AI News Archive: July 17, 2026 — Part 12
Sourced from 500+ daily AI sources, scored by relevance.
- 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.
- Trance practice and well-being measures: the case of Auto-Induced Cognitive Trance
Introduction Auto-Induced Cognitive Trance (AICT) is a non-ordinary state of consciousness (NOSC) that can be accessed by will alone once a standardised self-induction procedure has been learnt. The first research publication on AICT dates back only ten years, meaning that research on this phenomenon is still in its infancy. Previous reports concerning the phenomenology and neurophysiology of AICT revealed similarities with more extensively described NOSCs, as well as unusual features, raising questions about the potential benefits of AICT practice for well-being. Objective This study aimed to gather quantitative descriptive data on features associated with well-being in a large comparative sample of AICT practitioners and non-practitioners. Method This research followed a web-based survey study design which enquired AICT-trained and yet-to-be trained participants to self-report through validated standardised questionnaires on vitality, self-esteem, mental well-being, trait anxiety, life satisfaction, happiness, positive and negative affect, nature-relatedness and connectedness. Data on NOSCs practices, life history events that could have led to spontaneous NOSCs, and demographic data were collected for further inclusion as control variables in statistical models. Results The online questionnaire yielded 607 valid responses, (171 yet-to-be trained participants and 436 AICT-trained participants). AICT practice was found to be associated with increased self-esteem (RSE), overall connectedness (WCS) as well as all subdimensions of connectedness (WCS Self, WCS Others, WCS World). AICT practice Duration exhibited significant effects on global connectedness and all subdimensions of connectedness, self-esteem, trait anxiety (STAIT-5), and positive affect (PANAS+). Conclusions AICT seems to benefit to practitioners well-being shortly after training through increases in self-esteem and in the sense of connectedness. Prolonged AICT practice is associated with added decreased trait anxiety and increased positive affect. Further research is needed to confirm these findings with a sample including AICT-uninterested participants, and to clarify the underlying mechanisms of AICT.
- The Shape of a Final Message: An Emotional Landscape in the Language of Suicide
The emotional content of suicide notes is typically examined using categorical coding, where each labeled passage is treated in isolation from its surrounding language. In contrast, dimensional models of psychopathology propose that affective content varies along continuous gradients. We evaluated this proposition directly. Excerpts from 884 annotated suicide notes were embedded in a semantic space defined solely by their linguistic properties, and we investigated whether human-assigned emotion labels changed smoothly across this space. They did: affective tone showed clear spatial autocorrelation (Moran's $I = 0.18$, $z = 19.68$, $p < 0.001$), an effect that replicated across three different encoders and remained after removing all within-note dependencies. Emotions occupied recognizable yet overlapping regions rather than forming distinct clusters and varied substantially in how tightly they were concentrated: love and hopelessness appeared with similar frequency, but love was far more localized ($z = 15.7$ versus $10.8$). Among all emotions, hopelessness was the most linguistically diffuse, implying that a single categorical label is capturing multiple, qualitatively different manifestations of suicidal distress.
- Toward precision rehabilitation in adolescent mild traumatic brain injury: leveraging physiologic data from commercially available smartwatches to identify patient subgroups
Autonomic dysfunction is a common sequela of mild traumatic brain injury (mTBI). Physical activity progression is an integral component of mTBI rehabilitation, particularly in addressing autonomic dysfunction. However, clinicians often rely on point-in-time evaluation of orthostatic and exercise intolerance to guide activity recommendations. Commercially available wearable devices (e.g., Fitbits) provide an opportunity to evaluate heart rate response to activity in a real-world setting. Previous work has used physiologic (heart rate) and activity (step count) data to identify subgroups of adults with stroke that may be used to guide activity recommendations. This method may be useful to subgroup youth post-mTBI to identify those who have abnormal physiologic responses to activity. We aimed to identify subgroups using heart rate and step count data in adolescents presenting for specialty care after diagnosed mTBI. Eighty participants aged 13-18 within six months of mTBI diagnosis were recruited to wear a Fitbit Sense 2. Data from seven days and two nights collected within fourteen days of enrollment were included. A group-based steps per minute (SPM) threshold (25th percentile; 10 SPM) and individualized heart rate threshold (20% heart rate reserve (HRR)) were used to classify each minute of active daytime data into one of four quadrants: SPM>10 & HRR>20% (QI), SPM<10 & HRR>20% (QII), SPM<10 & HRR<20% (QIII), and SPM>10 & HRR<20% (QIV). We used percentage of minutes in each quadrant, mean steps per day, percentage of minutes with zero steps, mean SPM in QI, and resting heart rate in a k-means clustering algorithm to identify subgroups. We evaluated subgroup differences by clustering variables using Kruskal-Wallis tests. Sixty-one participants were included. Three subgroups emerged: Sedentary (n=12), Active (n=23), and Atypically Elevated Heart Rate (AEHR; n=26). Subgroups varied significantly on all clustering variables (p<0.01). The Active subgroup took a high number of steps per day, had lower sedentary time, and had the highest activity intensity (mean SPM in QI). The Sedentary subgroup took fewer steps per day compared to the Active subgroup, had high sedentary time, and showed the highest resting heart rate. The AEHR subgroup took fewer steps per day compared to the Active subgroup and had high sedentary time. The AEHR subgroup also spent a higher percentage of time with an atypically high heart rate response to low levels of activity compared to the other subgroups. Our findings suggest that data from wearable devices can identify subgroups of adolescents with mTBI with distinct physiologic/physical activity profiles, which may ultimately be used to inform personalized activity prescriptions. Future work should aim to understand how the identified subgroups relate to longitudinal outcomes.
