AI News Archive: July 17, 2026 — Part 11
Sourced from 500+ daily AI sources, scored by relevance.
- Powerball Prediction Today
The best mirofish website for Powerball
- Otagon
Spoiler-free game hints on your phone—never alt-tab again
- SpaceRush
Compete to become the world's fastest spacebar player
- bip.guide
Build in public guide
- IntellIQ
Run your whole business from one AI-powered platform.
- Orizen Panel
Modern open-source self-hosted hosting control panel
- RecipePT
An AI that decides what's for dinner and learns your taste
- Gtartistoo tattoo supply
Fluorescent Tattoo Ink Set 14 Colors
- Lavender Fields Argan Oil Body Butter
Lavender Fields Argan Oil Body Butter - 1 Gallon
- AI Deck Creator
Turn content into a polished slide deck.
- remio: Your Personal ChatGPT
Get Tailored Answer with Your Personal ChatGPT
- Home Design AI
Redesign any room or home with a single photo.
- FreeOCR.AI
Convert document images into editable Word, Excel, and structured data.
- AdsCreator.com
Turn any website into scroll-stopping ads.
- Script.it
Describe it in English, automate it instantly.
- AISafe Labs
AI agents that assess your app with hacker expertise at machine speed.
- Turning an object into a scene: buildings activate scene-selective visual cortex independently of visual features
Human visual cortex contains regions that selectively respond to both scenes and large objects, particularly buildings. The cortical overlap between buildings and scenes has been attributed to shared visual features (e.g., cardinal orientations, rectilinearity). Alternative accounts propose that buildings may also activate scene representations indirectly, independently of specific visual features, for example because buildings evoke a sense of space. Here, we tested for such feature-independent activation by comparing EEG and fMRI responses in human participants (both sexes) to buildings and visually-matched boxes, and relating these responses to scene-selective responses. Buildings and boxes were matched, across exemplars, using image-based metrics, deep neural networks, and a perceptual similarity task. Time-resolved EEG decoding showed that buildings and boxes evoked discriminable responses from 360ms post-stimulus onset, incompatible with feedforward visual feature processing. Importantly, the building-box classifier generalized to discriminate scenes from chairs, providing EEG evidence for a representational overlap between buildings and scenes. Temporal generalization analyses further showed that the late building-selective response corresponded to an earlier scene-selective response, with a temporal offset of ~130ms. Finally, ultra-fast fMRI (TR=140ms) revealed that these findings were mirrored in the response of the scene-selective parahippocampal place area (PPA), which similarly showed a feature-independent building-selective response that was delayed and prolonged relative to the scene-selective response. These results clarify the nature of building selectivity in visual cortex by showing that this selectivity can arise independently of visual features, putatively reflecting associative processes between buildings and scenes (or space).
- Multiscale learning and topological analysis across complex postures enable robust nematode size quantification in pharmacological assays
Body size is an important trait that reflects animal development and physiology. In nematodes, precise measurement is valuable for linking variation in body dimensions to biological questions such as developmental timing, genetic regulation, and drug responses. However, robust size measurements can be difficult to obtain because nematodes can vary in curvature, have self-intersecting postures, and overlap with neighboring animals. Current image analysis software, such as CellProfiler, can measure isolated animals in straight postures but struggles with curly or overlapping animals. Here, we present NemaSize, an artificial intelligence (AI)-aided pipeline to measure Caenorhabditis nematode body sizes across complex postures using multi-scale learning and topology-aware skeletonization. Using You Only Look Once (YOLO) models trained at different spatial scales, NemaSize first identifies individual animals in a large field of view (FOV; 6.75 x 6.75 mm) and then performs high-resolution body segmentation in the region of interest (ROI). Next, NemaSize converts segmented body masks into topological graph representations, allowing curly and overlapping animals to be classified and skeletonized according to the body topology. NemaSize achieved less than 4% overall error in length and width measurements across all posture classes. Compared to CellProfiler, NemaSize demonstrated higher robustness for complex postures, including a 48% error reduction in length measurements for curly or self-overlapping animals. Application of NemaSize in high-throughput imaging assays further shows that NemaSize provides accurate quantification of Caenorhabditis briggsae larval development in response to the anthelmintic drug ivermectin, a task that was difficult for CellProfiler because of curly animal postures. Together, NemaSize provides a robust approach for automated body size quantification for Caenorhabditis nematodes and will support broad applications in high-throughput pharmacological and genetic screens. Beyond nematodes, NemaSize introduces a multiscale computational framework for analyzing elongated biological objects with complex topologies.
- Multi-modal MRI characterisation of vascular remodelling, muscle fibre integrity, and inflammatory recovery in a hindlimb ischaemia mouse model
The hindlimb ischaemia (HLI) mouse model is a widely used preclinical model of chronic limb-threatening ischaemia (CLTI). While CLTI involves complex interactions between impaired perfusion, inflammation and muscle wasting, the standard imaging approach, laser Doppler imaging (LDI), only assesses perfusion. MRI is used clinically to assess neural tracts, inflammation, and perfusion in the brain. We therefore evaluated whether a multimodal MRI approach could longitudinally monitor recovery in the HLI mouse model. Mice underwent MRI three days pre-HLI surgery, and on Days +3 and +7 post-surgery, with histology on Day +7. The MRI detected significant increases in muscle volume and inflammation after HLI surgery, with significant decreases in perfusion, vascular length, and muscle fibre integrity. Overall, MRI can monitor inflammation, muscle fibre integrity, and vascular recovery post-HLI and should be applied in future studies to identify mechanisms of therapeutic recovery in a sequential in vivo analysis without requiring animal sacrifice.
