Browse Articles

Five highlights from lung-cancer research

Triglyceride-glucose index as a marker of metabolic and inflammatory risk across different thyroid function states

A direct black-hole mass measurement in a little red dot at high redshift

Abstract Recent discoveries of faint active galactic nuclei (AGN) at the redshift frontier have revealed a plethora of broad Hα emitters with optically red continua, named little red dots (LRDs) 1 , which comprise 15–30% of the high-redshift broad-line AGN population 2 . Owing to their peculiar properties 3–6 , modelling LRDs with standard AGN scenarios has proven challenging. In particular, the validity of single-epoch virial mass estimates in determining the black-hole masses of LRDs has been called into question, with some models claiming that masses might be overestimated by up to two orders of magnitude 7–10 . Here we report a direct, dynamical black-hole mass measurement in a strongly lensed LRD at a redshift of 7.04. The combination of lensing with deep spectroscopic data reveals a rotation curve that is inconsistent with a nuclear star cluster, yet can be well explained by Keplerian rotation around a point mass of 50 million solar masses, consistent with virial black-hole mass estimates. The Keplerian rotation leaves little room for any stellar component in a host galaxy, as we conservatively infer M BH / M ⁎  > 2 (where M BH is the black-hole mass and M ⁎ is the stellar mass). Such a ‘naked’ black hole, together with its near-pristine environment 11 , indicates that this LRD is a massive black-hole seed caught in its earliest accretion phase.

Spontaneous eye blink rate indicates increased attention during grooming in female Barbary macaques

Abstract Spontaneous eye blinking is a ubiquitous behavior in animals including humans necessary for lubricating the ocular surface and preventing dryness. Beyond this functional role, eye blinking also provides a window into an animal’s cognitive state and attention allocation. Here in a purely observational study, we investigated in 13 female Barbary macaques ( Macaca sylvanus ) the modulation of spontaneous eye blinking during two naturally occurring activities differing in their attentional demand (resting and allo-grooming) and additionally assessed the influence of social relevance of the interaction on attention allocation. Eye blink rates were significantly lower during grooming compared to resting, suggesting increased attention during this cognitively more demanding task. Dominance rank difference and affiliative relationship strength between the groomer and groomee did not additionally influence eye blink rate. Blinking was timed to coincide with ingestion events during grooming, which may serve as explicit attentional breakpoints. By systematically timing blinks with periods of decreased visual demand, macaques effectively minimized information loss during the non-visual phase of the grooming process. Our study provides insights into the regulation of spontaneous eye blinking in nonhuman animals using a non-invasive tool for the study of visual attention and cognitive load.

Growth regulator-mediated modulation of antioxidant and secondary metabolism in coriander under exposure to high temperature

How the connection between lung cancer and the brain could lead to better treatments

Effect of different enamel preconditioning protocols on the adhesion of ceramic brackets

Magnetically recoverable Fe3O4-chitosan/ZIF-8 beads for tetracycline removal from aqueous solution

Psychological associations of sports technology use with technostress, self-efficacy, cognitive weariness, and athlete burnout

Quality and content evaluation of male HPV infection on Bilibili and TikTok: a cross-sectional study

