In this paper, we are concerned with a quasi-linear hyperbolic-parabolic system of persistence and endogenous chemotaxis modelling vasculogenesis. Under some suitable structural assumption on the ...pressure function, we first predict and derive the system admits a nonlinear diffusion wave in R driven by the damping effect. Then we show that the solution of the concerned system will locally and asymptotically converge to this nonlinear diffusion wave if the wave strength is small. By using the time-weighted energy estimates, we further prove that the convergence rate of the nonlinear diffusion wave is algebraic.
Background
The contribution of B‐cell subsets and T‐B cell interaction to the pathogenesis of allergic rhinitis (AR) and mechanisms of allergen immunotherapy (AIT) remain poorly understood. This ...study aimed to outline circulating B‐cell signature, the underlying mechanism, and its association with clinical response to AIT in patients with AR.
Methods
IgD/CD27 and CD24/CD38 core gating systems were used to determine frequencies and phenotypes of B cells. Correlations between B cells, T cells, antigen‐specific IgE, and disease severity in AR patients were investigated. Switched memory B cells were co‐cultured with type 2 follicular helper T (Tfh2) cells and follicular regulatory T (Tfr) cells. Associations between B‐cell subsets and clinical benefits of AIT were analyzed.
Results
Frequencies and absolute numbers of circulating memory B cells were increased in AR patients. CD23 expression on CD19+CD20+CD27+IgD− switched memory B cells was significantly enhanced and positively correlated with antigen‐specific IgE levels, symptom scores, and Tfh2/Tfr cell ratio in AR patients. Compared with those from healthy controls, Tfh2 cells from AR patients had a greater capacity to induce CD23 expression on switched memory B cells via IL‐4, which was unable to be sufficiently suppressed by AR‐associated Tfr cells with defective IL‐10 expression. CD23 expression on switched memory B cells was downregulated after 12‐month AIT, which positively associated with disease remission in AR patients.
Conclusion
T‐B cell interaction, bridged by CD23 expression particularly on switched memory B cells, may be involved in the disease pathogenesis and mechanism of AIT in patients with AR.
Circulating memory B cells are increased in AR patients. The enhanced expression of CD23 on switched memory B cells correlates with antigen‐specific IgE levels, symptom scores, and allergen immunotherapy efficacy in AR patients. Tfh2 cells from AR patients have a greater capacity to induce CD23 expression on switched memory B cells via IL‐4, which is unable to be sufficiently suppressed by AR‐associated Tfr cells with defective IL‐10 expression.
Abbreviations: AIT, allergen immunotherapy; AR, allergic rhinitis; HC, healthy controls; NSM, nonswitched memory; SM, switched memory; Tfh2, type 2 follicular helper T cells; Tfr, follicular regulatory T cell.
Facing the increasing worldwide prevalence of mental disorders, the symptom-based diagnostic criteria struggle to address the urgent public health concern due to the global shortfall in ...well-qualified professionals. Thanks to the recent advances in neuroimaging techniques, functional magnetic resonance imaging (fMRI) has surfaced as a new solution to characterize neuropathological biomarkers for detecting functional connectivity (FC) anomalies in mental disorders. However, the existing computer-aided diagnosis models for fMRI analysis suffer from unstable performance on large datasets. To address this issue, we propose an efficient multitask learning (MTL) framework for joint diagnosis of multiple mental disorders using resting-state fMRI data. A novel multiobjective evolutionary clustering algorithm is presented to group regions of interests (ROIs) into different clusters for FC pattern analysis. On the optimal clustering solution, the multicluster multigate mixture-of-expert model is used for the final classification by capturing the highly consistent feature patterns among related diagnostic tasks. Extensive simulation experiments demonstrate that the performance of the proposed framework is superior to that of the other state-of-the-art methods. Moreover, the potential for practical application of the framework is also validated in terms of limited computational resources, real-time analysis, and insufficient training data. The proposed model can identify the remarkable interpretative biomarkers associated with specific mental disorders for clinical interpretation analysis.