- Fluorescence cross-correlation spectroscopy quantifies affinity, cooperativity, and kinetic stability in ternary protein complexes
Many biological processes and emerging therapeutic modalities rely on higher-order protein complexes whose properties cannot be predicted from their constituent binary interactions. However, methods for directly quantifying affinity, cooperativity, and kinetic stability within such assemblies remain limited. Here, we establish fluorescence cross-correlation spectroscopy (FCCS) as a solution-phase approach for characterizing multicomponent protein interactions and apply it to the clinically important HER2-targeting antibodies trastuzumab and pertuzumab. Using fluorescently labelled HER2, trastuzumab, and pertuzumab, we quantified binary binding affinities, directly measured ternary complex formation, and characterized the dissociation kinetics of binary and ternary complexes. FCCS measurements revealed positive cooperativity in the formation of the HER2-trastuzumab-pertuzumab ternary complex, while dissociation experiments demonstrated that the ternary complex is kinetically more stable than the corresponding binary interactions. Together, these findings provide direct solution-phase evidence that cooperative interactions stabilize the HER2-trastuzumab-pertuzumab complex, offering a molecular explanation for the enhanced efficacy of dual HER2 targeting in cancer therapy. More broadly, this work demonstrates that FCCS can robustly quantify affinity, cooperativity, and kinetic stability of multicomponent protein complexes using a commercially available platform. We provide a broadly accessible framework for studying higher-order protein interactions and supporting the development of next-generation multispecific and combination therapeutics.
- MEGAHIT k-mer range tuning trades computational efficiency for improved recovery of functional genes across cave sediment and wastewater metagenomes
Shotgun metagenomics is a powerful approach for profiling complex microbial ecosystems and discovering functional genes, including antimicrobial resistance genes (ARGs) and biosynthetic gene clusters (BGCs). De novo assembly with tools such as MEGAHIT commonly uses multiple k-mer lengths, but the effect of reduced k-mer sets on functional gene recovery has received limited attention. Here, we quantify the trade-off between assembly speed and functional-gene recovery using 17 cave sediment metagenomes and 10 wastewater metagenomes assembled under 19 MEGAHIT k-mer scenarios. In cave metagenomes, finer-grained k-mer ranges recovered more BGCs and, in several pairwise comparisons, more ARGs, but required longer runtimes. In wastewater metagenomes, finer-grained settings most clearly affected ARG recovery, whereas BGC counts did not differ significantly after Friedman testing. These results indicate that reduced k-mer sets can lower computational cost but may miss biologically relevant functional signal, depending on the dataset and downstream target. The study provides a quantitative basis for selecting MEGAHIT k-mer parameters according to whether computational efficiency or functional gene discovery is the primary aim.
- PathoBench: an open community-driven benchmark registry for pathogen bioinformatics tools
The proliferation of pathogen bioinformatics pipelines has outpaced the community ability to compare them on common ground. Self-reported performance numbers, ad-hoc evaluation datasets, and inconsistent metrics make pipeline selection difficult for clinical and public-health researchers. We present PathoBench, an open web platform that addresses this gap through three coordinated mechanisms: (i) a curated registry of 26 standard benchmark datasets across 10 human pathogens, each with persistent identifiers and direct download links; (ii) pathogen-specific evaluation metrics that submissions must report, allowing direct head-to-head comparison only on the same dataset; and (iii) a credibility framework combining mandatory dataset attestation, ORCID-linked attribution, public peer comments, and administrator verification. As a case study, four published Mycobacterium tuberculosis drug-resistance pipelines were evaluated against the WHO TB mutation catalogue, demonstrating the framework discriminating power. PathoBench is open for community contributions across all ten supported pathogens.
- Recent social loss, not chronic isolation, reshapes sleep in Drosophila
Drosophila melanogaster is widely used to study social experience, yet it remains unclear whether sleep phenotypes attributed to isolation reflect chronic deprivation or the consequences of a recent change in social state. Many fly studies alter social context again at the time of testing, making these possibilities difficult to distinguish. Here, we developed sociSleep, an identity-preserving tracking system that quantifies sleep without inducing social-state transitions. Using this approach, we found that chronic social isolation had little effect on sleep, whereas recent social loss robustly increased sleep. These findings show that recent social experience, rather than chronic isolation alone, is a major modulator of sleep in flies.
- Frequent errors are the worst: robustness to individual failures in collective foraging
The collective behavior of complex systems emerges from the actions and interactions of their individual components. But what if these individuals make mistakes, or deviate from behavior tuned to lead to collective success? Here, we explore the effects of individual errors on collective outcomes. We investigate in particular whether information flow among individuals exacerbates or mitigates such individual failures. We use an agent-based, spatially explicit model inspired by collective foraging in social insects. Social insect colonies forage for food with autonomous workers who search for and exploit resources around the central nest, as well as share information about discovered resources. We find that the errors that have the most chances of occurring had the strongest impacts: for example, false positive detections can occur at any time during search, and each such error derailed exploration activity. Similarly, forgetting errors are potentially frequent and detrimental to resource exploitation. Despite the fact that communication inherently may narrow the breadth of information used by a colony, we found, in contrast, that it enhanced spatial exploration in our model. Communication in our model also reduced the effects of individual errors, instead of permitting erroneous information to spread. Our model thus illustrates that communication plays a central role in error management in complex systems, and that the evolution of communication systems in social insects may be shaped by selection on exploration and error mitigation as well as on efficient food retrieval.
- ITPKB is a conserved regulator of natural killer cell desensitization/education that constrains antitumor immunity
Natural Killer (NK) cell desensitization induced by persistent stimulation limits durable antitumor immunity, yet the molecular mechanisms governing this dysfunctional state remain poorly defined. To identify conserved regulators of NK cell desensitization, we performed comparative transcriptomic analyses across multiple murine models of persistent activation and dysfunction. This approach defined a shared transcriptional program of desensitization and identified Itpkb to be upregulated across multiple distinct contexts. Genetic and pharmacological inhibition of ITPKB enhanced degranulation, cytokine production, and cytotoxicity in both murine and human NK cells under multiple desensitization settings. Mechanistically, ITPKB regulated signaling downstream of persistent activation through the IP3/IP4 axis, limiting calcium mobilization and NFAT-dependent transcriptional responses in desensitized NK cells. Furthermore, inhibition or deletion of ITPKB enhanced NK-cell mediated tumor control in vivo and improved the efficacy of adoptively transferred CAR-NK cells, underscoring the translational potential of targeting this pathway. Together, these findings identify ITPKB as a cell-intrinsic regulator of NK cell desensitization and support targeting the IP3/IP4 signaling axis to enhance NK cell-mediated antitumor immunity.
