sonnet on general medical criteria, and were comparable with respect to how often they provided an unsafe response (GPT-o1 30%, GPT-4o 40%, Claude 37%) and contained a critical omission (GPT-o1 27%, GPT-4o 43%, Claude 47%). Llama 3.1 8B had significantly decreased performance for most criteria. On ID-specific criteria, GPT-o1 outperformed other models and all models significantly outperformed Llama for interpreting microbiology results, AMS principles, appropriate antimicrobial spectrum and infection control considerations. Performance was poor in secondary prevention and management of risk factors. Conclusions: On complex ID scenarios, LLM responses were variable. The open-source, smaller Llama 3.1 8B model performed poorly and large, non-reasoning models varied, but more than 30% of responses containing a risk of harm or critical omission. These findings suggest caution is required when deploying these models in ID domains without specialist oversight.
- An Initial Genetic Correlation Analysis of Externalizing Behavior and Neuroimaging Phenotypes in the ABCD Cohort
Adolescent externalizing behavior is a major risk factor for later substance use and other psychiatric outcomes. Understanding its genetic architecture and its relationship with brain imaging phenotypes requires scalable genome-wide methods applied to youth cohorts. Using data from the Adolescent Brain Cognitive Development (ABCD) Study, we implemented a pipeline for genome-wide association studies (GWAS) of longitudinally measured externalizing traits and multimodal neuroimaging-derived phenotypes (IDPs). We performed quality-controlled genotype processing and constructed harmonized phenotype and covariate datasets. GWAS analyses were conducted using REGENIE in a two-step framework, with Step 1 ridge regression models trained on LD-pruned variants and Step 2 association testing performed genome-wide. Externalizing traits measured at baseline and summarized as longitudinal means and slopes, together with approximately 200 IDPs measured at baseline and summarized as longitudinal means and slopes, were analyzed. We further constructed a custom linkage disequilibrium (LD) reference panel using unrelated individuals and computed LD scores using LDSC. Genetic correlations between externalizing traits and imaging phenotypes were estimated using LD Score Regression. This exploratory study systematically evaluated genome-wide genetic correlations between regional cortical morphology and externalizing phenotypes in adolescence. Although several associations reached nominal significance, none remained significant after correction for multiple comparisons. These findings should not be interpreted as demonstrating an absence of shared genetic architecture. Rather, the precision of the estimates was constrained by the available imaging GWAS sample size, uncertainty in SNP-heritability estimates, and the large number of regional comparisons. Larger imaging-genetics samples and independent replication will be required to determine whether modest or regionally specific genetic correlations exist.
- Should kids use Google AI search? These experts say no.
Experts say Google AI Search is 'unacceptably risky.'
- Data-driven calibration of low-cost wearable motion trackers for gait and dynamic stability measurement
Low cost inside out wearable trackers can be deployed at scale to measure body motion, but errors in estimated sensor position propagate through coordinate transformations into derived gait and dynamic-stability metrics. Healthy adults walked on a treadmill at 0.5 to 2.0 m/s while VIVE Ultimate Tracker (VUT) and Vicon data were recorded. Data-driven calibration models were developed to correct tracker coordinates and to estimate full body centre of mass (CoM) from a sacrum-only configuration. Agreement with Vicon was assessed using RMSE, mixed-effects Bland-Altman limits of agreement, MAE, and intraclass correlation coefficients. Calibration improved coordinate-level agreement. For gait parameters, model-corrected VUT showed small errors against Vicon (MAE: 0.24 to 0.71 mm step height, 1.73 to 4.63 mm step length, 0.15 to 0.95 mm step width, 0.26 to 0.88 mm foot clearance). Proxy CoM-derived margin of stability (MoS) agreed excellently with Vicon. For the sacrum-only pipeline, calibration reduced CoM RMSE from 103.65 to 104.04 mm to 7.55 to 8.95 mm, and markedly reduced systematic error in stability outcomes, with extrapolated CoM bias decreasing from 172.92 to 0.29 mm and MoS bias from -75.09 to -3.54 mm. Data-driven calibration improved the measurement utility of low-cost VUTs, enabling inexpensive, relatively simple gait and stability measurement from a sacrum-only setup in controlled settings.
- SENSChat
AI-powered decision network for smarter choices
- Microsoft Gives Sellers Tips to Knock Down Anthropic, OpenAI
Microsoft Corp. is coaching its sales team on how to vigorously compete against Anthropic PBC and OpenAI, playing up shortcomings in products from the artificial intelligence firms.
- OpenAI's New $230 Mini Keyboard Is for Codex Power Users
OpenAI's New $230 Mini Keyboard Is for Codex Power Users Business Insider
- OpenAI Just Launched Its First Hardware Product—and It’s a Tiny Keyboard for Bossing Around AI Agents
The AI company teamed up with Work Louder on a programmable keyboard designed specifically for Codex.
- OpenAI launches a physical keypad for controlling agents
OpenAI's collaboration with keyboard maker Work Louder is available to order today.
- OpenAI Is Selling a $230 Keyboard Designed for Its Codex Agent
OpenAI Is Selling a $230 Keyboard Designed for Its Codex Agent PCMag
- OpenAI Is Selling a $230 Keyboard Designed for Its Codex Agent
OpenAI Is Selling a $230 Keyboard Designed for Its Codex Agent PCMag Australia
- OpenAI Is Selling a $230 Keyboard Designed for Its Codex Agent
OpenAI Is Selling a $230 Keyboard Designed for Its Codex Agent PCMag UK
- OpenAI's First Hardware Release Turns Out to Be Keypad for Codex
The keypad is for helping monitor your Codex agents, not keeping you company.