Objective: Deep learning (DL) techniques have been introduced to assist doctors in the interpretation of medical images by detecting image-derived phenotype abnormality. Yet the privacy-preserving ...policy of medical images disables the effective training of DL model using sufficiently large datasets. As a decentralized computing paradigm to address this issue, federated learning (FL) allows the training process to occur in individual institutions with local datasets, and then aggregates the resultant weights without risk of privacy leakage. Methods: We propose an effective federated multi-task learning (MTL) framework to jointly identify multiple related mental disorders based on functional magnetic resonance imaging data. A federated contrastive learning-based feature extractor is developed to extract high-level features across client models. To ease the optimization conflicts of updating shared parameters in MTL, we present a federated multi-gate mixture of expert classifier for the joint classification. The proposed framework also provides practical modules, including personalized model learning, privacy protection, and federated biomarker interpretation. Results: On real-world datasets, the proposed framework achieves robust diagnosis accuracies of 69.48 <inline-formula><tex-math notation="LaTeX">\pm</tex-math></inline-formula> 1.6%, 71.44 <inline-formula><tex-math notation="LaTeX">\pm</tex-math></inline-formula> 3.2%, and 83.29 <inline-formula><tex-math notation="LaTeX">\pm</tex-math></inline-formula> 3.2% in autism spectrum disorder, attention deficit/hyperactivity disorder, and schizophrenia, respectively. Conclusion: The proposed framework can effectively ease the domain shift between clients via federated MTL. Significance: The current work provides insights into exploiting the advantageous knowledge shared in related mental disorders for improving the generalization capability of computer-aided detection approaches.
•China's workweek reduction policy implemented in 1995 reduced state employees’ working hours from 48 to 40 h per week.•The health of Chinese state employees was negatively impacted by the policy, ...even though their income and nutrition intake remained unaffected.•The policy led to a significant increase in alcohol consumption, especially among male workers, which may partially explain the negative health effects.
This paper examines the impact of China's Workweek Reduction Policy, implemented in 1995, on state employees' health status. The study draws on data from the China Health and Nutrition Survey conducted between 1991 and 2000. Using a difference-in-differences strategy, the research compares the health outcomes of state and non-state employees before and after the policy's implementation. The results reveal a surprising finding: reducing working hours from 48 to 40 had an adverse effect on the health of Chinese state employees despite the policy not affecting their income or nutrition intake. Specifically, the policy resulted in a substantial increase in alcohol consumption, particularly among males. These insights highlight the potential unintended consequences of workweek reduction policies and have important implications for policymakers considering similar measures.
The 2007 discovery of fragmentary human remains (two molars and an anterior mandible) at Zhirendong (Zhiren Cave) in South China provides insight in the processes involved in the establishment of ...modern humans in eastern Eurasia. The human remains are securely dated by U-series on overlying flowstones and a rich associated faunal sample to the initial Late Pleistocene, >100 kya. As such, they are the oldest modern human fossils in East Asia and predate by >60,000 y the oldest previously known modern human remains in the region. The Zhiren 3 mandible in particular presents derived modern human anterior symphyseal morphology, with a projecting tuber symphyseos, distinct mental fossae, modest lateral tubercles, and a vertical symphysis; it is separate from any known late archaic human mandible. However, it also exhibits a lingual symphyseal morphology and corpus robustness that place it close to later Pleistocene archaic humans. The age and morphology of the Zhiren Cave human remains support a modern human emergence scenario for East Asia involving dispersal with assimilation or populational continuity with gene flow. It also places the Late Pleistocene Asian emergence of modern humans in a pre-Upper Paleolithic context and raises issues concerning the long-term Late Pleistocene coexistence of late archaic and early modern humans across Eurasia.
In nature, grasses simultaneously establish multiple symbiotic associations with endophytic fungi and arbuscular mycorrhizal fungi (AMF). The effect of these multiple interactions on competitive ...interactions between plants remains poorly understood.