- How bursty infectiousness shapes epidemic dynamics
An epidemic's expected course is determined by the magnitude and timing of a typical person's infectiousness --- captured, in turn, by the basic reproduction number and the generation-time distribution. These fundamental, population-average quantities can mask individual-level variation that shapes how an epidemic actually unfolds: for example, individual variation in the magnitude of infectiousness (overdispersion) creates superspreading, a key feature of the SARS-CoV-1 and SARS-CoV-2 epidemics. However, the impact of individual variation in infectiousness timing is less well understood. Here, we demonstrate that individual infectiousness timing varies substantially and to different degrees across pathogens. For some common pathogens, including influenza, measles, and SARS-CoV-2, infectiousness is "bursty", or highly concentrated and variably-timed across individuals: for example, the window of appreciable infectiousness for SARS-CoV-2 may last for roughly a day, vs. the 9--12 days usually quoted. We show that bursty infectiousness creates superspreading without inherent superspreaders, makes epidemic timing more variable, amplifies the time-sensitivity of common interventions, and complicates inference of key epidemiological parameters. Together with the reproduction number, the generation-time distribution, and overdispersion, burstiness completes a family of basic parameters that govern how epidemics unfold.
- Mathematical Modeling of Rift Valley Fever in the Sahelian Zone
We develop a mathematical model of Rift Valley Fever integrating mosquito vectors, ruminants, and humans, based on an SEIR-type structure with vertical transmission in vectors. Local data from the Sudanian and especially the Sahelian zones are used to capture the impact of climatic variations on mosquito population dynamics. The mathematical analysis establishes the models positivity, determines the basic reproduction number R0, and demonstrates the local and global stability of the disease-free equilibrium. Sensitivity analysis (PRCC) highlights the most influential parameters, while the stochastic approach using a continuous-time Markov chain confirms the major role of seasonal rainfall. Numerical simulations reveal a peak in animal and human infections around the 9th month, correlating with periods of heavy rainfall. This model provides a relevant tool for surveillance and prevention within a "One Health" approach in Chad.
- CuGen: A GPU-accelerated framework for large-scale genomics
Biobank-scale genomic analyses remain computationally expensive, CPU-bound workflows, particularly when adjusting for confounding. Here, we present CuGen, a GPU-accelerated framework for large-scale genomics. CuGen uses UltraLasso, a novel hierarchical application of univariate-guided sparse regression (uniLasso), to select a compact, phenotype-informed active set of fewer than 30,000 variants. This achieves robust leave-one-chromosome-out (LOCO) confounding control, enabling both downstream GWAS and in-sample fine-mapping. Additionally, we introduce the .cugen file format, a genotype representation designed for memory-optimized, high-throughput streaming and random access on GPU hardware. Building on this substrate, we provide a general GPU-accelerated genomics toolkit handling polygenic prediction, data manipulation, quality control, analysis, and visualization. We demonstrate CuGen's efficacy in the UK Biobank with up to 408,624 individuals, where the full GWAS pipeline and fine-mapping against 6.8 million imputed variants completes in approximately 10 minutes on a single high-throughput GPU with 80 GB of memory. The pipeline scales efficiently to massive phenome-wide analyses with sublinear resource consumption.