- Integrating activation-induced costimulation and cytokine signals enhance TCR-based cell therapies
TCR based cell therapies offer broad targeting of tumor antigens with high sensitivity but often low durability due to insufficient costimulation (signal 2) and cytokine (signal 3) support. To address this, we developed a dual stimulatory receptor (DSR) consisting of the 4 1BBL ectodomain fused to the thrombopoietin receptor (cMPL) endodomain. DSR engages 4-1BB that is transiently upregulated upon TCR stimulation eliciting signal 2 and simultaneously activates cMPL driven STAT3/5 phosphorylation providing signal 3. DSR increases the expansion of T cells, preserving their effector function and an effector-associated transcriptional profile upon repeated antigen stimulation. DSR arming significantly improves in vivo antitumor activity of T cells redirected to cancer through engineered TCRs or soluble T-cell engagers in diverse xenograft tumor models by enhancing T cell expansion and persistence post-infusion. These results demonstrate broad utility and establish DSR as a modular receptor for the effective and synchronized delivery of signals 2 and 3 to support TCR based immunotherapies.
- Neural correlates of the subjective experience of free-will during value-based risky decisions: a pilot study
Underlying the very notion of choice is the fundamental idea of free-will, which is challenged by decision-neuroscience aiming to explain and predict choices using interactions of neurons. The question of whether any choice is truly a free-choice and born out of free-will has long been a subject of philosophical debate. In this work, we do not take a position on this debate, rather investigating the subjective experience of free-will, whose existence is more universally accepted. We had healthy participants report the level of their experienced free-will while performing value-based risky decision-making task to find the neural and behavioral correlates of this experience. We identified regions in mid-cingulum and middle frontal gyrus showing positive association with self-reported free-will as well as a region in hippocampus and parahippocampal gyrus showing a negative association. The requirement to report the experience of free-will was associated with a higher BOLD signal in striatum during decision-making. Behaviorally, we found a positive trend between RT and free-will. While our sample size is small, these results help forming hypotheses for further studies with larger cohorts and provide a proof-of-concept for the investigations of neural and behavioral mechanisms of the subjective experience of free-will in decision research.
- Coarse-grained simulations of long intrinsically disordered proteins: a benchmark of Martini 3 force-fields
Martini 3 is a force field ideally suited to simulating long intrinsically disordered proteins (IDPs) in cell-like surroundings. So far, most Martini 3 variations intended for IDPs have only been benchmarked on shorter IDPs of up to 140 amino acids. In this paper, we present a comprehensive benchmark including IDPs up to 809 amino acids in length and compare the behavior of four well-known Martini 3 variations for IDPs. Modifications to only the bonded parameters result in excessively compact conformations, thereby failing to reproduce the experimental radius of gyration observed for large IDPs. In contrast, general rescaling of interaction parameters, including tuning electrostatic interactions in the case of highly-charged long IDPs, yields acceptable levels of compaction at all tested length scales.
- Excited state Relaxation Activation Energy (ESRAct) of Di-4-ANEPPDHQ Maps Nanoscale Molecular Organization in Biomembranes
Live cell plasma membranes show spatially heterogeneous liquid-ordered (Lo)-like and liquid-disordered (Ld)-like regions similar to the co-existing Lo/Ld phases observed in lipid vesicles. The Lo-like regions are relatively less hydrated and less polar due to tight packing of the membrane components compared to the Ld-like regions. The steady-state fluorescence spectra of Di-4-ANEPPDHQ (Di-4), a widely used polarity-sensitive probe, is blue or red shifted when solvated in less polar (Ld-like) or more polar (Lo-like) regions respectively. However, quantification of Di-4 fluorescence in blue and red channels for the evaluation of membrane phase state suffers from the lack of specific wavelength choice for these two channels and relatively higher concentration of Di-4 in Ld phase (red channel) due to its partitioning preference. To address these issues, we employed fluorescence lifetime of Di-4, a concentration independent photophysical parameter, to understand membrane biophysical properties. The fluorescence lifetime of Di-4 in lipid vesicles exhibits Arrhenius-like temperature dependence. Centred around this energetic feature of Di-4 photophysics, we developed a novel analytical module, namely excited state relaxation activation energy (ESRAct), that serves as an intrinsic descriptor of the membrane nano-environment sensed by this probe. We show that the ESRAact value scales with increasing disorder in nanoscale phase separation (i.e., ESRAct of pure Ld > mixed Ld/Lo > pure Lo phase). We then extended its applications to giant plasma membrane vesicles (GPMVs) isolated from MCF-7 cells and found that these vesicles exhibit nanoscale Lo/Ld co-existing phase within 16-37C. We envisage wide applications of ESRAct to delineate plasma membrane phase behavior as well as general photophysical studies on other newly designed polarity-sensitive probes.
- Mechanosensation, habituation, and behavioural plasticity in tintinnid ciliates
Unicellular organisms sense, integrate and respond to environmental cues despite lacking neurons or a nervous system. Several species, notably ciliates, are capable of adaptive behaviours normally associated with multicellular animals. Integral to marine ecosystems are planktonic ciliates called tintinnids, which feed on microbes and serve as prey for larger predators. In nature, they swim, feed, hunt, and build ornate loricas, while experiencing varied mechanical and flow perturbations from encounters with other organisms and their environment. Here, we investigated mechanosensory responses in tintinnids at single-cell resolution using temporally controlled touch and vibrational stimuli. Localised touch stimulation of the ciliary band triggered stereotyped ciliary reversals and beat frequency elevation, but strong vibrational stimuli elicited rapid whole-cell contractions. Repeated stimulation produced a progressive decline in response probability that depends on stimulus frequency, with spontaneous recovery if stimulation is withheld. Our work identifies a novel form of cilia-associated habituation response to mechanical stimulation in tintinnids that is distinct from contractile whole-body responses previously reported in other protists. The results show how motility and sensory feedback are tightly coupled to coordinate cellular information processing and a hierarchy of mechanosensory responses in a single-celled organism.
