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Novelty and appropriateness are two fundamental components of creativity. However, the way in which novelty and appropriateness are separated at behavioral and neural levels remains poorly understood. In the present study, we aim to distinguish behavioral and neural bases of novelty and appropriateness of creative idea generation. In alignment with two established theories of creative thinking, which respectively, emphasize semantic association and executive control, behavioral results indicate that novelty relies more on associative abilities, while appropriateness relies more on executive functions. Next, employing a connectome predictive modeling (CPM) approach in resting-state fMRI data, we define two functional network-based models-dominated by interactions within the default network and by interactions within the limbic network-that respectively, predict novelty and appropriateness (i.e., cross-brain prediction). Furthermore, the generalizability and specificity of the two functional connectivity patterns are verified in additional resting-state fMRI and task fMRI. Finally, the two functional connectivity patterns, respectively mediate the relationship between semantic association/executive control and novelty/appropriateness. These findings provide global and predictive distinctions between novelty and appropriateness in creative idea generation.
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http://dx.doi.org/10.1038/s42003-024-06405-0 | DOI Listing |
Expert Rev Clin Immunol
September 2025
Allergology Unit, Guglielmo da Saliceto Hospital, Piacenza, Italy.
Introduction: The demographic increase, environmental concerns, and heightened awareness about health have led Western countries to consider edible sources previously overlooked. The novelty of these sources implies a scarce knowledge about their allergenicity.
Areas Covered: This review has the purpose of offering an arranged view about the allergenicity of different categories of novel foods, such as edible insects, new plant-based foods, and microalgae, by exploring cross-reactivity and common traits with other food allergies but also specific peculiarities.
J Med Case Rep
August 2025
General Hospital of Northern Theater Command, Shenyang, China.
Background: Abdominal pain caused by diabetic ketoacidosis is uncommon and can easily be misdiagnosed as abdominal pain caused by ureteral stones.This case highlights the importance of distinguishing between these etiologies, particularly in patients with diabetes, as delayed recognition of diabetic ketoacidosis can result in life-threatening complications. The novelty lies in emphasizing the diagnostic challenges and the necessity for clinicians to consider diabetic ketoacidosis even when imaging suggests alternative causes.
View Article and Find Full Text PDFBMC Nurs
August 2025
Department of Industrial Engineering and Economics, Institute of Science Tokyo, Tokyo, 152-8552, Japan.
Background: As a sharp increase in healthcare demand has led to a severe shortage of nurses in aging societies, international nurses become to play a crucial role in supporting healthcare systems. However, they often face immigration-specific stress that may influence their 24-hour movement behaviors, including physical activity, sedentary behavior, and sleep, as key determinants of health. Despite the importance of these behaviors, limited research has examined the complex interrelationships among 24-hour movement behaviors in this population.
View Article and Find Full Text PDFActa Parasitol
August 2025
Animal Biology Key Laboratory of Chongqing Education Commission of China, Chongqing Key Laboratory of Conservation and Utilization of Freshwater Fishes, College of Life Sciences, Chongqing Normal University, Chongqing, 401331, China.
Purpose: This study describes a novel myxosporean species, Auerbachia megacapsula n. sp., isolated from the gallbladders of Larimichthys crocea Richardson, 1846 from the South China Sea.
View Article and Find Full Text PDFExpert Rev Proteomics
August 2025
Department of Computer Science, University of Antwerp, Antwerp, Belgium.
Introduction: Machine learning holds significant promise for accelerating biomarker discovery in clinical proteomics, yet its real-world impact remains limited by widespread methodological pitfalls and unrealistic expectations.
Areas Covered: In this perspective, we critically examine the application of machine learning for biomarker discovery in clinical proteomics, emphasizing that algorithmic novelty alone cannot compensate for issues such as small sample sizes, batch effects, overfitting, data leakage, and poor model generalization.
Expert Opinion: We caution against the uncritical application of complex models, such as deep learning architectures, that often exacerbate these problems, offering limited interpretability and negligible performance gains in typical clinical proteomics datasets.