- Genetic sensitivity analysis: estimating genetic confounding and environmentally mediated genetic effects using multiple exposures
Polygenic scores are imperfect measures of the additive genetic effects of common genetic variants. The resulting measurement error biases estimates of quantities of interest in epidemiological analyses integrating polygenic scores. For example, how much of an exposure-outcome association is genetically confounded can be substantially underestimated when using polygenic scores alone. Here we present extensions to Gsens, a genetic sensitivity analysis, which aims to correct for such measurement error using both polygenic scores and heritability estimates. Gsens now allows for multiple exposures and estimates several quantities of interest, i.e. genetic confounding, adjusted residual association (net of genetic confounding), genetic overlap and environmentally mediated genetic effects. We present derivations and simulations showing how Gsens accounts for measurement error in the polygenic score; we also show how estimation may be affected by misspecifications of the causal structure between exposures. Applying Gsens in the Norwegian Mother, Father and Child Cohort Study (MoBa), we uncover, among other results, substantial genetic confounding in the associations between multiple known risk factors for attention deficit hyperactivity disorder (ADHD), such as low birth weight and temperament, and measures of ADHD in childhood. The updated Gsens R package offers multiple options, including for missing data handling and customisable syntax. Our extended version of Gsens is applicable to a broad range of substantive questions in multiple disciplines.
- Pediatric Poverty and County-Level Cardiovascular Mortality in the United States: A National Cross-Sectional Analysis
Background: Cardiovascular disease remains the leading cause of death in the United States, and marked geographic disparities in cardiovascular mortality persist. However, the community-level socioeconomic indicators most strongly associated with these disparities remain unclear. Community-level measures capture the social and economic conditions that influence cardiovascular health across populations and may help identify communities at greatest risk. We used the Area Health Resources File (AHRF) to identify socioeconomic measures most strongly associated with county-level cardiovascular mortality. Methods: We performed a national cross-sectional ecological analysis using the 2024-2025 Area Health Resources File (AHRF), including counties in the 50 U.S. states and the District of Columbia. The primary outcome was an AHRF-defined cardiovascular mortality composite derived from 2021-2023 National Center for Health Statistics (NCHS) mortality data. Community-level socioeconomic measures included 2023 overall, pediatric, and family childhood poverty and 2019-2023 overall, female, and White unemployment. County-level associations were evaluated using Spearman rank correlation and regional differences using the Kruskal-Wallis test. Sensitivity analyses used a partial mortality composite and Kendall {tau} correlation. Results: Among 1,982 counties, cardiovascular mortality varied significantly across U.S. Census divisions (P<0.001), with the highest population-weighted rate in the East South Central division (348.5 deaths/100,000) and the lowest in the Mountain division (233.9 deaths/100,000). Pediatric poverty demonstrated the strongest association with cardiovascular mortality ({rho}=0.612), followed by family childhood poverty ({rho}=0.603) and overall poverty ({rho}=0.524, all P<0.001). In contrast, unemployment measures were more weakly associated (overall {rho}=0.209, White {rho}=0.176, female {rho}=0.141, all P<0.001). Results were consistent in sensitivity analyses. Conclusions: County-level poverty, particularly pediatric poverty, was more strongly associated with cardiovascular mortality than unemployment across U.S. counties. These findings suggest pediatric poverty may serve as a useful community-level indicator for identifying populations at increased cardiovascular risk and prioritizing future public health interventions.