- Mapping absolute membrane voltage using dynamic photocycle control
Fluorescent voltage indicators are widely used to report relative changes in membrane potential, but mapping absolute voltages remains difficult. Here we present Voltage Measurement by Activated Photocycles (VMAP), a simple method for absolute voltage imaging based on a photophysical switch between voltage-insensitive and sensitive indicator states. VMAP requires no specialized hardware or additional labeling, and is applicable across species, sample preparations, and microscope configurations. Using VMAP, we quantified drug-induced shifts in neuronal resting potential, revealed the emergence of bioelectric patterns during multi-day recordings of human iPSC populations, and created 3D membrane-potential maps across whole live zebrafish embryos. By making absolute voltage imaging accessible from cellular to organismal scales and from milliseconds to days, VMAP opens a route to mapping bioelectrical organization in complex living systems.
- Textural features for pathway-level representation of omics data in biological networks
More than 50 years ago, Haralick and co-authors proposed a family of gray-level co-occurrence statistics that became known as textural features. These features are widely used in image analysis, but their application to biological networks has remained limited because cellular networks are sparse, irregular graphs rather than regular pixel grids. This work presents a network-adapted version of Haralick texture analysis for generating pathway-level features from gene-level omics profiles. The resulting profiles reduce dimensionality and can be used as candidate predictors of anti-cancer drug response. Performance of these features is compared with original gene expression variables and with pathway features from network enrichment analysis (NEA), whose robustness has been demonstrated previously. Although technically simpler than NEA, Haralick features showed comparable sensitivity. More importantly, selected Haralick features were preserved between in vitro drug screens and clinical treatment-associated survival analyses, supporting their potential use for prioritizing robust pathway-level drug-response correlates.
- Computational Counterfactuals Reveal Non-Additive Audiovisual Semantics in Natural Movie Responses
Natural audiovisual perception may not be fully captured by decomposing movies into auditory and visual streams. I introduce a computational-counterfactual framework that keeps movie viewing intact while varying only AI-derived descriptions of the same clips. Using 7 Tesla movie fMRI imaging data from 176 participants, I tested whether cortical responses were better predicted by native audiovisual semantics than by a dimension-matched additive reconstruction from audio-only and video-only descriptions. The native model outperformed the matched additive baseline under content-aware purged cross-validation, with strongest gains in auditory, visual, and dorsal attention systems. Representational-similarity, feature-replacement, and content-gating analyses showed that the advantage reflected feature- and network-specific routing linked to coherent audiovisual semantic emergence rather than raw auditory-visual discrepancy. The effect survived stronger temporal purging and repeat-content exclusion, suggesting that intact movie viewing evokes cortical structure aligned with native audiovisual meaning beyond additive unimodal semantics.
- Glial subcellular specialisation resolved with high resolution spatial transcriptomics
Cells in the brain have complex structures with extended processes. This complex morphology supports diverse specialized functions in health and disease, and specifically, cell processes appear to be critical for cellular integration and signalling. Here, we developed a new spatial averaging framework to recover and interrogate molecular phenotypes of glial processes in spatial transcriptomics (ST) data. We characterised cell type specific signatures associated with processes of astrocytes and microglia in both mouse and human brain tissue. Astrocytic processes were enriched for transcripts related to neuronal support relative to their soma, while microglial processes preferentially expressed genes liked to specific microglial states. When investigated in tissue from brains with Alzheimer's Disease (AD) pathology, we found that local amyloid-beta pathology was associated with subcellular differences in transcriptomes in both an amyloid-beta mouse model and human AD patient tissue. Specifically, astrocytic and microglial processes oriented towards amyloid-beta plaques exhibited distinct molecular changes in comparison to processes extending away into plaque free areas, suggesting polarized glial responses to pathology. Our work thus outlines a general method for the selective characterisation of transcriptomics of glial processes in mouse and human ST data and provides evidence for differential transcriptomic responses between the soma and processes of glia in health and disease.
- Food Ration Affects mRNA Processing, Translation, Proteostasis, and Cytoskeletal Responses During Heat Shock in Mytilus californianus
Despite a likely role in setting stress tolerance limits, food ration has received limited attention as an ecological factor affecting the cellular stress response (CSR). To study the interactive effects of food and temperature on the proteomic heat shock response, we acclimated intertidal mussels (Mytilus californianus) to four combinations of nearshore (low) and aquaculture (high) phytoplankton levels, combined with low (20 {degrees}C) and high (30 {degrees}C) aerial temperatures during daytime low tides. Mussels were then exposed to an acute (6 h), aerial heat stress (33 {degrees}C) and allowed to recover for 1 h and 25 h in pre-exposure conditions. Proteomic changes in the gill before and after heat shock were measured using label-free liquid-chromatography-mass spectrometry. Compared to other acclimation treatments, low-food-low-temperature (LTLF) mussels modified more splicing factors, heterogeneous nuclear ribonucleoproteins, ribosomal proteins, and translation initiation and elongation factors, suggesting systemic changes to RNA processing, selection, and translation. LTLF mussels also increased chaperones of the actin- and microtubule-associated cytoskeleton and proteins that mature along the endoplasmic reticulum to Golgi secretory pathway. Simultaneously, actin stress fiber formation at focal adhesions and the extracellular matrix, along with anchoring of microtubule-associated cilia, indicate a possible system-wide mechanical breakdown of the cytoskeleton. Signaling proteins causing cytoskeletal changes varied mainly in LTLF mussels and suggest a food-dependent role for prostaglandin synthesis. Overall, the acclimation-dependent proteomic changes show how thermal conditioning and food ration together will shape the CSR of mussels during heat waves.
