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ADHD medications and preadolescent brain structure: patterns of cortical attenuation from the ABCD study
Large-scale integrated optoelectronic chaos for machine learning acceleration
Abstract Chaos has emerged as a useful resource for machine learning, yet traditional nonlinear circuits face speed bottlenecks. Optical chaos sources offer an attractive alternative with ultra-wideband operation and massive parallelism, but existing schemes must trade single-channel throughput against multi-channel scalability. Here, we demonstrate an integrated microcomb-optoelectronic chaos engine (iMOCE). By driving an optoelectronic nonlinear cavity with a chaotic microcomb, the iMOCE generates massively parallel chaos with a 6-dB bandwidth of 25 GHz per channel, representing a two-order-of-magnitude improvement over previous microcomb-based approaches. The system delivers a total random-bit generation rate of 32.768 Tbps and accelerates four representative tasks. Compared with MCU/GPU baselines, it reduces per-inference time by about two orders of magnitude. These results establish iMOCE as a scalable, massively parallel chaos primitive for machine learning acceleration.
Acute stress impairs visual narrative comprehension in younger but not older adults
Abstract Visual narrative comprehension is essential for navigating modern society, where information, rules, and news are frequently communicated through images, diagrams, and visual stories. Encoding a coherent narrative from disparate elements is critical for all age groups. Although recent studies report a significant rise in stress and anxiety levels, driven by factors such as the COVID-19 pandemic and recent geopolitical conflicts, the impact of stress on visual narrative comprehension remains largely underexplored. This study explored how acute stress affects narrative comprehension in younger ( N = 203, 18–57 years; M = 23 years; Experiment 1) and older adults ( N = 212, 60–85 years; M = 67 years; Experiment 2). Participants were assessed under both acute stress and neutral conditions. A tool for inducing acute stress online employed mathematical and logical tasks under time pressure, along with elements that simulate social stress. Participants were presented with pictorial stories consisting of three panels, with the second panel intentionally left blank. Their task was to comprehend the storyline despite the missing information. On the following page, an image representing a possible bridging event was shown, either depicting the correct or an incorrect inference. Participants were asked to judge whether or not the presented image accurately reflected the missing event in the story. Results revealed that acute stress negatively impacted narrative comprehension in younger adults, while the older adults’ comprehension remained unaffected by acute stress. Similarly, younger adults demonstrated reduced confidence in their responses under stress, whereas older adults’ confidence levels remained unaffected. These findings highlight the relationship between visual narrative comprehension, stress, and aging, suggesting that, with age and experience, comprehenders may develop more differentiated event schemas, which makes their comprehension processes more resilient to stress. Understanding how cognitive and perceptional processes function under stress is crucial for daily life across all age groups. Our research demonstrates that younger adults exhibit poorer visual narrative comprehension under acute stress, whereas older adults’ performance remains stable. This finding suggests that older adults may employ more differentiated event schemas, which help maintain their narrative comprehension in the face of stress. Consequently, narrative comprehension appears to be more resilient compared to other fundamental cognitive skills. These insights could inform interventions and strategies to support cognitive health across different age groups.
Fine-scale heterogeneity and local amplification of West Nile virus in urban environments in Berlin
Abstract Climate change can intensify mosquito-borne disease risks through rising temperatures and more frequent extreme weather events. To mitigate effects of climate change, cities are adopting nature-based solutions, such as urban greening and rainwater management, yet their implications for vector-borne diseases and host community composition remain poorly understood. West Nile virus (WNV), an emerging mosquito-borne human pathogen in Europe, is primarily transmitted between birds and mosquitoes. Using mosquito sampling at five sites within a one-square-kilometre area in Berlin, Germany, we examined how urban land cover, including climate-resilient infrastructure, influences local WNV amplification over two mosquito seasons in 2023 and 2024. We found seasonal WNV infection rates of up to 4.8% in mosquitoes and identified fine-scale heterogeneity in infection risk. Residential areas and cemeteries exhibited the highest minimum infection rates per month (up to 15 and 21, respectively), whereas natural conversation and sponge city sites showed significantly lower rates (up to 4 and 13, respectively). These patterns were not explained by mosquito abundance or species composition but by habitat characteristics and avian host community structure. Our findings reveal that urban land cover shapes WNV infection risk and suggest that incorporating biodiversity restoration into nature-based solutions may serve as strategy for sustainable climate-resilient urban planning.
Machine learning guided processing, microstructure and coercivity mapping in M type strontium hexaferrite
Experimental human colonisation with non-toxigenic Clostridioides difficile: a placebo-controlled randomised clinical trial
Prediction of soil shear strength using hybrid machine learning approaches for performance and interpretability analysis
Abstract Shear strength of soil plays an essential role in geotechnical properties affecting construction stability. Conventional laboratory testing for determining soil shear strength is often expensive and time-consuming. Therefore, machine learning (ML) methods were employed to predict soil shear strength using geotechnical parameters compiled from previously published literature based on investigations conducted at the Le Trong Tan Geleximco project in western Hanoi, Vietnam. In total, there were 202 samples analyzed, including parameters such as depth, sand percent, loam percent, clay percent, moisture content, wet density, dry density, void ratio, liquid limit, plastic limit, plasticity index, and liquidity index. Four predictive models have been developed and analyzed, including Multiple Linear Regression (MLR), Support Vector Machine (SVM), Random Forest (RF), and Multi-Expression Programming (MEP). The model performance was evaluated using several parameters like coefficient of determination (R 2 ), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), residual analysis, and Taylor diagrams. RF showed the best predictive performance, with training R 2 = 0.9527 and testing R 2 = 0.8578, along with the lowest prediction error. The SVM model performed impressively in terms of prediction, while the MEP model demonstrated satisfactory accuracy along with the added benefit of mathematical equation formulation. Conversely, the MLR model was relatively less accurate because of its inability to deal with nonlinear relations. Moreover, the SHAP analysis revealed that liquid index, moisture content, and plasticity index were the most critical factors influencing the prediction of soil shear strength. This research has established that modern machine learning models like RF and SVM prove to be efficient at modeling the nonlinear nature of soil characteristics.