- Neighborhood and Environmental Factors and Post-Discharge Healthcare Utilization in HFpEF: A Retrospective Cohort Study
Background: Neighborhood-level social determinants of health influence cardiovascular outcomes; however, their association with post-discharge healthcare utilization in heart failure with preserved ejection fraction (HFpEF) remains incompletely defined. Methods: We conducted a retrospective cohort study of 6,702 adults hospitalized for HFpEF (2014 to 2022). Patients were assigned to one of four neighborhood environments (NEnv-1 to NEnv-4) using a validated clustering framework based on ZIP code-level socioeconomic variables. The primary outcome was time to first HF readmission, evaluated within prespecified post-discharge intervals (0-30 days, >30-90 days, and >90-365 days). Secondary outcomes included HF-related healthcare re-encounters and HF hospitalization burden (0, 1, or [≥]2 admissions). Cox proportional hazards and multinomial logistic regression models were used. Results: Neighborhood environment was independently associated with post-discharge outcomes with distinct temporal patterns. Early (0-30 days) HF readmission risk was higher in NEnv-3 (aHR, 1.63) and NEnv-4 (aHR, 1.76), with similar increases in HF-related re-encounters (aHR, 1.72 and 1.84) persisting through the >30-90-day interval. In contrast, NEnv-2 demonstrated a delayed-risk pattern, with the highest risk occurring in the >90-365-day interval (readmission aHR, 3.42; re-encounter aHR, 3.45). All non-reference environments were associated with a higher likelihood of at least one post-index HF admission (aOR range, 1.84-2.24). NEnv-4 uniquely demonstrated higher odds of recurrent hospitalization ([≥]2 vs. 1 admission; aOR, 1.64). Conclusions: Neighborhood environment is associated with distinct, time-dependent patterns of HF utilization in HFpEF, including early, delayed, and recurrent risks. Incorporating neighborhood context may help identify when patients with HFpEF are most vulnerable after discharge and guide the timing of post-discharge interventions.
- Vaccination recommendations to others among physicians and the general public: effects of birth-year-based vaccination policy changes assessed by regression discontinuity analysis
Introduction: Recommendations from physicians and peers play a crucial role in promoting vaccination. This study evaluated differences in recommendations to others regarding four vaccines with varying efficacy (seasonal influenza, measles, human papillomavirus [HPV], and coronavirus disease 2019 [COVID-19]) between physicians and the general public and examined the impact of birth-year-based vaccination policy changes on these recommendations. Methods: This cross-sectional study was conducted in February 2026 among 492 physicians and 5,252 members of the general public in Japan. Consistency in recommendations across the four vaccines was assessed using the intraclass correlation coefficient (ICC[3,1]), and group differences were examined using a two-way mixed-design analysis of covariance. Multilevel regression discontinuity analyses were performed to evaluate the effects of birth-year-based vaccination policy. Results: Physicians showed significantly stronger recommendations to others than the general public, and their recommendation patterns generally reflected vaccine efficacy. However, physicians showed lower consistency across vaccine types than the general public (ICC[3,1]), driven primarily by heterogeneity in COVID-19 vaccine recommendations. Regression discontinuity analyses showed that birth-year-based vaccination policy, including routine vaccination opportunities, was significantly associated with recommendations to others for measles and HPV vaccines, independently of perceived benefits and risks. Conclusion: To improve vaccination coverage from a public health perspective, it is important for physicians to provide effective vaccination recommendations to the general public on a broader scale; however, it is also necessary to address the heterogeneity in vaccine-specific recommendation patterns among physicians, as observed for COVID-19. Routine vaccination opportunities may increase vaccination coverage not only through the routine vaccination program itself but also through peer effects among the general public. Vaccination policy may therefore influence vaccination coverage not only in the current generation but also in future generations. Designing vaccination policy should consider its long-term impact on future vaccination coverage as well as herd immunity.
- A study of PROGRESS: the Therapeutic Potential of 17 OHPC on the Pathophysiology of Severe Preeclampsia
Preeclampsia (PE), new onset hypertension after 20 weeks of gestation, affects 10% of all pregnancies in the U.S. and it is associated with progesterone deficiency, chronic inflammation, elevated angiotensin II type 1 receptor agonistic autoantibody (AT1-AA) and endothelial dysfunction. Progesterone, through its receptors, stimulates an anti- inflammatory protein called Progesterone Induced Blocking Factor (PIBF) which decreases during various pregnancy disorders. Therefore, this study was designed to test the hypothesis that a progestogen, in the form of 17-hydroxyprogesterone caproate, stimulates PIBF, lowers vasoactive mechanisms which reduces maternal blood pressure in women with early-onset preeclampsia (EOPE). PE women received 17-OHPC (250 mg, I.M.) and blood draws were collected before and after 17-OHPC supplementation. Placentas were collected at the delivery. 17-OHPC prolonged time of delivery beyond 72h on average and maternal blood pressure was significantly decreased in PE+17- OHPC. Progesterone and PIBF levels were reduced in PE group vs. NP group. Importantly, 17-OHPC increased PIBF and decreased vasoactive mechanisms and markers of inflammation. In conclusion, 17-OHPC or progesterone supplementation improves maternal outcomes in response to EOPE without causing further harm to the fetus.