- E-HAPLOS: Electrical Impedance Human-guided Assesment with Pressure for Lump Observation System
Breast cancer is the leading cause of cancer-related deaths among women in the Philippines. Over 65% of these cases are diagnosed when they are advanced (Montemayor, 2023). This highlights the need for improved early screening devices. E-HAPLOS, or Electrical Impedance Human-guided Assessment with Pressure for Lump Observation System, is a low-cost glove with sensors designed to improve early detection of suspicious breast lump through touch. It integrates force-sensitive resistors (FSRs) to measure tissue stiffness and Electrical Impedance Spectroscopy (EIS) to analyze conductivity across different frequencies, properties that are closely linked to breast cancer. The prototype uses an ESP32 microcontroller that transmits real-time pressure and impedance data to the website. Tested on gelatin breast models with simulated lump, the FSRs effectively identified lump locations by recording higher mean force values (45.81 kPa vs. 33.57 kPa). This guided approach allowed the combined FSR-EIS system to reach a diagnostic performance with an Area Under the Curve (AUC) above 0.94, a significant improvement over unguided measurement (AUC 0.78). A two-way ANOVA confirmed a significant difference in diagnostic performance based on the system modality (p < 0.001). Tukey's Honesty Significant Difference (HSD) test showed that the FSR-EIS system was statistically superior to both the unguided EIS (p < 0.001) and FSR-only system (p = 0.041). Results demonstrate the synergistic effect of the integrated system, enabling accurate differentiation of suspicious lumps from normal tissue. The FSR-EIS system of the E-HAPLOS glove shows a great potential for detection of lumps in simulated breasts as a screening tool.
- How specific structural differences of Bcl2 proteins modulate the interaction with BH3 domains and apoptotic function
Intrinsic apoptosis is mainly regulated through a network of conserved interactions between Bcl-2 proteins involving hydrophobic binding grooves and BH3 domains. Despite these conserved interfaces, family members exhibit distinct binding affinities and play opposing roles in apoptosis. While static structural differences partially account for this divergence, it remains unclear how opposing apoptotic function reflects in BH3 helix engagement of individual members. Here, we investigate how a BidBH3 peptide engages with the hydrophobic groove of full-length membrane-anchored Bcl-xL and Bax to identify shared and unique features of binding that may relate to distinct apoptotic functions. Using state-of-the-art enhanced-sampling simulations, we mapped the complete binding and folding landscapes of these critical cell-death regulators in membranes. Our simulations align with experimental measurements in terms of predicted absolute binding affinities, and also capture the dynamic, atomistic details of the conformational changes induced by BH3 helices. Together, these details highlight the structural principles of BH3 in-groove engagement that determine apoptotic function, paving the way towards the modulation of the interactions among the Bcl-2 family members.
- Abdominal-B neurons selectively drive vibrations in Drosophila
Male Drosophila courtship includes two communication signals: airborne song and substrate-borne vibrations. While the neural control of song has been extensively characterized, little is known about the circuits underlying vibration production. Here, we identify neurons expressing the Hox gene abdominal-B (abdB) as a driver of vibration production. Optogenetic activation of abdB neurons selectively elicited vibrations in both males and females without inducing courtship song, whereas silencing these neurons did not impair vibration production during natural courtship. The vibration-driving abdB neurons are neither doublesex- nor fruitless-positive, defining a previously unrecognized component of the courtship circuit. Although abdB activation produced only stimulus-locked vibrations, co-activation of the persistence-promoting neuron cluster pCd converted this transient signal output into sustained vibration trains. Together, our results identify a dedicated pathway for vibration production and show that signal identity and persistence can be independently specified by distinct circuit components.
- Trends and Future Burden of Major Gastrointestinal Cancers in Jiangsu Province, China, 2010-2030
Aim: To assess temporal trends in incidence and mortality and project the future burden of five major gastrointestinal cancers in Jiangsu Province, China. Methods: Population-based cancer registry data from Jiangsu Province between 2010 and 2021 were used to analyze the burden of esophageal, gastric, colon, rectal, and liver cancers. Age-standardized incidence and mortality rates were calculated and compared by cancer type, sex, and urban-rural residence. Joinpoint regression was used to estimate annual percentage changes (APC) and average annual percentage changes (AAPC). The APC from the most recent Joinpoint segment was used to project incidence and mortality rates to 2030. Results: In 2021, gastric cancer had the highest age-standardized incidence and mortality among the five cancers. Incidence and mortality were consistently higher in males than in females and increased markedly after 50 years of age. From 2010 to 2021, age-standardized incidence and mortality declined for esophageal, gastric, and liver cancer, but increased for colon and rectal cancer. Colon cancer showed the steepest increase in both incidence and mortality. Rural areas experienced faster increases in colon and rectal cancer burden than urban areas. Projections to 2030 suggest continued declines in esophageal, gastric, and liver cancer, while colon cancer incidence and mortality are expected to rise further. Conclusion: Jiangsu Province is experiencing a transition in gastrointestinal cancer burden, with continued declines in esophageal, gastric, and liver cancers but an emerging and growing burden of colorectal cancer, especially colon cancer. Prevention strategies should focus on expanding colorectal cancer screening and early diagnosis, particularly in rural areas, while sustaining control of esophageal, gastric, and liver cancers.
- Resolving early cochlear inflammation prevents lasting damage from noise exposure
Noise-induced hearing loss (NIHL) is a leading cause of permanent hearing impairment worldwide, yet no pharmacological therapies are currently available to prevent or treat this disorder. Although inflammation is increasingly recognized as a key contributor to cochlear degeneration, the therapeutic potential of targeting early inflammatory signaling remains poorly understood. Here, we combined phenotypic screening in zebrafish with mechanistic and functional validation in complementary mouse models to identify quinoxaline derivatives with otoprotective activity following acoustic trauma. Lead compounds preserved cochlear synapses and auditory function after moderate noise exposure, while one derivative also protected sensory hair cells in a model of permanent hearing loss. Mechanistic analyses demonstrated that this protection was associated with attenuation of early NF-{kappa}B signaling and modulation of the cochlear inflammatory response toward a reparative state, consistent with suppression of pathogenic innate immune activation before irreversible tissue damage occurred. Together, these findings identify early NF-{kappa}B-dependent inflammatory signaling as a therapeutically actionable mechanism in NIHL and establish quinoxaline derivatives as promising candidates for pharmacological intervention. More broadly, this work demonstrates the utility of a cross-species discovery platform for identifying therapies that preserve sensory function by targeting early inflammatory pathways.
