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Persistent representation of a prior schema in the orbitofrontal cortex facilitates learning of a conflicting schema
Abstract Schemas allow efficient behavior in new situations, but reliance on them can impair flexibility when new demands conflict. Evidence implicates the orbitofrontal cortex (OFC) in deploying schemas in new situations. But how does this role affect learning of a conflicting schema? Here we addressed this question by recording or transiently inactivating OFC neurons in rats learning odor problems with identical external information but orthogonal rules governing reward. OFC representations adapted to track the underlying rules, and both performance and encoding were faster on subsequent than initial problems. Surprisingly, when the rule changed, persistent representation of the prior schema predicted faster acquisition of the new, and disrupting OFC activity during initial schema learning, later impaired acquisition of the second schema. Thus, rather than interfering with new learning, OFC neural activity was linked to improved acquisition by preserving accurate representations of the prior schema alongside the new one.
Correction: Alpinetin pretreatment prevents lipopolysaccharide/D-galactosamine-induced acute liver injury in mice by inhibiting ferroptosis via the Nrf2/SLC7A11/GPX4 pathway
The potential impact of wheat stem rust on global agricultural supply, demand, and food security, considering market interactions
Wheat stem rust, a fungal disease that can be highly devastating under the right environmental conditions, was reduced to non-economically damaging levels during the Green Revolution. However, it has reemerged as a global threat to wheat production due to the appearance of new virulent strains in Uganda in 1999 that have spread steadily to other geographic areas. Wheat experts warn that the disease could pose a catastrophic threat to the global wheat supply if not monitored. Considering the importance of wheat as a principal source of calories, nutrients, and farm income throughout the world, assessments of the potential impacts of the disease are urgently required in order to formulate an appropriate response. Published assessments so far vary widely in method and results, and generally focus on wheat production losses alone, without considering how markets may offset or aggravate impacts (spillover effects). Here we take an integrated assessment approach and examine a set of “what-if” scenarios to account for direct and indirect economic and food security impacts of wheat stem rust in various world regions over the years 2026–2050. The severity and frequency of epidemics is introduced into the modeling framework based on a survey of international wheat experts. The results suggest that global market incentives may offset the worst impacts of wheat stem rust in most affected areas via international trade. However, the market mechanism simultaneously precipitates considerable food insecurity in areas far from any epidemic, as farms in these areas reallocate resources from the domestic cereal market to the wheat export market, in response to price signals.
Triggering action potentials of a single neuron by multiphoton excitation elicits visually guided behavior
Optimized thermal response of Au nanoframes in NIR-II window: a numerical study
Correction: Investigating the causal effects of COVID-19 vaccination on the adoption of protective behaviors in Japan: Insights from a fuzzy regression discontinuity design
Hypersensitive detection of single millimeter vascular emboli from adhesive in vivo
Abstract Surgical adhesives are widely used in clinical practice but pose a significant risk of severe vascular embolism complications. Nevertheless, there are currently no non-invasive direct methods for precise detection of detached emboli. Herein, we show a CT-visualized method for hypersensitive detection of single millimeter vascular emboli from adhesive in vivo by simply doping BiOCl into surgical adhesives. As proof of concept, BiOCl-BioGlue with excellent CT imaging capability is fabricated and applied to repair ruptured vessels and liver in male rats. The location, morphology, and degradation process of BiOCl-BioGlue can be dynamically monitored by CT imaging for 42 days, and pulmonary emboli caused by BiOCl-BioGlue, with sizes as small as 1.2 mm, are successfully detected. Additionally, the high K-edge of Bi enables precise detection of pulmonary emboli in spectral CT imaging, unaffected by confounding calcifications. The proposed non-invasive detection strategy for adhesive emboli significantly enhances the biosafety of surgical adhesives.
Neural responses to virtual avatars are shaped by user preference and personality traits
Abstract This study aimed to investigate individual differences’ effects on brain activity in selected and non-selected avatars for re-engagement. The development of some applications for human–computer interaction has accelerated over the past decade. To develop a human–computer communication system using virtual avatars without losing the user’s interest and attention, this study revealed differences in the neural mechanisms underlying the perception of virtual avatars between avatars with which users want to converse again (selected avatars) and those with which they do not (non-selected avatars). Forty-two individuals were recruited; they watched two videos in sequence in which each virtual avatar greeted them, and they then reported which avatars they wanted to talk to again. Meanwhile, brain activities were recorded by functional magnetic resonance imaging (fMRI). After the fMRI recording, the responses to the questionnaires regarding personality traits and avatar impressions were rated. Brain activities were compared along with the score of each personality questionnaire. The results indicated that the left middle temporal gyrus (MTG) was more active in selected compared to non-selected avatars. Furthermore, the brain activities of right superior frontal gyrus (SFG) and the left middle cingulate gyrus (MCG) had a statistically negative correlation with the score of openness in the Ten-Item Personality Inventory (TIPI) in selected avatars. These findings indicate that neural responses during brief avatar evaluation are associated with both avatar selection outcomes and individual personality differences. While these results do not permit direct inferences about specific psychological processes, they provide an insight into the neural correlates of early-stage avatar preference formation in human–computer interaction.