- The Registry of Pregnant Women at Cruces University Hospital: an ethical framework for prospective research with preanalytical optimization of maternal plasma processing
Background: Prospective pregnancy registries and biobanking infrastructures are essential for future translational studies investigating maternal, placental and offspring health. However, circulating nucleic acid analyses are highly sensitive to preanalytical variability, particularly regarding blood-collection tube type and sample processing conditions. We established a prospective pregnancy registry and biobanking workflow at Cruces University Hospital and evaluated the impact of preanalytical variables on circulating cell-free DNA (cfDNA) and cell-free RNA (cfRNA) preservation in maternal plasma collected at delivery. Methods: The Registry of Pregnant Women at Cruces University Hospital was designed as a prospective infrastructure integrating placental sampling, maternal blood collection and ethically controlled future access to maternal and offspring clinical data. Within this framework, peripheral blood samples from 50 women at delivery were simultaneously collected into EDTA, Norgen and Roche tubes. Plasma samples processed within or after 24 hours following collection underwent cfDNA/cfRNA extraction, electrophoretic profiling, fluorometric quantification and RT-qPCR analyses targeting different stress-related genes. Results: By the end of June 2026, 1,127 women had been prospectively recruited into the registry, with 661 plasma samples, 637 serum samples and 858 sets of four placental biopsies collected, processed and stored in the Basque Biobank. In the preanalytical substudy, EDTA tubes yielded higher cfDNA concentrations, likely reflecting reduced cellular preservation and genomic DNA contamination. In contrast, Roche tubes showed superior cfRNA preservation, with higher cfRNA concentrations and more consistent detection of the characteristic 5S rRNA peak compared with EDTA and Norgen tubes. Processing delays beyond 24 hours reduced cfRNA concentration, while associations between circulating transcripts and gestational age were more consistently detectable in preservative-containing tubes. Conclusions: Prospective infrastructures like ours offer strong foundation for large scale, long-term studies in the framework of the Developmental Origins of Health and Disease hypothesis. Technically, Roche tubes provided superior cfRNA preservation and enhanced sensitivity for detecting subtle biological associations, supporting the importance of standardized preanalytical workflows within prospective pregnancy biobanking resource.
- Prescribing Trends of Antimicrobials in Obstetric and Gynaecological Inpatients: A Prospective Drug Utilization Study with Concurrent Antimicrobial Stewardship Audit from a Tertiary Care Hospital in Karachi, Pakistan
Background: Antimicrobial resistance (AMR) disproportionately affects low- and middle-income countries (LMICs) such as Pakistan, where obstetric and gynaecological (OBGYN) patients carry high antibiotic exposure. Specialty-specific drug utilization data with concurrent stewardship audit remain scarce. This study evaluated antibiotic prescribing patterns, consumption metrics, and antimicrobial stewardship program (AMS) compliance in OBGYN inpatients at a public sector tertiary care hospital. Methods: A prospective cross-sectional study was conducted in OBGYN wards of Dow University Hospital, Karachi, from 1 September to 31 October 2025. Women receiving [≥]1 systemic antibiotic were included. Daily AMS rounds were conducted by an Infectious Diseases physician and pharmacist. Antibiotic consumption was measured as Defined Daily Doses (DDD) and Days of Therapy (DOT) per 1,000 patient-days (total = 821). Antibiotics were classified by WHO AWaRe (2023) framework. Results: Of 812 total admissions, 278 patients (34.2%) received [≥]1 antibiotic and were enrolled (205 obstetric, 73 gynaecological), generating 636 prescriptions (mean 2.29/patient). Surgical prophylaxis was the predominant documented indication (213, 33.5%); 65.1% carried no documented indication. By AWaRe classification, 53.6% were Access-group and 46.1% Watch-group. Ceftriaxone (38.4%) and metronidazole (36.8%) together represented 75.2% of prescriptions. Combined DDD/1,000 patient-days was 1,758.6 and DOT/1,000 patient-days was 1,852.7. AMS compliance was 0%. Conclusions: This study documents high antibiotic prescribing burden, near-universal documentation failure, and zero AMS compliance in OBGYN inpatients at a Pakistani public sector hospital. The predominance of Watch-group antibiotics and undocumented surgical prophylaxis highlights structural stewardship gaps. Findings support urgent need for institutional OBGYN antibiotic guidelines and structured pharmacist-led AMS programs.