- A ReAct Agentic AI System for Natural Language Querying and Statistical Analysis of The Cancer Genome Atlas Clinical Data
The Cancer Genome Atlas (TCGA) holds clinical data for over 11,000 patients across 33 cancer types, but access is hard because of complex file structures, heterogeneous formats, and the need for programming. We present an agentic system for natural language querying and statistical analysis of TCGA clinical data. The system uses a large language model as an autonomous ReAct agent that selects from eight computational tools, including data extraction, descriptive statistics, Kaplan-Meier survival analysis with log-rank tests, hypothesis testing, and verification against the curated TCGA Pan-Cancer Clinical Data Resource (CDR). The agent reasons about intermediate results, adapts its approach, and returns clinically contextualized responses with source attribution and auditable traces. We introduce TCGA-Agent-Bench, 440 queries across five difficulty tiers with ground truth from the independently curated TCGA-CDR, evaluated with dual metrics of numerical accuracy and clinical completeness. The system achieves 93.4% overall accuracy (100% single-patient lookups, 99.1% cohort statistics, 92.8% comparative analyses), outperforming a fixed rule-based pipeline (87.1%), a single-pass LLM (81.8%), and retrieval-augmented generation (66.9% on a subset). Most of the benchmark is answerable from the CDR alone, so we locate the extraction layer's value in fields the CDR lacks (drug treatments, TNM components, biomarkers, biospecimen metadata): on 26 queries targeting these, the full system answers 100% versus 3.8% for CDR-only. Ablations show the reasoning loop is most impactful (+9.1% accuracy, +22.0 completeness points). A tool-based agentic architecture enables accurate, auditable analysis of clinical repositories, with value driven by tool design and recovered fields rather than model scale.
- Identification of collagen features predictive of recurrence following radiotherapy for localised prostate cancer: a retrospective case control analysis
Background: Changes in the extracellular matrix (ECM) are a recognised feature of aggressive prostate cancer, but they are not exploited in clinical decision-making. We aimed to develop automated quantitative ECM parameters to facilitate risk stratification for localised prostate cancer. Methods: 378 quantitative ECM parameters were derived from picrosirius red-stained diagnostic prostate biopsies in a cohort of 422 patients, matched 1:1 for recurrence, recruited to the CHHiP (Conventional or Hypofractionated High Dose Intensity Modulated Radiotherapy in Prostate Cancer) trial of radiotherapy fractionation for localised prostate cancer. These ECM parameters comprehensively described fibre architecture, gaps and ECM texture. Machine learning models at the level of both individual image tiles and patients defined how ECM parameters related to tumour versus normal prostate, Gleason grade group and recurrence. Shapley analysis was used to interpret ECM feature importance and develop signatures associated with recurrence. Results: Specific ECM patterns identified tumour versus normal prostate, Gleason pattern 4 versus 3 and recurrence. ECM patterns associated with recurrence were enriched in Gleason 4+3 patients, versus Gleason 3+4 patients. Shapley analysis revealed that biopsies from patients with recurrence had smaller more elongated gaps between fibres, with finer grained ECM texture and lower ECM homogeneity than less recurrent regions. Interpretation: Quantitative automated analysis of ECM architecture can inform probability of prostate cancer recurrence after radiotherapy; Features relating to ECM gap size and texture are of particular relevance.
- Personality Traits, Trust, and Acceptance of Artificial Intelligence Assistive Systems: Evidence from Nigeria Population
The increasing deployment of artificial intelligence (AI) assistive systems across healthcare, education, and organisational domains necessitates a deeper understanding of dispositional factors shaping trust and acceptance. This study investigated the Big Five personality traits as predictors of trust in and acceptance of AI assistive systems among a large adult sample (N = 380) in Makurdi Benue State. Anchored in the Technology Acceptance Model (TAM) developed by Davis (1989), the study examined both direct and indirect pathways linking personality traits to AI acceptance through trust. Participants completed standardised measures of the Big Five Inventory, Trust in AI Scale, and AI Acceptance Scale. Data were analysed using structural equation modelling (SEM) with maximum likelihood estimation. The hypothesised model demonstrated good fit indices (CFI = .84, TLI = .82, RMSEA = .05). Openness to experience ({beta} = .34, p < .001) and agreeableness ({beta} = .27, p < .01) significantly predicted trust in AI systems, which in turn strongly predicted AI acceptance ({beta} = .62, p < .001). Neuroticism negatively predicted trust ({beta} = -.29, p < .001), while conscientiousness showed a modest positive direct effect on acceptance ({beta} = .18, p < .05). Extraversion was not a significant direct predictor but exerted an indirect effect through trust. Mediation analysis confirmed that trust significantly mediated the relationship between personality traits and AI acceptance. The findings underscore the centrality of dispositional traits in shaping technological trust formation and highlight the psychological architecture underlying human AI interaction. These results contribute to social psychological theory and provide empirical guidance for designing personality sensitive AI systems to enhance user adoption and sustained engagement.