A stretch-responsive fibroblast program promotes epidermal stem cell self-renewal during skin expansion
Spatiotemporal dynamics and antimicrobial resistance of bacterial pathogens recovered from various fomites at a paediatric care facility in Ghana
Human CD24+ dental papilla cells are competent seed cells for dentin-pulp regeneration via BMP2/SIRT1 axis
Dietary lactic acid and rosemary leaf supplementation enhances growth and immune responses in Nile tilapia (Oreochromis niloticus)
Abstract The present study investigated the individual and combined effects of lactic acid and rosemary meal on growth performance, biochemical parameters, immune responses, the expression of growth and antioxidant-related genes, intestinal morphology, and oxidative status in Nile tilapia ( Oreochromis niloticus ). A total of 120 apparently healthy fish, with an average weight of 3.03 ± 0.02 g, were randomly assigned to four equal groups, each consisting of three replicates. Four experimental diets were formulated: a basal control diet (CON), a basal diet supplemented with 1 g of lactic acid per kg of diet (LA), a basal diet containing 10 g of rosemary per kilogram of diet (RM), and a basal diet that included both supplements (LA + RM). Fish were fed these diets for a duration of 60 days. The results indicated that either lactic acid or rosemary alone or in combination had a greater growth-stimulating impact than the CON group ( P ≤ 0.05) with superiority to the combination group (LA + RM group). Activities of aspartate aminotransferase and alanine aminotransferase, along with creatinine and urea levels, were significantly reduced ( P ≤ 0.05) in the groups of lactic acid and rosemary alone or in combination relative to the CON group. Total protein and albumin concentrations were elevated in the LA + RM group ( P ≤ 0.05). Intestinal histology revealed normal morphology across groups, with increased villus height, intestinal villi spacing, and goblet cell density in LA + RM ( P ≤ 0.05) without pathological lesions in the liver and spleen. Antioxidant, immune, and growth-related gene expressions were upregulated in RM and LA + RM groups. In conclusion, rosemary supplementation, alone or combined with lactic acid, enhanced fish health status and upregulated target genes without pathological lesions with superiority to the combination treatment.
Catalytic asymmetric formal nucleophilic C2-substitution of indoles via C2-umpolung toward 2,3’-bisindole atropisomers
Integrated experimental and machine learning investigation of green-synthesized ZnO nanoparticle-dispersed neem biodiesel: effects on diesel engine performance, combustion, and emissions
Heterogeneity and multi-scale dynamics in the molecular bearing of the bacterial flagellum
Abstract The bacterial flagellum is a protein-based rotary machine that drives bacterial motility. It comprises the bacterial flagellar motor (BFM), consisting of a stator which is anchored to the cell wall and a rotor in the cytoplasmic membrane, linked via the flagellar rod to the extracellular hook and filament. We observe passive rotational diffusion of six individual Escherichia coli flagella lacking torque-generating units via polarization microscopy of single gold nanorods attached to the hook, sampled at 250 kHz. Transitions across energy barriers of the 26-fold symmetric LP-ring/rod flagellar bearing exhibit highly non-Poissonian kinetics spanning four orders of magnitude in time scale. At sub-millisecond timescales we observe anomalous ultra-slow diffusion typically associated with disordered systems, despite the ordered crystalline atomic structure of the bearing revealed by cryo-Electron Microscopy. Over longer periods, we observe dynamic shifts in the preferred angular positions, indicating that the bearing’s energy landscape evolves over time.
Clinical and genomic characterization of Influenza A co-infection with SARS-CoV-2 and Influenza B: a respiratory surveillance study in Assam, India
Abstract Influenza and SARS-CoV-2 are the primary contributors to seasonal respiratory infections and frequently co-circulate, creating significant health challenges. The present respiratory surveillance study was conducted in Dibrugarh, Assam, India from January 2025 to August 2025 to investigate the genomic characteristics of circulating viruses and identify potential co-infections. Overall, 4,948 respiratory samples were screened using multiplex real-time PCR, followed by subtyping of Influenza A and Influenza B. Next-generation sequencing (NGS) was performed in selected positives of SARS-CoV-2 and Influenza A. Genomic analysis included mutational profiling, phylogenetic analysis and N-glycosylation site prediction using bioinformatics tools. Two co-infection cases were detected: one involving Influenza A (H3N2) with SARS-CoV-2 (Omicron XFG lineage) and another involving Influenza A (H3N2) with Influenza B (Victoria lineage). Both patients experienced mild illness without hospitalisation. NGS revealed that the Influenza A (H3N2) viruses belonged to clade 3C.2a1b.2a.2a.3a.1 while SARS-CoV-2 sequence was classified under the Omicron XFG lineage. Mutational analysis of the HA gene showed several amino acid differences compared to the reference vaccine strain A/Darwin/6/2021. N-glycosylation analysis predicted conserved sites at positions 79, 181, 262, and 301 in all strains along with an additional predicted site at position 110 in both co-infection cases. Although the co-infection cases presented with mild clinical manifestations, the observed genomic variations indicate a potential role of co-infecting viruses in shaping viral evolution. Given the limited genomic data available from Northeast India, the study underscores the need for sustained large scale follow up and genomic surveillance to monitor emerging mutations and target future vaccine strategies.