Browse Articles

An end-to-end attention-based approach for learning on graphs

Abstract There has been a recent surge in transformer-based architectures for learning on graphs, mainly motivated by attention as an effective learning mechanism and the desire to supersede the hand-crafted operators characteristic of message passing schemes. However, concerns over their empirical effectiveness, scalability, and complexity of the pre-processing steps have been raised, especially in relation to much simpler graph neural networks that typically perform on par with them across a wide range of benchmarks. To address these shortcomings, we consider graphs as sets of edges and propose a purely attention-based approach consisting of an encoder and an attention pooling mechanism. The encoder vertically interleaves masked and vanilla self-attention modules to learn an effective representation of edges while allowing for tackling possible misspecifications in input graphs. Despite its simplicity, the approach outperforms fine-tuned message passing baselines and recently proposed transformer-based methods on more than 70 node and graph-level tasks, including challenging long-range benchmarks. Moreover, we demonstrate state-of-the-art performance across different tasks, ranging from molecular to vision graphs, and heterophilous node classification. The approach also outperforms graph neural networks and transformers in transfer learning settings and scales much better than alternatives with a similar performance level or expressive power.

Analysis of the factors influencing the dissemination of Mazu culture in Southeast Asia during the Qing Dynasty

Mazu culture has had a profound impact on Southeast Asia. Its widespread dissemination during the Qing Dynasty resulted from the combined influence of various factors. This study uses the number of Mazu temples in Southeast Asia during the Qing Dynasty as a quantitative indicator and employs Pearson, Spearman, and Kendall correlation analyses to reveal the central role of geographical environment in cultural diffusion. The data show that there is a threshold effect between the scale of immigration and the distribution of Mazu temples: when the immigrant population exceeds 100,000, the impact significantly weakens. The propagation of Mazu culture is significantly underpinned by a distinctive geo-economic environment, notably characterized by an extensive coastline and a high coastline-to-land ratio, which inherently fosters the development of a marine economy. The high concentration of Mazu temples in port cities highlights the role of the compatibility between the maritime economic network and geographical space in promoting the dissemination of the belief. Consequently, the dissemination and spatial distribution of Mazu culture during the Qing Dynasty were intrinsically linked to the distinctive geographical environment of Southeast Asia.

Influences of carrier sex, body size, and time on the symbiotic interaction between Nicrophorus vespilloides and the Uroobovella nova mite species complex

Abstract Phoretic dispersal is critical in low-mobile invertebrates because it enables feeding, breeding, and gene flow. Phoresy may have serious evolutionary consequences for species in highly specific interactions. Mites within the Uroobovella nova species complex have a narrow range of carriers limited to burying beetles. Nicrophorus vespilloides, a model organism used in behavioural studies, is a common carrier of U. nova, but this interaction remains underexplored. This study investigated how carrier sex, body size, season, and year affect the relationship between U. nova and N. vespilloides. We tested the hypotheses that mite infestation is sex-biased because of differences in parental care between females and males and that larger individuals carry more mites. Mite prevalence was affected only by season. A slightly higher mite load was found in females than in males, and mites showed a significant but weak preference for beetle body size. Considerable temporal differences in mite load were found. Deutonymphs were highly specific when selecting attachment sites, irrespective of the carrier sex, and appeared on some body parts when the preferred sites had already been infested. The low specificity of U. nova towards N. vespilloides individuals and the high selectivity of attachment sites seem to increase the probability of colonising beetle brood chambers.

The evolutionary origin of sensitive dental structures

Emergent mechanics of a networked multivalent protein condensate

Abstract Multivalent proteins can form membraneless condensates in cells by liquid-liquid phase separation, and significant efforts have been made to study their biochemical properties. Here, we demonstrate the emergent mechanics of a functional multivalent condensate reconstituted with six postsynaptic density proteins, using atomic-force-microscopy-based mesoscale rheology and quantitative fluorescence measurements. The measured relaxation modulus and protein mobility reveal that the majority (80%) of the proteins in the condensate are mobile and diffuse through a dynamically cross-linked network made of the remaining (20%) non-mobile scaffold proteins. This percolating structure gives rise to a two-mode mechanical relaxation with an initial exponential decay followed by a long-time power-law decay, which differs significantly from simple Maxwell fluids. The power-law rheology with an exponent α ≃ 0.5 is a hallmark of weak bonds’ binding/unbinding dynamics in the multivalent protein network. The concurrent molecular and mechanical profiling thus provides a reliable readout for characterizing the mechanical state of protein condensates and investigating their physiological functions and associations with diseases.