- Malaria Pre-screening Technology Using Artificial Intelligence (AI)
Malaria remains a severe health problem in endemic regions because people lack adequate diagnostic tools, leading to delayed medical care and elevated death rates. This research introduces a dual-mode artificial intelligence system that uses two complementary models to enhance malaria pre-screening and diagnosis. The patient-centered model uses multivariate logistic regression to analyze biosignals, including heart rate, body temperature, and oxygen saturation, collected through a wearable sensor prototype and a mobile interface for symptom analysis. The system enables patients to begin self-assessment to determine their level of need before scheduling a doctor's appointment. The clinician-centered model represents a customized convolutional neural network that uses annotated microscopy images of red blood cells to achieve 94.84% accuracy, 95.71% precision, 93.87% recall, 94.78% F1 score, and 0.84 Area Under Curve (AUC). The patient model achieved 94.6% accuracy and an AUC of 0.985 using a 70/30 train-test split. These systems work together to create a layered diagnostic system that can operate independently or together to detect malaria at an early stage, especially in areas with limited resources. The findings demonstrate that wearable biosignal data integration with image-based deep learning can produce dependable, scalable, and user-friendly systems for malaria pre-screening. Keywords - malaria diagnosis, artificial intelligence (AI), convolutional neural networks (CNN), wearable biosensors, multivariate logistic regression
- Predicting daily sleep outcomes from continuous HRV in female chronic pelvic pain disorders
Background: Female chronic pelvic pain disorders (CPPDs) are highly prevalent and frequently accompanied by sleep disturbance and autonomic nervous system (ANS) dysregulation. Heart rate variability (HRV), a non-invasive index of ANS function, may provide an objective, physiological correlate of sleep health and can be monitored using wearable devices, enabling a continuous, scalable approach. Objectives: This study examined whether wearable-derived daily HRV metrics are associated with self-reported sleep disturbance in women with CPPD(s) compared with healthy controls, using epoch-level data and generalized additive models. Methods: We conducted a retrospective observational study using up to 90 days of data from a mobile health research app. Participants were 128 women with CPPD(s) and 63 demographically matched healthy controls, who completed a daily PROMIS-based 3-item sleep disturbance questionnaire and wore Fitbit devices that provided 5-minute HRV epochs. Primary predictors were high frequency (HF) and low frequency (LF) power and root mean square of successive differences (RMSSD), with group (CPPD vs control), daily pain severity, and menstrual status as covariates. We fit separate generalized additive mixed models (GAMMs) for each HRV metric with a nonlinear smooth term and an HRV x Group interaction. Results: Higher HF and RMSSD were associated with lower sleep disturbance scores, and these associations were stronger in controls than in the CPPD group (HF x group B {approx} -1.59, p < 0.00010; RMSSD x group B {approx} -0.58, p < 0.0001). LF showed a more complex pattern but also differed by group (B {approx} -0.531, p < 0.0001). HRV smooth terms were highly nonlinear, and models explained ~8-9% of deviance in sleep disturbances. Pain severity and menstrual bleeding were strongly associated with worse sleep. Conclusion: These findings indicate small but consistent associations between wearable-derived HRV metrics and daily sleep disturbances in women with CPPD(s) and healthy controls, with weaker associations in CPPD(s). Integrating continuous HRV with symptom tracking could support low-burden and multimodal monitoring of sleep health in chronic pelvic pain, but prospective validation is needed before HRV can be used for diagnostic or treatment response decision making.
- FoodScribe: an open-source semantic framework for nutrient estimation from free-text dietary records
Efficiently summarizing dietary records at scale remains a persistent bottleneck in nutritional epidemiology. We present FoodScribe, which translates free-text meal descriptions into quantitative nutrient profiles by combining ingredient parsing with nutrient retrieval by querying the USDA FoodData Central (FDC) database. Benchmarked using three LLM providers using Nutribench dataset, FoodScribe completed annotation of 3,807 meal descriptions in 2.5 hours, a task otherwise requiring substantial manual effort from trained nutritionists. FoodScribe achieved accuracy across macronutrient estimation (F1=0.79-0.89), with models performing better for protein than fat estimation. Application to a Mediterranean diet intervention cohort indicated dietary shifts consistent with the intervention pattern based on model-derived estimates. Integration with metabolomics data suggested that fiber and vegetable intake were positively associated with a fecal metabolite cluster.
- Engaging adolescent girls and young women in HIV prevention: A retrospective, observational outcome evaluation of the Eyakho Moghel digital rewards programme in South Africa
Introduction: Adolescent girls and young women (AGYW) in South Africa face disproportionately high HIV incidence, yet uptake and retention in prevention services remain suboptimal. Behavioural economics approaches, including incentive based models, have shown promise in improving health seeking behaviours among this population. This study evaluated the Eyakho Mo'ghel (EM) programme, a membership based digital rewards initiative implemented by Shout It Now within the DREAMS HIV prevention framework, to assess its impact on HIV prevention and sexual and reproductive health (SRH) service engagement among AGYW. Methods: A retrospective, observational outcome evaluation was conducted across the full programme implementation period (December 2021 to February 2025) in five districts in Gauteng and North West provinces, South Africa. Deidentified clinical and app records for 4,684 EM members were analysed alongside a 1:1 matched comparison group of 4,684 non members drawn from approximately one million records using stratified random sampling. Outcomes included HIV testing, pre exposure prophylaxis (PrEP) uptake and persistence, contraceptive use, gender based violence (GBV) disclosure, and key health indicators. Multivariable logistic and Poisson regression models, adjusted for age and district, were used to examine associations between EM membership, app usage patterns, and outcomes. Results: EM members were over three times more likely to have tested for HIV (OR = 3.16, 95% CI: 2.83 to 3.54) and tested significantly more frequently than non-members. PrEP initiation was also markedly higher among EM members (OR = 3.15, 95% CI: 2.85 to 3.48), and persistence beyond the first dispensation was approximately 67% more likely (OR = 1.67, 95% CI: 1.63 to 1.72). Contraceptive uptake was 75% more likely (OR = 1.75, 95% CI: 1.53 to 2.01), and EM members were 54% more likely to disclose GBV experiences (OR = 1.54, 95% CI: 1.24 to 1.91). Sustained app engagement and cumulative point accumulation were consistently associated with improved outcomes. No significant differences in HIV seroconversion, TB screening, or incident pregnancy were observed. Conclusions: A non-monetary, digitally integrated rewards programme was associated with meaningful improvements in HIV prevention service uptake, PrEP persistence, contraceptive use, and GBV disclosure among AGYW. These findings support the integration of incentive-based digital engagement models within combination HIV prevention frameworks, particularly in resource-constrained settings.
- Modeling effect of hypertension control on death, incidence of atrial fibrillation and economic impact to Medicare and hospitals.
