Rumination thinking is a type of negative repetitive thinking, a tendency to constantly focus on the causes, consequences and other aspects of negative events, which has implications for a variety of psychiatric disorders. Previous studies have confirmed a strong association between rumination thinking and poor sleep or insomnia, but the direction of causality between the two is not entirely clear. This study examined the relationship between rumination thinking and sleep quality using a longitudinal approach and resting-state functional MRI data. Participants were 373 university students (males: n = 84, 18.67 ± 0.76 years old) who completed questionnaires at two time points (T1 and T2) and had resting-state MRI data collected. The results of the cross-lagged model analysis revealed a bidirectional causal relationship between rumination thinking and sleep quality. Additionally, the functional connectivity (FC) of the precuneus and lingual gyrus was found to be negatively correlated with rumination thinking and sleep quality. Furthermore, mediation analysis showed that rumination thinking at T1 fully mediated the relationship between FC of the precuneus-lingual and sleep quality at T2. These findings suggest that rumination thinking and sleep quality are causally related in a bidirectional manner and that the FC of the precuneus and lingual gyrus may serve as the neural basis for rumination thinking to predict sleep quality. Overall, this study provides new insights for enhancing sleep quality and promoting overall health.

It is feasible to predict delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH) using Artificial intelligence (AI) algorithms, which may offer significant improvements in early diagnosis and patient management. This systematic review and meta-analysis evaluate the efficacy of machine learning (ML) in predicting DCI, aiming to integrate complex clinical data to enhance diagnostic accuracy. We searched PubMed, Scopus, Web of science, and Embase databases without restrictions until June 2024, applying PRISMA guidelines. Out of 1498 studies screened, 10 met our eligibility criteria involving ML approaches in patients with confirmed aSAH. The studies employed various ML algorithms and reported differential ML metrics outcomes. Meta-analysis was performed on eight studies, which resulted in a pooled sensitivity of 0.79 [95% CI: 0.63-0.89], specificity of 0.78[95% CI: 0.68-0.85], positive DLR of 3.54 [95% CI: 2.22-5.64] and the negative DLR of 0.28 [95% CI: 0.15-0.52], diagnostic odds ratio of 12.82 [95% CI: 4.66-35.28], the diagnostic score of 2.55 [95% CI: 1.54-3.56], and the area under the curve (AUC) of 0.85. These findings show significant diagnostic accuracy and demonstrate the potential of ML algorithms to significantly improve the predictability of DCI, implying that ML could impart a significant role on improving clinical decision making. However, variability in methodological approaches across studies shows a need for standardization to realize the full benefits of ML in clinical settings.

High-grade glioma (HGG) is an aggressive brain tumor with poor survival rates. Predicting survival outcomes is critical for personalized treatment planning. In recent years, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) models, has emerged as a promising approach for enhancing prognostic accuracy in HGG but this study especially focused on the potential of AI in the recurrence of HGG. A systematic review and meta-analysis were conducted to assess the performance of AI-based models in predicting survival outcomes for HGG patients. Relevant studies were retrieved from PubMed, Embase, Scopus, and Web of Science until 2 Dec 2024, using predefined keywords ("High-Grade Glioma", "Survival" and "Machine Learning") without date or language restrictions. Data extraction and quality assessment were performed in accordance with PRISMA and PROBAST guidelines. In this study were included. The pooled diagnostic metric, the area under the curve (AUC), was analyzed using random-effects models. A total of 39 studies with 29 various algorithms and 79,638 patients were included, with 15 studies contributing to the meta-analysis. The most commonly used algorithms were random forest (RF) and logistic regression (LR), which demonstrated robust predictive accuracy. The pooled AUCs for one-year, two-year, three-year and overall survival predictions were 0.816, 0.854, 0.871 and 0.789 respectively. Subgroup analysis revealed that RSF achieved the highest predictive accuracy with an AUC of 0.91 (95% CI: 0.84-0.98), while LR followed with an AUC of 0.89 (95% CI: 0.82-0.96). Models integrating clinical, radiomics, and genetic features consistently outperformed single-data-type models. MRI was the most frequently utilized imaging modality. AI-based models, particularly ML and DL algorithms, show significant potential for improving survival prediction in HGG patients. By integrating multimodal data, these models offer valuable tools for personalized treatment planning, although further validation in prospective, multicenter studies is needed to ensure clinical applicability.

An important source of long range forecast skill for the North Atlantic Oscillation (NAO) comes from predictability of tropical rainfall. While the El Niño Southern Oscillation (ENSO) is a better-known driver of the NAO, the Indian Ocean Dipole (IOD) also has an influence, particularly when ENSO is inactive. Given future projected changes to ENSO and the IOD, it is important to understand how the IOD–NAO teleconnection may evolve. Here we use climate model simulations to investigate the IOD and its NAO teleconnection. We find that the IOD itself changes considerably under climate change, with a weakening of the present-day anticorrelation between the dipole nodes and a westward shift in the IOD pattern. While historical model simulations reproduce the IOD–NAO teleconnection pathway seen in observational analyses, the teleconnection is projected to weaken in future, with the weakening linked to the westward IOD shift.

The ultimate goal of understanding the structure of matter has spurred a constant search for composite particles, especially high-order correlated entities for nearly all forms of matter, from elementary particles, nuclei, and cold atoms, to condensed matter. So far, composite particles involving two or three constituent particles and their weak-coupling combinations have been experimentally studied, such as the Cooper pairs, excitons, trions, and bi-excitons in condensed matter physics, or diquarks, mesons, and di-mesons in quantum chromodynamics. Although genuine four-particle correlated entities have long been theorized in various materials, alternatively known as quadruplons (Rausch and Potthoff in New J. Phys. 18, 2016), quadrons (Quang et al. in Physica B 602, 2021), or quartets (Jiang et al. in Phys. Rev. B 95, 2017), the only closely related experimental evidence is the tetraquark observation at CERN (LHCb in  Nat. Phys. 18, 751–754, 2022). In this article, we present for the first time the experimental evidence for the existence of a four-body entity in condensed matter, the quadruplon, involving two electrons and two holes in a monolayer of Molybdenum Ditelluride. Using the optical pump–probe technique, we discovered a series of new spectral features in addition to those of excitons and trions. Furthermore, we found that all these spectral features could be reproduced theoretically using transitions between the two-body and four-body complexes based on the Bethe–Salpeter equation. Interestingly, we found that the fourth-order irreducible cluster is necessary and sufficient for the new spectral features by using the corresponding cluster expansion technique. Thus, our experimental results combined with theoretical explanation provide strong evidence for the existence of a genuine four-particle entity, the quadruplon. In contrast to a bi-exciton which consists of two weakly interacting excitons, a quadruplon involves tightly bound four-particle entity without the presence of well-defined excitons. Our results could impact the understanding of the structure of materials in a wide range of physical systems and potentially lead to new photonic applications based on quadruplons.