Abstract Social media platforms, particularly short-video applications, have emerged as crucial channels for disseminating public health information. Human papillomavirus (HPV) infection in males represents a significant sexually transmitted disease burden with under-addressed health impacts. Despite its prevalence, the quality of HPV-related content targeting male audiences on these platforms remains unevaluated, posing potential risks to public health literacy. We conducted a cross-sectional analysis of 265 TikTok and Bilibili videos addressing male HPV infection. Video reliability and educational quality were assessed using the modified DISCERN (mDISCERN) instrument and Global Quality Scale (GQS). The study used statistical analysis to examine the content and quality of videos on the two platforms, the identities of the uploaders and the correlations between these factors and user engagement data. Overall video quality was low (median GQS = 2.00; mDISCERN = 2.00). TikTok outperformed Bilibili in both engagement metrics and quality scores (GQS: TikTok median = 2.00 [IQR 2.00–3.00] vs. Bilibili = 2.00 [2.00–2.00], p  < 0.01; mDISCERN: 2.00 [2.00–3.00] vs. 2.00 [2.00–2.00]). Content gaps were prominent: only 15.8% ( n  = 42) covered prevention strategies (e.g., vaccination), while symptom descriptions dominated (29.8%). Specialist-generated videos scored significantly higher in quality (GQS = 3.00 [2.00–3.00]; mDISCERN = 2.00 [2.00–3.00]) than non-specialist content (GQS = 2.00 [2.00–2.00]; mDISCERN = 2.00 [2.00–2.00]). Engagement metrics showed no correlation with quality scores. Short-video platforms exhibit suboptimal quality in disseminating male HPV information, with TikTok marginally superior to Bilibili. Specialist involvement enhances content reliability, underscoring the importance of leveraging professional health communication on social media. Public health initiatives must prioritise engaging experts to amplify accurate prevention messaging, particularly regarding vaccination, and address current informational inequities, thereby improving community health outcomes.

Iran’s Internet blackout: a scholar’s month in the dark

Chronic developmental exposure to traffic-derived PM2.5 has limited skeletal effects in mice

Abstract Epidemiological studies link traffic proximity to increased osteoporosis and fracture risk in older adults, but mechanistic relationships remain unclear. Following our recently published hypothesis regarding metal-induced bone fragility, we performed an exploratory study to investigate whether chronic exposures to metals in traffic-derived PM 2.5 during development compromise bone health in young adult mice. This opportunistic investigation utilised stored biological material from a concurrent sub-chronic exposure study. BALBc mice were divided into PM 2.5 exposed and control groups over 12 weeks. Starting at 6 weeks of age PM 2.5 exposed mice received daily intranasal administration of 10 µg/mL filtered particulates collected from Sydney roadside air while controls received saline. Metal accumulation was quantified using ICP-MS and bone structural properties were assessed via micro-CT. Mechanical properties were evaluated through three-point bending tests at 4 and 8 and 12 weeks of exposure. No significant differences were observed between groups across any parameter. Both groups exhibited comparable concentrations of endogenous and exogenous metals at all examined time points. Bone geometric properties and mechanical characteristics including yield stress and ultimate tensile strength and stiffness were similar between groups with normal age-related development in both. These findings suggest that metal accumulation in bone is not significant in early life and is not associated with a deterioration of mechanical properties under the specific conditions of this exploratory model. It is more likely that any adverse effects require a cumulative lifetime burden and age-related decline in protective mechanisms rather than immediate developmental vulnerability. This shifts the focus from early-life susceptibility to long-term risk and establishes a methodological pipeline for future longitudinal research.

A cautious voice on the closure of China’s journal ranking list

Ectopic NMDAR expression in cancer unmasks germline-encoded autoimmunity

Retinal lesion annotation on fundus imaging: an interobserver variability study

Abstract Accurate detection and segmentation of retinal lesions in fundus photographs is essential for diagnosing diabetic retinopathy (DR) and for developing reliable machine learning models for this purpose. However, the reliability of manual annotation of retinal lesions by clinical experts has yet to be evaluated. We quantified interobserver variability in the pixel-wise annotation of microaneurysms (µA), intraretinal hemorrhages (Hem), hard exudates (Ex), and cotton-wool spots (CWS) using 51 images from the MAPLES-DR dataset, independently annotated by three senior retinologists. Manual segmentation showed substantial variability: 38% of lesions marked by one observer were undetected by the others, lesion coordinates differed by approximately one third of the lesion diameter, and the mean interobserver IoU was 0.47, indicating inconsistent contour delineation even when observers detected the same lesion. Paradoxically, inter-expert DR grading assessments were largely concordant, suggesting reliance on more holistic or experience-based judgment criteria rather than on lesion counting. We assessed a semi-automated workflow in which annotators corrected model-generated pre-segmentations. This approach markedly improved agreement, yielding a pixel-wise mean IoU of 0.71, reducing both detection and contour-related variability; over 90% of lesions were preserved directly from the pre-segmentation.