Machine learning-based prediction model for cognitive impairment risk in patients with chronic kidney disease

Background The high prevalence of cognitive impairment (CI) in Chronic kidney disease (CKD) patients impacts their quality of life and prognosis, yet risk prediction models for CI in this population remain underexplored. Objective This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention. Methods A total of 415 CKD patients from the 2015 China Health and Retirement Longitudinal Survey (CHARLS) dataset were included in this study. Participants were categorized into two groups: the CI group (n = 53) and the non-CI group (n = 362). Binary logistic regression, encompassing both univariate and multivariate analyses, was conducted to identify influencing factors. Subsequently, a CI risk prediction model was constructed using four machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN), and Logistic Regression (LR). The optimal model was further assessed for predictor importance utilizing the SHAP method and deployed on a web platform using the Streamlit library. Results Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. Calibration curves demonstrated that all models were well-calibrated. Among these, the NNET model exhibited the highest predictive performance. According to the SHAP analysis of the optimal model, the most influential predictors are age, education level, and hemoglobin concentration. Conclusion Machine learning models are valuable tools for predicting the risk of CI in CKD patients and can assist healthcare professionals in developing appropriate intervention strategies.

Research on the mechanism of the influence of family socioeconomic status on the Cardinal principle of young children

Abstract This study investigated the mediating roles of Executive Function, Approximate Number System, and Receptive Vocabulary Skills in Family Socioeconomic Status and Cardinality Principle. A cross-sectional research design was used in this study. The study included 130 young children (63 boys and 67 girls, mean age = 68.52 ± 7.37 months) and their parents. Correlation analyses revealed significant positive correlations between children’s understanding of the Cardinality Principle and Family Socioeconomic Status, Executive Function, Approximate Number System, and Receptive Vocabulary Skills. After controlling for children’s gender and age, mediation analyses indicated that Family Socioeconomic Status significantly and positively affected children’s base comprehension. At the same time, Executive Function, Receptive Vocabulary Number System, and Receptive Vocabulary Skills all mediate the relationship between Family Socioeconomic Status and children’s Cardinality Principle to some extent.

Room-temperature cavity exciton-polariton condensation in perovskite quantum dots

Abstract The exploitation of the strong light-matter coupling regime and exciton-polariton condensates has emerged as a compelling approach to introduce strong interactions and nonlinearities into numerous photonic applications. The use of colloidal semiconductor quantum dots with strong three-dimensional confinement as the active material in optical microcavities would be highly advantageous due to their versatile structural and compositional tunability and wet-chemical processability, as well as potentially enhanced, confinement-induced polaritonic interactions. Yet, to date, exciton-polariton condensation in a microcavity has neither been achieved with epitaxial nor with colloidal quantum dots. Here, we demonstrate room-temperature polariton condensation in a thin film of monodisperse, colloidal CsPbBr3 quantum dots, placed in a tunable optical resonator with a Gaussian-shaped deformation serving as wavelength-scale potential well for polaritons. The onset of polariton condensation under pulsed optical excitation is manifested in emission by its characteristic superlinear intensity dependence, reduced linewidth, blueshift, and extended temporal coherence.

A comparative overview of aggregation methods of a graphical risk assessment: An analysis based on a critical infrastructure project

We compare the traditional risk and opportunity assessment method, which relies on fixed values for impacts (or potentials) and probabilities, with a graphical approach that incorporates the representation of uncertainties. To date, this graphical risk assessment method, in combination with subsequent opinion pooling, has neither been empirically studied nor validated. Therefore, its comparison with classical risk assessment remains an open question. Its significance lies in the need to validate the graphical method as a consistent generalization of the well-established classical risk assessment, which is based on expert-defined impact and probability specifications. To establish and test consistency between classical and graphical risk specifications, the latter requires appropriate aggregation methods to synthesize risk assessments from individual expert judgments—an independent challenge in itself. Therefore, various aggregation methods are introduced and tested using a case study in the field of critical infrastructure, based on expert interviews from multiple specialized domains. The collected data enables both qualitative and statistical analysis. The Kolmogorov-Smirnov and Wasserstein tests were employed to quantify differences between the methods, while overall significance was assessed using Fisher’s method. This study underscores the importance of integrating uncertainties into risk assessments and provides insights into the effectiveness and applicability of different aggregation methods.

Annual and sub-seasonal dynamics of a rapidly eroding permafrost coastline along the Beaufort Sea in northern Alaska

Mechanism of DNA degradation by CBASS Cap5 endonuclease immune effector

The impact of agricultural green finance on the level of agricultural green development

Agricultural green finance is an important means to boost the development of agricultural green. Based on the panel data of 30 provinces in China from 2011 to 2021, this paper theoretically discusses the impact of agricultural green finance on the level of agricultural green development and conducts an empirical study by using the two-way fixed effect model and panel threshold model. The empirical findings show that agricultural green finance can significantly improve the level of agricultural green development mainly through facilitating the level of agricultural green technology innovation. The promoting effect of agricultural green finance on the level of agricultural green development is remarkable in the eastern region and non-major grain-producing areas, but not obvious in the central and western regions and major grain-producing areas. Indeed, the impact has a single threshold effect with environmental regulation as the threshold. Therefore, efforts should be made to improve the level of agricultural green development from the following aspects: vigorously developing agricultural green finance; promoting agricultural green technology innovation; strictly implementing environmental protection policies; and designing appropriate environmental regulation zones.