Background Hypertension is a major modifiable risk factor for atrial fibrillation (AF), yet blood pressure (BP) control remains suboptimal in older U.S. adults. Objectives This study evaluated how improve systolic BP (SBP) control could affect incident AF, downstream AF ablation demand, Medicare savings, and hospital revenue. Methods A population-based modelling framework was developed to estimate mortality and incident AF hazards across SBP strata: <120, 120-139, 140-159, and ?160 mm/Hg. AF incidence in the SBP <120 mmHg group was set at 2.2 per 1,000 person-year, with hazard ratios of 1.17, 1.42 and 1.64 applied to higher SBP strata. We assumed 25% of incident AF patients would undergo ablation, with a 7.2% complication rate. AF prevalence was projected to increase by 4.6% annually over 10 years. Medicare savings and hospital revenue foregone were estimated under varying procedure cost and contribution-margin assumptions. Results Higher SBP was associated with greater hazards of death and incident AF. Improved SBP control reduced projected AF incidence and ablation demand. Over 10 years, cumulative Medicare savings were projected at $8.7B-$10.9B across the full modelled population. However, reduced ablation volume translated into hospital revenue foregone, ranging from $75M to $377M in the first year, and approximately $1.03B-$5.2B cumulatively over 10 years. Conclusions Improved SBP control may reduce AF incidence, prevent avoidable invasive ablation procedures, relieve pressure on surgical waitlists, and generate substantial Medicare savings. However, these benefits may reduce hospital procedural revenue, highlighting a misalignment between prevention-oriented care and fee-for-service reimbursement incentives.
- Initial Technical and Clinical Validation of Mobile Pupillometry with Virtual Reality: A Digital Biomarker for Screening Cognitive Function and Impairment
Cognitive impairment is a prevalent symptom extending from physiological ageing to disease. It commonly manifests itself in initial memory problems, progressing and co-occurring in more severe conditions such as Mild Cognitive Impairment, Alzheimer's Disease and Major Depressive Disorder. However, current non-invasive screening assessments either lack biological information or are invasive and restricted to specialized centers with complex and cost-intensive set-ups. Here, we conducted an initial validation of mobile pupillometry with Virtual Reality (VR) under experimental conditions as a digital biomarker for cognitive impairment by testing required biomarker-specific properties. For this purpose, we first assessed its construct validity by testing healthy participants (n=43) on an n-back task in VR while pupil size was measured. Mixed effects models revealed that similar to lab-based eye-tracking systems, pupil size increased in a sensible and distinguishable fashion as a function of working memory load. Second, to test the signal's reliability, the same participants were tested on the identical set-up two to three months after their first visit. We observed that the pupil response profile was highly stable over this period. Third, for its clinical validity, we examined patients (n=89) from three different cohorts with varying degrees of cognitive impairment and compared them to healthy control participants (n=81). Mixed-effects models indicated that pupil size was reduced as a function of cognitive impairment levels at higher cognitive load and that this effect was stronger pronounced with increasing age. In conclusion, we provide initial evidence for mobile pupillometry being a sensitive, reliable and clinically valid digital biomarker for cognitive functioning and impairment, which offers desirable properties due to its quick, automatized and location-independent set-up. Keywords: digital biomarker, mobile pupillometry, Virtual Reality, cognition, , Major Depressive Disorder, Mild Cognitive Impairment, Alzheimer's Disease
- Accuracy of a Smart-Ring VO2max Estimate and Five Published Prediction Equations Against Cardiopulmonary Exercise Testing: Development and Validation Study With Population-Scale Analysis
Background. Maximal oxygen uptake (VO2max) is a leading marker of cardiorespiratory fitness and a strong predictor of all-cause mortality. Cardiopulmonary exercise testing (CPET) is the reference method but is resource-intensive, so consumer wearables estimate VO2max from passively collected signals; these estimates compress the fitness range, returning near-correct group averages while ranking individuals poorly. No peer-reviewed validation of a smart-ring VO2max estimate against CPET has been reported, and none in a South Asian cohort. Objective. To validate the Ultrahuman Ring AIR VO2max estimate against laboratory CPET, benchmark it against published prediction equations, and assess its generalization and construct validity. Methods. In a single-site paired ring-CPET cohort (N = 101; mean CPET peak VO2 43.3 mL{middle dot}kg-{superscript 1}{middle dot}min-{superscript 1}, SD 9.9), peak oxygen uptake was measured by treadmill or cycle-ergometer CPET, and the Ultrahuman Ring AIR estimate was computed from passively collected signals using a transparent ensemble based on published equations. Ensemble weights and calibration were selected on an 85-subject development set by an automated search minimizing a composite 5-fold cross-validated error criterion; the locked estimate was evaluated on a 16-subject held-out test set. The calibrated coefficients are proprietary. Agreement was quantified with mean absolute error (MAE), bias, Pearson r, regression slope and Lin's concordance correlation coefficient (CCC; bootstrap 95% CIs), and Bland-Altman limits of agreement. Separately, in 181,133 de-identified Ring users (no CPET reference), construct validity was assessed against ring-measured sleep, continuous glucose monitoring (n = 2,597), and a venous blood panel (n up to 15,203), adjusted for age, sex, and BMI, with lipoprotein(a) as a pre-specified negative control. Reporting followed TRIPOD and STARD. Results. With a self-reported fitness level provided, the estimate agreed with CPET peak VO2 at MAE 4.68 mL{middle dot}kg-{superscript 1}{middle dot}min-{superscript 1} (95% CI 3.93 to 5.49), Pearson r 0.79, CCC 0.79, and slope 0.71. The five published equations were worse on every metric (MAE 6.2 to 10.6, CCC 0.28 to 0.56, slope 0.32 to 0.42), each compressing the fitness range. On the held-out test set (n = 16), agreement held (r 0.84, slope 0.81, MAE essentially unchanged). Without the fitness input, full-cohort MAE was 5.16, still ahead of every published equation. At population scale, higher estimated fitness tracked a healthier profile on measurements the estimate does not use: better ring-measured sleep; higher continuous-glucose time in target range (79.6% versus 61.5%, top versus bottom decile; n = 222 and 399 of 2,597 users); and lower triglycerides, fasting glucose, and HOMA-IR (n up to 15,203 assayed per marker). These associations held after adjustment for age, sex, and BMI, whereas the pre-specified negative control lipoprotein(a) did not separate the deciles. Conclusions. The Ultrahuman Ring AIR VO2max estimate agreed with laboratory CPET substantially better than published prediction equations, held its agreement on held-out subjects, and ordered a large population along independent cardiometabolic gradients consistent with true fitness.