- Psychosocial and socioeconomic vulnerability among caregivers of children with retinoblastoma: a cross-sectional latent profile study
Background: Caregivers of children with retinoblastoma (RB) face substantial psychological and socioeconomic challenges. However, the factors independently associated with caregiver burden and the distribution of risk across caregiver subgroups remain incompletely characterized. We examined psychosocial and socioeconomic correlates of caregiver burden, identified distinct vulnerability profiles, and evaluated factors associated with high-risk profile membership. Methods: This cross-sectional study enrolled 413 primary caregivers of children with RB at a tertiary ophthalmic oncology center. Participants completed validated measures of caregiver burden (ZBI-22), anxiety (GAD-7), perceived social support (PSSS), family functioning (FAD-GF), and mental and physical quality of life (SF-12 MCS and PCS). Multivariable linear regression identified factors independently associated with caregiver burden and mental quality of life. Mediation analysis evaluated the indirect association between social support and burden through family functioning, and moderation analysis assessed whether household income modified the association between family dysfunction and burden. Latent profile analysis (LPA) identified caregiver risk profiles, and multinomial logistic regression examined factors associated with profile membership. Results: Anxiety showed the strongest independent association with greater caregiver burden (standardized coefficient beta = 0.641, 95% CI [1.46, 1.84], P < 0.001) and poorer mental quality of life (beta = -0.483, 95% CI [-0.12, -0.08], P < 0.001). Family debt was independently associated with greater burden (beta = 0.195, P = 0.040). Family functioning accounted for 32.19% of the total association between social support and burden. Household income modified the association between family dysfunction and burden (interaction B = -0.85, P < 0.001), with a steeper gradient in lower-income households. LPA identified three profiles: severe burden-high vulnerability (n = 82, 19.85%), moderate burden (n = 193, 46.73%), and mild burden-high resilience (n = 138, 33.41%). Low-to-moderate household income was associated with higher odds of severe-profile membership (OR = 31.50, 95% CI [6.56, 151.24], P < 0.001). Conclusions: Caregiver burden in pediatric RB was associated more strongly with psychosocial and socioeconomic factors than with the clinical indicators examined. Family functioning partly accounted for the association between social support and burden, while household income modified the association between family dysfunction and burden. These findings support prospective evaluation of family-centered and financial-support interventions and suggest that profile-based screening may help identify caregivers requiring more intensive support.
- I Use ChatGPT-5.6 Sol For All My Work
Testing Sol across files, research, builds, and planning
- First-Line Opioids and Short-Term All-Cause Emergency Department Return After Headache Visits: A Two-Center Comparative Cohort Study
Objective: To compare first-line emergency department (ED) treatment classes for acute headache on short-term all-cause ED return and index admission across two independent health systems. Background: ED trials of acute headache treatment are judged on in-ED pain relief, a documented endpoint that is recorded incompletely and shifts with the scoring rule, and is a weak surrogate for what happens after discharge. All-cause ED return after an index headache visit (any subsequent ED encounter within the window) has not been used to compare first-line treatments at scale, and society guidance favors dopamine-receptor antagonists while recommending against routine opioids. Methods: Retrospective two-center cohort of adults treated for headache in the ED, using MIMIC-IV-ED (Beth Israel Deaconess Medical Center, 2011-2019) and MC-MED (Stanford, 2020-2022). The first-line class was the earliest qualifying acute agent. The primary contrast was opioids versus dopamine-receptor antagonists (the guideline-preferred class). Outcomes were 72-hour and 7-day all-cause ED return (among discharged patients; any subsequent ED encounter within the window) and index hospital admission. Confounding by indication was addressed with propensity overlap weighting; associations are reported as adjusted risk ratios (RRs) with bootstrap 95% CIs and E-values. Estimates were pooled with a site term and examined per site. Results: Among 13,285 treated adults (10,799 MIMIC-IV-ED; 2,486 MC-MED), opioid recipients were older and higher-acuity than dopamine-antagonist recipients (index admission 38.1% vs 16.4%). In the MIMIC-IV-ED discharged primary-contrast population, overlap weighting reduced the maximum standardized mean difference from 0.35 to 0.002; pooled and site-specific balance diagnostics are provided in the Supplement. First-line opioids remained associated with a higher 72-hour all-cause ED return (6.8% vs 3.8%; adjusted RR 1.79; 95% CI 1.31 to 2.33), 7-day return (10.7% vs 6.6%; RR 1.62; 95% CI 1.28 to 1.98), and index admission (RR 2.32; 95% CI 2.11 to 2.58, consistent with