Resilience assessment of urban connected infrastructure networks

Bio-inspired multifunctional disruptors of calcium oxalate crystallization

Simulated larvae dispersion of the invasive sun-coral (Tubastrea spp.) along Rio de Janeiro’s coast: The role of submesoscale filaments on offshore transport and connectivity

The spread of invasive species in marine ecosystems is a growing global concern, particularly in regions with high economic and ecological importance. Sun corals (Tubastraea spp.) are native scleractinians from the Pacific Ocean that have spread along most of the Brazilian coast. This invasive species initially established populations in Rio de Janeiro state, SE Brazil, reaching high levels of abundance. Although the ecological aspects and impacts caused by this organism have been studied in detail, the natural mechanisms that drive its dispersal have attracted little attention. In this research, we focus on the coastal dispersion of sun coral larvae between Cabo de São Tomé and Ilha Grande Bay, and the offshore transport of sun coral larvae, investigating how submesoscale oceanographic features such as filaments, fronts and eddies influence connectivity among different sites. A high-resolution numerical model was used to simulate the coastal dynamics, incorporating the influence of the Brazil Current, wind-driven circulation, and submesoscale structures. Larval dispersal was examined under different wind scenarios, including northeasterly winds that drive southward currents and enhance offshore transport via submesoscale filaments. Results show that submesoscale features, particularly filaments emerged from upwelling regions, play a significant role on sun coral larvae dispersion. These features act as pathways that connect larvae from coastal to offshore oil exploration areas, highlighting the importance of both natural and anthropogenic processes for the dispersal of this invasive species. This research provides critical insights into the mechanisms governing the spread of invasive marine species, emphasizing the need for integrated coastal management strategies. Understanding how physical processes drive larval transport is essential for developing targeted control measures to mitigate the impact of invasive species like sun coral on native ecosystems and local economies. Furthermore, the study underscores the importance of monitoring both natural and anthropogenic influences on marine bioinvasions, particularly in regions with significant offshore industrial activities.

Enhancing pancreatic cancer detection in CT images through secretary wolf bird optimization and deep learning

Stimulus-specific and adaptive value representations in the basolateral amygdala in male mice

Adaptive density peak clustering based on Delaunay graph

Clustering is a fundamental tool in data mining, widely used in various fields such as image segmentation, data science, pattern recognition, and bioinformatics. Density Peak Clustering (DPC) is a density-based method that identifies clusters by calculating the local density of data points and selecting cluster centers based on these densities. However, DPC has several limitations. First, it requires a cutoff distance to calculate local density, and this parameter varies across datasets, which requires manual tuning and affects the algorithm’s performance. Second, the number of cluster centers must be manually specified, as the algorithm cannot automatically determine the optimal number of clusters, making the algorithm dependent on human intervention. To address these issues, we propose an adaptive Density Peak Clustering (DPC) method, which automatically adjusts parameters like cutoff distance and the number of clusters, based on the Delaunay graph. This approach uses the Delaunay graph to calculate the connectivity between data points and prunes the points based on these connections, automatically determining the number of cluster centers. Additionally, by optimizing clustering indices, the algorithm automatically adjusts its parameters, enabling clustering without any manual input. Experimental results on both synthetic and real-world datasets demonstrate that the proposed algorithm outperforms similar methods in terms of both efficiency and clustering accuracy.

Effect of preoperative rapamycin supplementation on perioperative clinical frailty and cognitive performance in a murine model undergoing anesthesia and surgery

Investigating the sources of variable impact of pathogenic variants in monogenic metabolic conditions

Abstract Over three percent of people carry a dominant pathogenic variant, yet only a fraction of carriers develop disease. Disease phenotypes from carriers of variants in the same gene range from mild to severe. Here, we investigate underlying mechanisms for this heterogeneity: variable variant effect sizes, carrier polygenic backgrounds, and modulation of carrier effect by genetic background (marginal epistasis). We leveraged exomes and clinical phenotypes from the UK Biobank and the Mt. Sinai Bio Me Biobank to identify carriers of pathogenic variants affecting cardiometabolic traits. We employed recently developed methods to study these cohorts, observing strong statistical support and clinical translational potential for all three mechanisms of variable carrier penetrance and disease severity. For example, scores from our recent model of variant pathogenicity were tightly correlated with phenotype amongst clinical variant carriers, they predicted effects of variants of unknown significance, and they distinguished gain- from loss-of-function variants. We also found that polygenic scores modify phenotypes amongst pathogenic carriers and that genetic background additionally alters the effects of pathogenic variants through interactions.