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Objectives Hippocampal characterization is one of the most significant hallmarks of Alzheimer’s disease (AD); rather, the single-level feature is insufficient. A comprehensive hippocampal characterization is pivotal for developing a well-performing biomarker for AD. To verify whether a comprehensive characterization of hippocampal features of gray matter volume, segmentation probability, and radiomics features could better distinguish AD from normal control (NC), and to investigate whether the classification decision score could serve as a robust and individualized brain signature. Methods A total of 3238 participants’ structural MRI from four independent databases were employed to conduct a 3D residual attention network (3DRA-Net) to classify NC, mild cognitive impairment (MCI), and AD. The generalization was validated under inter-database cross-validation. The neurobiological basis of the classification decision score as a neuroimaging biomarker was systematically investigated by association with clinical profiles, as well as longitudinal trajectory analysis to reveal AD progression. All image analyses were performed only upon the single modality of T1-weighted MRI. Results Our study exhibited an outstanding performance (ACC = 91.6%, AUC = 0.95) of the comprehensive characterization of hippocampal features in distinguishing AD ( n  = 282) from NC ( n  = 603) in Alzheimer’s Disease Neuroimaging Initiative cohort, and ACC = 89.2% and AUC = 0.93 under external validation. More importantly, the constructed score was significantly correlated with clinical profiles ( p  < 0.05), and dynamically altered over the AD longitudinal progression, provided compelling evidence of a solid neurobiological basis. Conclusions This systemic study highlights the potential of the comprehensive characterization of hippocampal features to provide an individualized, generalizable, and biologically plausible neuroimaging biomarker for early detection of AD. Key Points • The comprehensive characterization of hippocampal features exhibited ACC = 91.6% (AUC = 0.95) in classifying AD from NC under intra-database cross-validation, and ACC = 89.2% (AUC = 0.93) in external validation. • The constructed classification score was significantly associated with clinical profiles, and dynamically altered over the AD longitudinal progression, which highlighted its potential of being an individualized, generalizable, and biologically plausible neuroimaging biomarker for early detection of AD.

Tobacco mosaic virus (TMV) is extremely pathogenic and resistant to stress There are great needs to develop methods to reduce the virus in the environment and induce plant immunity simultaneously. Here, we report a multifunctional nano-protectant to reduce the virus in the environment and induce plant immunity simultaneously. The star polycation (SPc) nanocarrier can act as an active ingredient to interact with virus coat protein via electrostatic interaction, which reduces the proportion of TMV particles to 2.9% and leads to a reduction of the amount of virus in the environment by half. SPc can act as an adjuvant to spontaneously assemble with an immune inducer lentinan (LNT) through hydrogen bonding into nanoscale (142 nm diameter) LNT/SPc complex, which improves the physicochemical property of LNT for better wetting performance on leaves and cellular uptake, and further activates plant immune responses. Finally, the LNT/SPc complex displays preventive and curative effects on TMV disease, reducing TMV-GFP relative expression by 26% in the laboratory and achieving 82% control efficacy in the field We hope the strategy reported here would be useful for control of crop virus disease. Tobacco mosaic virus (TMV) infects a wide range of plants and cause tremendous economic loss. Here, the authors assemble lentinan, an immune inducer with antiviral activity, with the nanocarrier star polycation through hydrogen bonding and show its effectiveness in controlling TMV in field conditions.

Based on deep mediatization theory and artificial intelligence (AI) technology, this study explores the effective improvement of museums’ social media communication by applying Convolutional Neural Network (CNN) technology. Firstly, the social media content from four different museums is collected, a dataset containing tens of thousands of images is constructed, and a CNN-based model is designed for automatic identification and classification of image content. The model is trained and tested through a series of experiments, evaluating its performance in enhancing museums’ social media communication. Experimental results indicate that the CNN model significantly enhances user participation, access rates, retention rates, and sharing rates of content. Specifically, user participation increased from 15 to 25%, reflecting a 66.7% rise. Content coverage increased from 20 to 35%, showing a 75% increase. User retention rate rose from 10 to 20%, indicating a 100% increase. Content sharing rate increased from 5 to 15%, reflecting a 200% rise. Additionally, the study discusses the model’s performance across various museum types, batch sizes, and learning rate settings, verifying its robustness and wide applicability.