strong residual severity differences in patients selected for opioids). The direction of association was concordant across both health systems, although MC-MED return estimates were imprecise given the smaller opioid-treated discharged sample. In MIMIC-IV-ED, the cumulative all-cause return incidence by treatment class separated by day 3 and persisted through 30 days. The direction was consistent, though attenuated and no longer statistically significant, when the outcome was restricted to a headache-specific return (72-hour RR 1.31; 95% CI 0.91 to 1.88); the direction persisted for the composite of admission or 72-hour return, which does not condition on discharge but is influenced by the more confounded admission component (RR 2.16; 95% CI 1.98 to 2.39). Conclusion: Across two health systems, first-line opioid treatment for ED headache was associated with higher all-cause short-term ED return among discharged patients and higher index admission than dopamine antagonists. These observational associations reflect downstream all-cause ED utilization after an index headache visit rather than confirmed headache recurrence or treatment failure; they are consistent with guideline-concordant, opioid-sparing first-line treatment and warrant prospective confirmation. Plain Language Summary: Emergency departments treat headaches with several different medicines, but the usual way of judging which works, the pain score recorded during the visit, is often missing or inconsistent. Using two large hospital systems and a clearer outcome, whether patients came back to the emergency department for any reason, we found that patients first treated with opioids returned within 72 hours about 1.8 times as often as those given the guideline-preferred dopamine-blocking medicines and were admitted more than twice as often. These patterns pointed the same direction in both hospital systems after adjustment for the measured differences available in both databases. Because this was an observational comparison and returns were counted for any reason, the findings are consistent with using guideline-preferred non-opioid medicines first, rather than proof that opioids worsen headache.
- DeepSeek valued at $51.9b after funding
A person close to DeepSeek said the company had completed that financing round and had started a second one.
- Phrase
Your AI notes can edit themselves
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Discover a company’s closest competitors
- How Apple’s big lawsuit could disrupt OpenAI’s IPO plans
Apple filed a trade secrets lawsuit against OpenAI last Friday, and it’s not messing around. The complaint alleges a pattern of misconduct reaching all the way up to OpenAI’s chief hardware officer and claims more than 400 former Apple employees now work at the company. OpenAI’s response so far has been carefully hedged, and the timing couldn’t be worse with the company reportedly eyeing an IPO […]
- Apple Sues OpenAI: The Injunction Threatening AI Hardware
By alleging that Tang Tan’s engineering division is structurally compromised, Apple is laying the legal groundwork to halt physical… Continue reading on Towards AI »
- Apple widens OpenAI trade secrets fight with preservation orders
Dozens of former Apple employees now working at OpenAI have been put on notice after Apple reportedly sent legal letters ordering them to preserve documents and communications relevant to its trade secrets lawsuit against OpenAI. The Financial Times reports that “around 40” employees have been targeted with these letters, which repeat Apple’s claim that its confidential information might have been exfiltrated, alleging “trade secret misappropriation and breach of contract.” The letters also require them to arrange to meet with Apple’s lawyers. The underlying lawsuit This comes on the heels of Apple’s explosive lawsuit against OpenAI in which Apple accused the AI company (and former Apple Vice President Tang Tan) of extensive coordinated data theft. Tan was at Apple for 24 years and is now Chief Hardware Officer at OpenAI. Apple’s lawsuit is defined by claims OpenAI took a range of steps to pry confidential Apple data from existing Apple employees, including using information such as internal project code names, to gain even more knowledge during interviews. The company says the evidence it has presented so far is only the “tip of the iceberg” concerning OpenAI’s approach. The lawsuit requests that OpenAI be prevented from using any Apple information during the development of its hardware. Apple is also seeking damages and suing two former employees for breach of contract for violating their employment agreements. The letters are significant. They represent formal directives that require former Apple staff to preserve documents, messages, emails, and other communications that could be relevant to the case. The demand reflects Apple’s belief that the alleged misuse of confidential information could be more widespread across the competing company. What’s critical is that orders of this kind override any standard data destruction policy and deletion of the requested information becomes a legal offense. Why this matters beyond the protagonists In making its move, Apple shows this