Soil organic carbon dynamics: Influences of land-use change in natural and plantation forests of the Western Ghats, India
Soil organic carbon (SOC) is a fundamental component of the global carbon cycle, underpinning ecosystem health, climate regulation, and sustainable land management worldwide. The conversion of natural forests to plantation systems in humid tropical regions has emerged as a critical global issue, leading to significant reductions in SOC stocks and compromising the carbon sequestration potential of soils. To assess these impacts, we compared SOC concentrations in plantation and natural forests across five humid tropical zones, including the globally significant Southern Western Ghats (SWG) of India- a recognized biodiversity hotspot and one of the world’s most complex forest ecosystems. A stratified random sampling design was used across five agroecological zones to select paired natural forests and adjacent long-rotation teak ( Tectona grandis ) plantations (40–50 years old). Soil samples were collected from four horizons (O, A, B, and C) within 1 m depth profiles. SOC concentration (CHNS analyzer), bulk density, texture (hydrometer method), cation exchange and pH were determined. SOC stocks were calculated using bulk density and horizon depth. Our analysis shows that natural forests maintain substantially higher average SOC concentrations (16.61 g/kg) than plantation forests (11.82 g/kg). In natural forests, SOC ranged from 9.53 g/kg to 26.09 g/kg, while plantation forests ranged from 6.93 g/kg to 17.73 g/kg, reflecting similar trends observed in the SWG and other tropical regions. SOC concentrations were significantly greater in the surface layers of natural forests compared to deeper layers (P < 0.05), with more than 70% of SOC typically stored in the upper 30 cm. Correlation analysis showed a significant negative relationship between SOC and soil pH in natural forests (r = −0.37, P < 0.05), whereas plantation soils exhibited a positive relationship (r = 0.03, P < 0.05). Forest soils also showed a positive correlation between SOC and clay content (r = 0.16, P < 0.05) and a weak negative correlation with sand content (r = −0.04, P < 0.05). These findings underscore a global challenge: land use change from natural forest to plantation reduces SOC stocks, alters soil health, and diminishes the resilience of tropical soils to environmental change. Maintaining and restoring natural forests—both globally and in biodiversity hotspots like the SWG—is essential for maximizing soil carbon sequestration, supporting soil fertility, and achieving climate mitigation targets. This study provides a scientific foundation for sustainable land management and carbon storage strategies in tropical regions globally.
Global patterns of inequality in pedestrian shade provision
Predicting the effects of temperature variability on nutritional status of children under five in Sub-Saharan Africa using machine learning
Refining weak supervision for robust lung cavity segmentation: A graph-affinity method with boundary constraints
Pixel-level annotation of lung cavities (LCs) in computed tomography (CT) images is challenging due to their morphological diversity and complexity. Weakly supervised semantic segmentation (WSSS) methods, which utilize sparse annotations (e.g., image-level labels), offer a promising solution. However, existing WSSS approaches often generate coarse pseudo-labels and lack sufficient spatial supervision, resulting in under- or over-segmentation of irregular lesions. To address these limitations, we introduce several key innovations. First, we propose a novel Graph-based Affinity Network (GA-Net) that, unlike conventional methods relying on low-level pixel features, models long-range contextual relationships and structural dependencies using a superpixel graph and learned edge inference kernel, enabling structure-aware pseudo-label refinement for complex lesion morphology. Second, we introduce region-wise affinity propagation, which refines segmentation by propagating activations within semantically coherent 3D regions, offering more precise control over under-/over-segmentation compared to global affinity methods. Additionally, we incorporate Exponential Moving Average (EMA) ensembling for training stability and a scribble-based segmentation module that utilizes pseudo-label contours to provide direct boundary supervision. Extensive experiments on three benchmark datasets demonstrate that our method outperforms existing state-of-the-art medical WSSS techniques, achieving precise and reliable segmentation of complex LCs in CT scans.
Application analysis of transfected cell method for detecting AChR antibodies in MG patients
Abstract This study aimed to establish a technical process for detecting nicotinic acetylcholine receptor (nAChR) antibodies using the transfected cell method and evaluate its application in the serological diagnosis of myasthenia gravis (MG), thereby enhancing diagnostic efficiency. Cell transfection technology was used to introduce various nAChR subunit combinations into HEK293 cells for antibody detection. Indirect immunofluorescence (IIF) was utilized to test nAChR antibodies in serum samples from 85 MG patients, and the results were compared for consistency with those of enzyme-linked immunosorbent assay (ELISA).The combination of fetal and adult AChR subunits in transfected cells exhibited the highest sensitivity for detecting serum antibodies in patients with MG. The prepared cell slides demonstrated excellent consistency with the ELISA kit results for 85 MG patients, yielding a Kappa value of 0.769, indicating excellent agreement between the two methods. Co-transfection of multiple AChR subunits successfully generated a cell expressing clustered nAChRs, establishing a highly sensitive detection technique for nAChR antibodies. This technique is invaluable for serological detection of patients with MG, facilitating early disease detection, condition assessment, and therapeutic guidance.