In this study, we tested whether endophytes and AMF (Glomus mosseae or Glomus etunicatum) alter plant competition between a subordinate plant species that associates with both symbionts (Achnatherum sibiricum) and a dominant plant species, Stipa grandis, that only associates with one symbiont (AMF). And we hypothesized that endophytes can facilitate the coexistence of the subordinate plant species (A. sibiricum) and the dominant plant species (S. grandis).
The results demonstrated that endophyte infection significantly enhanced the competitive ability of the subordinate plant species compared to the dominant plant species. The effects of AMF on plant competition were variable and depended on the identity of the AMF species. Glomus etunicatum gave A. sibiricum plants a higher competitive ability, while G. mosseae gave S. grandis a higher competitive ability. Simultaneous infections of both endophytes and AMF in A. sibiricum also altered the competitive relationships with S. grandis.
In conclusion, these results suggest that endophytic fungi can facilitate the coexistence of a subordinate plant species with a dominant plant species. Moreover, endophytes could not only affect the competitive ability of the host plant directly but also indirectly by interacting with different AMF to change the growth of competing plant species.
A plain language summary is available for this article.
Plain Language Summary
The high prevalence of mental disorders gradually poses a huge pressure on the public healthcare services. Deep learning-based computer-aided diagnosis (CAD) has emerged to relieve the tension in ...healthcare institutions by detecting abnormal neuroimaging-derived phenotypes. However, training deep learning models relies on sufficient annotated datasets, which can be costly and laborious. Semi-supervised learning (SSL) and transfer learning (TL) can mitigate this challenge by leveraging unlabeled data within the same institution and advantageous information from source domain, respectively. This work is the first attempt to propose an effective semi-supervised transfer learning (SSTL) framework dubbed S3TL for CAD of mental disorders on fMRI data. Within S3TL, a secure cross-domain feature alignment method is developed to generate target-related source model in SSL. Subsequently, we propose an enhanced dual-stage pseudo-labeling approach to assign pseudo-labels for unlabeled samples in target domain. Finally, an advantageous knowledge transfer method is conducted to improve the generalization capability of the target model. Comprehensive experimental results demonstrate that S3TL achieves competitive accuracies of 69.14%, 69.65%, and 72.62% on ABIDE-I, ABIDE-II, and ADHD-200 datasets, respectively. Furthermore, the simulation experiments also demonstrate the application potential of S3TL through model interpretation analysis and federated learning extension.
The globally rising prevalence of mental disorders leads to shortfalls in timely diagnosis and therapy to reduce patients' suffering. Facing such an urgent public health problem, professional efforts ...based on symptom criteria are seriously overstretched. Recently, the successful applications of computer-aided diagnosis approaches have provided timely opportunities to relieve the tension in healthcare services. Particularly, multimodal representation learning gains increasing attention thanks to the high temporal and spatial resolution information extracted from neuroimaging fusion. In this work, we propose an efficient multimodality fusion framework to identify multiple mental disorders based on the combination of functional and structural magnetic resonance imaging. A multioutput conditional generative adversarial network (GAN) is developed to address the scarcity of multimodal data for augmentation. Based on the augmented training data, the multiheaded gating fusion model is proposed for classification by extracting the complementary features across different modalities. The experiments demonstrate that the proposed model can achieve robust accuracies of <inline-formula> <tex-math notation="LaTeX">75.1~\pm ~1.5 </tex-math></inline-formula>%, <inline-formula> <tex-math notation="LaTeX">72.9~\pm ~1.1 </tex-math></inline-formula>%, and <inline-formula> <tex-math notation="LaTeX">87.2~\pm ~1.5 </tex-math></inline-formula>% for autism spectrum disorder (ASD), attention deficit/hyperactivity disorder, and schizophrenia, respectively. In addition, the interpretability of our model is expected to enable the identification of remarkable neuropathology diagnostic biomarkers, leading to well-informed therapeutic decisions.
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