is not a dispute about just one or two hires, but an attempt to constrain the movement of intellectual property between the two firms. With AI hardware emerging as the next major battleground in tech, the case could become a defining one; whatever resolution is eventually reached could define the extent to which former employees can carry experience and knowledge between competing firms. The case might also define what the line is between experience and knowledge and the sharing of trade secrets. This is important, because modern hardware development relies on far more than just finished designs . Product design leans into supplier relationships, manufacturing assumptions, physics, extensive prototyping, and product-roadmap priorities. If courts treat those accumulated insights as protectable secrets, hiring between major technology companies could become far more legally sensitive. The Jony Ive question The case comes as Apple prepares to combat OpenAI in hardware . Its competitor is now working with legendary former Apple designer Jony Ive . Ive is not named in the litigation, but Apple will be keen to find out whether confidential product knowledge, design processes, or supply-chain insights have travelled with former staff into OpenAI’s device work. Ultimately, for Apple, it’s about protecting its many blueprints for whatever hardware the company expects will come after the iPhone. For its part, OpenAI has refuted Apple’s lawsuit, arguing that it is “not aware of any evidence” that the lawsuit has merit. “We have no interest in other companies’ trade secrets,” said OpenAI spokesperson Drew Pusateri . “We remain focused on building innovative technology that empowers people everywhere.” The company’s lawyers also claim it did respond to Apple’s initial inquiries on the matter. What’s at stake The significance of Apple’s newly-shared communication preservation orders is that if discovery uncovers evidence supporting Apple’s claims, the case could complicate OpenAI’s hardware plans and create unwelcome scrutiny ahead of any future public offering. You can follow me on social media! Join me on BlueSky , LinkedIn , Mastodon and subscribe to The Core .
- Apple targets OpenAI workers over trade secrets theft lawsuit
The iPhone maker has accused OpenAI of stealing hardware secrets via its former employees. Read more: Apple targets OpenAI workers over trade secrets theft lawsuit
- What’s next in Apple’s legal battle with OpenAI
Apple's lawsuit against OpenAI is just getting started. Here's what could happen next and what it means for OpenAI's hardware ambitions.
- Apple sends legal letters to dozens of OpenAI defectors, report says
Apple appears to believe that additional former employees have shared company information with OpenAI.
- The Apple vs. OpenAI legal showdown
The Apple vs. OpenAI legal showdown marketplace.org
- Apple vs. OpenAI: These Lawsuit Details Are Wild video
Apple sued OpenAI, accusing the company of stealing its trade secrets. But what now? CNET's Bridget Carey breaks down what to expect next -- and how it might impact OpenAI's hardware plans.
- Apple vs. Open AI Explained: The Battle for AI Gadgets Begins With a Juicy Lawsuit
Ah, so that's why we didn't hear about ChatGPT at WWDC this year.
- Apple’s plot to crush OpenAI
Apple is suing OpenAI. The complaint is readable and intense, as these things often are, though many experts seem to think many of the allegations are just the ways things are done. So what does Apple really want here, and why is it picking such a public fight with OpenAI? On this episode of The […]
- Zoox issues software recall after a robotaxi got confused by heavy smoke
The recall comes as the top automotive safety regulator in the U.S. has warned AV companies about their vehicles interfering with first responders.
- Zoox is recalling its entire robotaxi fleet after one drove into a smoke-filled fire scene
The Amazon-owned company issued a software update after a vehicle entered an active emergency scene obscured by heavy smoke in Las Vegas on June 20
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Splits your grocery cart across stores, automatically
- Zoox recalls self-driving cars because they may not detect smoke
Last week, the top U.S. auto safety official said self-driving car companies must quickly address a "clear pattern" of driverless vehicles interfering with law enforcement and other first responders.
- Amazon’s Zoox recalls 105 robotaxis after one drove into heavy smoke at an active fire scene
Amazon’s Zoox voluntarily recalled software in 105 of its robotaxis after an unoccupied vehicle drove into heavy smoke at an active emergency fire scene in Las Vegas on June 20. The smoke obscured the scene, which had not been cordoned off with cones. The vehicle entered, braked hard while attempting to steer away, and came […] This story continues at The Next Web
- Zoox Issues Recall After Heavy Smoke Caused a Robotaxi to Enter an Active Emergency Scene
If robotaxis are the future, they better prepare for a lot more smoke.
- Zoox issues software recall because its robotaxis may be confused by smoke
The NHTSA recently called for autonomous vehicle companies to improve how cars respond in emergency situations.