The importance of mental health is increasingly emphasized in modern society. The assessment of mental health qualities among college and university students as the future workforce holds significant significance. Therefore, this study, aiming to streamline the process of writing quality evaluations and enhance the fairness of assessment comments, explores the use of Generative Adversarial Network (GAN) technology in deep learning to evaluate the mental health qualities of college and university students through the unique avenue of sports. Firstly, GAN and Sequence Generative Adversarial Network (SeqGAN) models are introduced. Secondly, GAN is employed to construct a model for generating evaluation texts, encompassing the construction of a generator and discriminator, along with the introduction of a reward function. Finally, the constructed model is utilized to train on evaluation texts related to the mental health qualities of college and university students engaged in sports, validating the effectiveness of the model. The results indicate: (1) The pre-training of the generator in the constructed text generation model stabilizes after the 10th epoch. In contrast, the pre-training of the discriminator gradually stabilizes after the 35th epoch, demonstrating overall good training effectiveness. (2) When the generator’s update speed surpasses that of the discriminator, the model’s loss does not converge. However, with a reduction in the ratio of rounds between the two, there is a noticeable improvement in the convergence of the model. (3) The mean score of adaptability quality is the highest among the four indicators, suggesting a strong correlation between comment generation and adaptability quality. The results validate the effectiveness of the proposed text generation model in semantic control. This study aims to advance the level of mental health education among college and university students in the sports domain, providing theoretical references for enhancing the effectiveness of quality education assessments in other subjects as well.

•Self-supervised deep learning computes E-fields induced by TMS.•E-fields are computed by solving the governing PDE directly.•Self-supervised deep learning obtains E-fields with high accuracy and efficiency. Electric fields (E-fields) induced by transcranial magnetic stimulation (TMS) can be modeled using partial differential equations (PDEs). Using state-of-the-art finite-element methods (FEM), it often takes tens of seconds to solve the PDEs for computing a high-resolution E-field, hampering the wide application of the E-field modeling in practice and research. To improve the E-field modeling's computational efficiency, we developed a self-supervised deep learning (DL) method to compute precise TMS E-fields. Given a head model and the primary E-field generated by TMS coils, a DL model was built to generate a E-field by minimizing a loss function that measures how well the generated E-field fits the governing PDE. The DL model was trained in a self-supervised manner, which does not require any external supervision. We evaluated the DL model using both a simulated sphere head model and realistic head models of 125 individuals and compared the accuracy and computational speed of the DL model with a state-of-the-art FEM. In realistic head models, the DL model obtained accurate E-fields that were significantly correlated with the FEM solutions. The DL model could obtain precise E-fields within seconds for whole head models at a high spatial resolution, faster than the FEM. The DL model built for the simulated sphere head model also obtained an accurate E-field whose average difference from the analytical E-fields was 0.0054, comparable to the FEM solution. These results demonstrated that the self-supervised DL method could obtain precise E-fields comparable to the FEM solutions with improved computational speed. [Display omitted]

The ternary composite MgO@ZnO@BC was synthesized and characterized for the adsorption of Cu2+, Pb2+ heavy metal ions from wastewater. The results show that the addition of the MgO@ZnO@BC composite results in higher adsorption properties for Cu2+ and Pb2+, with a molar ratio of 5% 0.1 g, and maximum adsorption capacity (50.63 mg/g for Cu2+ and 61.46 mg/g for Pb2+). The Langmuir adsorption isotherm of the adsorption complex and the kinetics of adsorption are secondary kinetics. The adsorption of Cu2+ and Pb2+ was mainly chemisorption, accompanied by physical adsorption. This adsorption method fully conforms to the concepts of clean production and efficient waste utilization, providing a reference for the removal of heavy metal ions from wastewater and waste recycling using ternary composite materials.

