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Integrating dynamic capability theory and social exchange theory, this study reveals the impact of organizational resilience (OR) on firm performance (FP) from both short-term and long-term perspectives. The mediating roles of R&D innovation (RD) and employee turnover (ET), as well as the moderating role of artificial intelligence (AI), are also explored. Using a sample of Chinese A-share listed companies in the manufacturing industry from 2009 to 2022, this study demonstrates that OR contributes to both short-term performance (SP) and long-term performance (LP). RD serves as a negative mediator between OR and SP, while it plays a positive mediating role between OR and LP. ET, in turn, acts as a positive mediator between OR and SP/LP. Our results also suggest that AI positively moderates the relationship between OR and SP/LP. Additionally, the heterogeneity results indicate that the promotion effect of OR on SP is more significant in larger enterprises and low-competition industries. In contrast, the impact of OR on LP is more significant in smaller enterprises and high-competition industries. This study comprehensively describes the effects of OR on FP and explains why there are contradictory findings on the relationship between the two. It also provides practical guidance for manufacturing companies to promote SP and LP through OR in the age of AI.

The early detection and differential diagnosis of respiratory infections increase the chances for successful control of COVID-19 disease. The nucleic acid RT-PCR test is regarded as the current standard for molecular diagnosis. However, the maximal specificity confirmation target ORF1ab gene is considered to be less sensitive than other targets in clinical application. In addition, recent evidence indicated that the initial missed diagnosis of asymptomatic patients with SARS-CoV-2 and discharged patients with “re-examination positive” might be due to low viral load, and the ability of rapid mutation of SARS-CoV-2 also increases the rate of false-negative results. Moreover, the mixed sample nucleic acid detection is helpful in seeking out the early community transmission of SARS-CoV-2 rapidly, but the detection kit needs ultra-high detection sensitivity. Herein, the lowest detection concentration of different nucleic acid detection kits was evaluated and compared to provide direct evidence for the selection of kits for mixed sample detection or make recommendations for the selection of validation kit, which is of great significance for the prevention and control of the current epidemic and the discharge criteria of low viral load patients.

•Proposes a framework to assess the repeatability of ICA algorithms comprehensively.•Enhances ICA-DP for better site effect removal and signal preservation.•Repeatability analysis applicable to other matrix and tensor factorization methods.•Facilitates more reproducible decomposition in large-scale neuroimaging studies. Integrating multi-site magnetic resonance imaging (MRI) datasets enhances statistical power and generalizability in neuroimaging research but introduces systematic variability, known as site effects, that can obscure true biological signals. Independent component analysis (ICA)-based harmonization methods, such as the dual projection ICA (ICA-DP) model, aim to mitigate these effects while preserving meaningful signal. However, the repeatability of ICA decompositions across different runs and parameter settings remains a critical challenge, affecting the stability and reliability of site effects removal. Here, we propose a novel evaluation framework for ICA repeatability that jointly assesses spatial components, their mixing coefficients, and component energy—a theoretically grounded but often overlooked parameter based on back-projection. Using both simulated and real multi-site MRI datasets, we demonstrate that incorporating component energy into repeatability metrics provides a more robust and theoretically grounded assessment of ICA stability. We further revise the ICA-DP harmonization scheme by removing site-associated components comprehensively, resulting in improved preservation of biologically relevant signals. Our results establish the importance of repeatability analysis and support the proposed framework as a reliable tool for ICA-based harmonization in multi-site studies.