Evaluating the effectiveness of preservice midwifery curricula in Ethiopia: A comparison of neonatal resuscitation and infection prevention practice of midwifery graduates trained in competency-based versus conventional curricula
Background Infection control and neonatal resuscitation are essential midwifery practices that can reduce maternal and neonatal mortality. However, in Ethiopia, theory-heavy midwifery education leads to limited clinical competence. To address this, Debre Tabor University implemented a competency-based curriculum in 2013. This study examines whether competency-based midwifery education produces graduates with significantly better performance in neonatal resuscitation and infection prevention compared to conventional education, thereby framing a testable argument about the curriculum’s effectiveness. Methods A comparative cross-sectional study assessed the infection prevention and neonatal resuscitation performance of 68 BSc midwifery graduates (32 competency-based vs. 36 conventional) from third-generation Ethiopian universities. Performance was measured using a validated observation tool in clinical settings for infection prevention and simulations for neonatal resuscitation. Mean percentage scores were compared using t -tests, with effect size illustrated via Gardner–Altman plots. Results Overall, midwives demonstrated 63.6% of essential neonatal resuscitation tasks, with competency-based curriculum graduates (CBCGs) having higher performance than conventional curriculum graduates (CCGs) (71.6% vs. 56.5%; t (66.0) = 3.82, p < .001; difference = 15.1%), particularly in airway suctioning and chest rise assessment. For infection prevention, midwives performed 71.7% of the required tasks, with CBCGs again scoring higher (76.9% vs. 67.0%; t (55.4) = 2.79, p < .01; difference = 9.9%). Key differences were observed in hand hygiene, the use of personal protective equipment, and apron decontamination. Despite these improvements, persistent deficiencies remained in both groups, particularly in checking breathing/pulse during neonatal resuscitation and in disinfecting aprons during infection prevention practices. Conclusions CBCGs demonstrated better performance in neonatal resuscitation and infection prevention compared to those from the conventional program, suggesting clinical relevance. However, performance gaps in both groups underscore the need for enhanced simulation training, ongoing skill reinforcement, and curriculum refinement.
Modelling the effect of motivation on mental health components with fuzzy logic among elite athletes
AI-based prediction of recurrence after carbon ion radiotherapy for early stage non-small cell lung cancer
Lung cancer is a leading cause of cancer-related deaths. Carbon ion radiotherapy (CIRT) is a treatment modality for patients with inoperable conditions or who decline surgery, but there is room for research to identify patients at high risk of recurrence. The use of artificial intelligence (AI)-based predictive models in healthcare is growing, yet their application in predicting outcomes after CIRT in NSCLC remains unexplored. This study developed an AI prediction model using clinical and imaging data to identify patients at high risk of recurrence after CIRT for early stage NSCLC. Patients with untreated early stage peripheral NSCLC undergoing CIRT between June 2010 and December 2020 were included. Simulated computed tomography (CT) images and clinical data were used to develop a model to predict recurrence within 2 years of CIRT. The model was tested using 5-fold cross-validation and evaluated using receiver operating characteristic (ROC) analysis. The study involved 124 patients. Two-year overall survival, local control, and progression-free survival rates stood at 90.8%, 91.0%, and 69.4%, respectively. The three-axis plane method for CT image input was more predictive than the three-transverse plane or 3D methods. Our AI-based model using CT images and clinical data predicted recurrence within 2 years of CIRT with a median area under the ROC curve of 0.762. Gradient-weighted class activation mapping enhanced model interpretability. The multimodal AI-based model identifies early stage NSCLC patients at high recurrence risk after CIRT although external validation is required for its generalizability and robustness.
Aberrantly expressed long noncoding RNAs in adipose-derived mesenchymal stem cells differentiation to nucleus pulposus-like cells
Beyond traditional stimuli: Validating AI-generated images for eliciting negative emotions in affect research
Studies of emotion often rely on standardized stimulus sets to elicit affective responses. Although established databases provide images with normative valence and arousal ratings, selecting suitable stimuli can be difficult when experiments require specific thematic or content constraints. This challenge is especially pronounced for negative stimuli, which are central to research on maladaptive emotions and behaviors in clinical contexts but are often scarce in necessary quantity or specificity. The present study evaluated the feasibility of using generative AI, specifically text-to-image generators, to create tailored negative and neutral affective stimuli. To assess whether these images can serve as alternatives to traditional stimuli, we compared their affective properties to those reported in standardized image databases. Across two studies, participants rated the valence and arousal of 160 and 200 AI-generated images. Our findings revealed that AI-generated negative and neutral images reproduced the characteristic inverse association between valence and arousal observed in standardized databases, with moderate to strong correlations between these dimensions. These results highlight the potential of generative AI as a practical methodological tool for creating customized affective stimuli aligned with specific research objectives and experimental designs.