Too dangerous to release: is Mythos the start of the restricted-AI era?

A weld point cloud recognition method based on an improved Light Gradient Boosting Machine

Abstract Accurate weld-region identification is essential for weld quality inspection and automated grinding. However, weld point clouds are highly irregular and lack explicit topological structure, which makes accurate recognition challenging. To address this issue, this study formulates weld point-cloud recognition as a binary point-wise classification task. Each point is classified as either weld bead or base metal. A systematic classification framework is established by combining neighborhood-based geometric feature extraction, baseline model comparison, and metaheuristic hyperparameter optimization. Three morphology-specific weld subsets, including straight-line, curved-line, and S-shaped welds, are used for evaluation. The classification performance of Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) is first compared under different neighborhood scales. Overall Accuracy (OA), Precision, Recall, and F1-Score are used as evaluation metrics. The results show that LightGBM achieves the best baseline performance at a neighborhood radius of 1.5 mm. To further improve classification performance, LightGBM hyperparameters are optimized using metaheuristic algorithms. The compared optimizers include the Artificial Lemming Algorithm (ALA), Alpha Evolution Algorithm (AE), and Starfish Optimization Algorithm (SFOA). Repeated-run results demonstrate that AE-LightGBM achieves the most favorable overall performance under the unified evaluation protocol. Statistical significance analysis and convergence analysis further support the effectiveness of AE among the compared optimizers. In addition, SHapley Additive exPlanations (SHAP) is employed to analyze feature contributions and improve the interpretability of the optimized model. The proposed method provides an effective technical pathway for robot-based weld recognition and grinding tasks using 3D vision and supervised machine learning.

Poland’s economy is thriving, but its science is dying

Segmentation and classification of hippocampal subregions using multi-task generative adversarial networks

Experimental, numerical, and data-driven analysis of impulsive ice-shedding-induced vibrations in wind turbine blades

Abstract This research investigates the dynamic response of a glass-filled nylon wind turbine blade subjected to ice shedding and turbulent wind conditions, with the aim of understanding and predicting structural behaviour under realistic operating scenarios. Experiments were conducted to capture the blade’s vibration response under controlled conditions, with the blade fixed at the root and measurements taken at the mid-span and tip. Ice shedding was modelled by attaching masses ranging from 100 g to 1 kg at various positions and release angles (− 5°, 0°, 5°, and 15°), allowing evaluation of how sudden mass loss influences dynamic behaviour; the first peak response was analysed as it represents the maximum deflection and highest risk of structural failure. A finite element model was developed and validated using the experimental results to accurately represent the blade’s structural dynamics and predict its natural frequencies and mode shapes, with frequency differences below 0.6% confirming model accuracy. Random vibration analysis using the Kaimal turbulence spectrum with frequency differences below 0.6% confirming model accuracy. In addition, neural network models such as Multi-Layer Perceptron, Levenberg-Marquardt (LM), and Scaled Conjugate Gradient were trained on 140 data points to predict trends in blade displacement amplitude as a function of ice mass, sensor location, and blade orientation under ice shedding and sudden mass loss scenarios, with Bayesian Regularization achieving the best performance (RMSE = 0.0032). While the blade response follows classical bending behaviour, the novelty of this study lies in the experimental methodology and integrated framework for evaluating ice-shedding effects. A noticeable change in the response is observed when the released mass becomes comparable to the blade mass, demonstrating the sensitivity of the method to mass variation. Overall, the approach provides a basis for design evaluation and structural monitoring of wind turbine blades under ice shedding and turbulent wind conditions.