Lateritic soils, also known as red soils, are prone to actions of external environment such as wetting and drying processes and their cycling. However, in the practice of pavement design in red soil regions, there lacks experimental studies and analytical approaches to reveal and predict the evolution of the hydromechanical characteristics of red soils under complex environmental actions. This paper investigates the variation in the hydrostructural and dynamic characteristics of a compacted subgrade red clay collected from Nanning, Guangxi, China, before and after wetting-drying cycles. The pore structure and soil-water characteristics of the red clay before and after ten wetting-drying cycles were determined to reveal the influences of moisture fluctuation history. Besides, cyclic triaxial tests were performed to determine the resilient modulus ( M R ) and permanent strain ( ε p ) of the red clay and reveal the influences of external stress, moisture content w , suction s , and WD cycles. It is found that (i) compacted red clay presents typical dual porosity with distinct inter-aggregate and intra-aggregate pores. Such pore structure results in bimodal soil water retention curves (SWRCs) of the red clay; (ii) upon wetting-drying cycles, the intra-aggregate pores shrink while the inter-aggregate pores swell. Besides, the global pore space (i.e., the overall void ratio) increases after WD cycles. This results in the elimination of the SWRC’s bimodal characteristics and the reduction in the clay’s water retention capacity in the low suction range and scale of shrinkage upon drying; (iii) the ε p and M R vary non-linearly with σ d , w , and s . Their relationships to the external stress and soil moisture change remarkably after WD cycles. A simple model was adopted to describe the variations of the ε p and M R with w and s , which has achieved close agreements with the experimental measurements; (iv) the ε p and M R of the tested red clay show a unique non-linear relationship regardless of the influences of σ d , w , s , and WD cycles, which highlights possible intrinsic relationships between the elastic and plastic behaviors of compacted subgrade soils.

Individual differences in cognition during childhood are associated with important social, physical, and mental health outcomes in adolescence and adulthood. Given that cortical surface arealization during development reflects the brain’s functional prioritization, quantifying variation in the topography of functional brain networks across the developing cortex may provide insight regarding individual differences in cognition. We test this idea by defining personalized functional networks (PFNs) that account for interindividual heterogeneity in functional brain network topography in 9–10 year olds from the Adolescent Brain Cognitive Development℠ Study. Across matched discovery ( n  = 3525) and replication ( n  = 3447) samples, the total cortical representation of fronto-parietal PFNs positively correlates with general cognition. Cross-validated ridge regressions trained on PFN topography predict cognition in unseen data across domains, with prediction accuracy increasing along the cortex’s sensorimotor-association organizational axis. These results establish that functional network topography heterogeneity is associated with individual differences in cognition before the critical transition into adolescence. Individual differences in cognitive abilities during childhood are associated with important outcomes in adolescence. Here, the authors show associations between youth cognition and individual-specific patterns of cortical brain network organization.

Motivated by the call of the International Maritime Organization to meet the emission targets of 2030, this study considers two important practical aspects of quay crane scheduling: efficiency and energy consumption. More precisely, we introduce the bi-objective quay crane scheduling problem where the objective is to minimize the vessel’s completion time and the crane’s energy consumption. This is done by formulating a bi-objective mixed-integer programming model. A branch-and-bound algorithm was developed as the exact solution approach to find the full set of Pareto-optimal solutions. We consider (i) various lower bounds for both objectives, (ii) specific upper bounds, (iii) additional branching criteria, and (iv) fathoming criteria to detect Pareto-optimal solutions. Numerical experiments on benchmark instances show that the branch-and-bound algorithm can efficiently solve small- and medium-sized problems.

The brain is organized into networks at multiple resolutions, or scales, yet studies of functional network development typically focus on a single scale. Here, we derive personalized functional networks across 29 scales in a large sample of youths (n = 693, ages 8–23 years) to identify multi-scale patterns of network re-organization related to neurocognitive development. We found that developmental shifts in inter-network coupling reflect and strengthen a functional hierarchy of cortical organization. Furthermore, we observed that scale-dependent effects were present in lower-order, unimodal networks, but not higher-order, transmodal networks. Finally, we found that network maturation had clear behavioral relevance: the development of coupling in unimodal and transmodal networks are dissociably related to the emergence of executive function. These results suggest that the development of functional brain networks align with and refine a hierarchy linked to cognition. Studies of brain network development typically focus on a single scale. Here, the authors derived personalized functional networks across scales, and find that network development systematically adheres to and strengthens hierarchical cortical organization.