The clinical application of music therapy and research into its use and effectiveness are common in Western countries. The physiological role of this type of therapy is to stimulate the central nervous system through music, which may have a sedative, analgesic effect, and reduce negative emotions. Previous studies have confirmed that music can be effective for a range of psychological disorders, including post-stroke depression (PSD). There is, however, a lack of systematic evaluation of its effectiveness, and variability in sample size and in the quality of research has detracted from the persuasiveness of findings. Based on PRISMA 2020, articles on music therapy intervention in post-stroke depression were identified through the Web of Science, PubMed, EMBASE, CNKI, Weipu, and Wanfang databases. The retrieval time was taken from the establishment of the database to October 18, 2022. Two researchers conducted a stringent evaluation of the quality of the articles and extracted the data. They then used RevMan5.3 software for meta-analysis. Twenty articles were listed, involving 1625 patients. Meta-analysis results showed that music therapy could lower scores on the Hamilton Depression Rating Scale (HDRS/Ham-D), the National Institutes of Health stroke scale and self-rated depression scale for patients with PSD. Music therapy was also shown to improve the Barthel Index for Activities of Daily Living and treatment efficacy of PSD patients. However, music therapy did not reduce the incidence of adverse reactions in PSD patients. Music therapy has benefits in improving HDRS/Ham-D score and symptoms of PSD patients, and could be more widely applied. •Based on PRISMA 2020, articles on music therapy intervention in PSD were first identified through the Web of Science, PubMed, EMBASE, CNKI, Weipu, and Wanfang databases.•Music therapy could lower scores on the Hamilton Depression Rating Scale, the National Institutes of Health stroke scale and self-rated depression scale for patients with PSD. Music therapy was also shown to improve the Barthel Index for Activities of Daily Living and treatment efficacy of PSD patients.•Music therapy did not reduce the incidence of adverse reactions in PSD patients.

•Tensor ICA can decompose multi-echo EPI data in time, space, and echo time domains.•Distribution across TEs separate BOLD and non-BOLD components of tensor ICA.•Elimination of the noise-related components enhances quality and activation patterns. Multi-echo echo-planar imaging (ME-EPI) acquires images at multiple echo times (TEs), enabling the differentiation of BOLD and non-BOLD fluctuations through TE-dependent analysis of transverse relaxation time and initial intensity. Decomposing ME-EPI in tensor space is a promising approach to characterize the distribution of changes across TEs (TE patterns) directly and aid the classification of components by providing information from an additional domain. In this study, the tensorial extension of independent component analysis (tensor-ICA) is used to characterize the TE patterns of neural and non-neural components in ME-EPI data. With the constraints of independent spatial maps, an ME-EPI dataset was decomposed into spatial, temporal, and TE domains to understand the TE patterns of noise or signal-related independent components. Our analysis revealed three distinct groups of components based on their TE patterns. Motion-related and other non-BOLD origin components followed decreased TE patterns. While the long-TE-peak components showed a large overlay on grey matter and signal patterns, the components that peaked at short TEs reflected noise that may be related to the vascular system, respiration, or cardiac pulsation, amongst others. Accordingly, removing short-TE peak components as part of a denoising strategy significantly improved quality control metrics and revealed clearer, more interpretable activation patterns compared to non-denoised data. To our knowledge, this work is the first application of decomposing ME-EPI in a tensor way. Our findings demonstrate that tensor-ICA is efficient in decomposing ME-EPI and characterizing the neural and non-neural TE patterns aiding in classifying components which is important for denoising fMRI data.

•A DL-based harmonization framework was established with a traveling subject dataset.•Site and brain factors were learned by the proposed framework from gray matter volumes.•Better harmonization performance was achieved relative to that of statistics-based methods.•The proposed harmonization method offered flexible expandability for adding new sites. The accumulation of multisite large-sample MRI datasets collected during large brain research projects in the last decade has provided critical resources for understanding the neurobiological mechanisms underlying cognitive functions and brain disorders. However, the significant site effects observed in imaging data and their derived structural and functional features have prevented the derivation of consistent findings across multiple studies. The development of harmonization methods that can effectively eliminate complex site effects while maintaining biological characteristics in neuroimaging data has become a vital and urgent requirement for multisite imaging studies. Here, we propose a deep learning-based framework to harmonize imaging data obtained from pairs of sites, in which site factors and brain features can be disentangled and encoded. We trained the proposed framework with a publicly available traveling subject dataset from the Strategic Research Program for Brain Sciences (SRPBS) and harmonized the gray matter volume maps derived from eight source sites to a target site. The proposed framework significantly eliminated intersite differences in gray matter volumes. The embedded encoders successfully captured both the abstract textures of site factors and the concrete brain features. Moreover, the proposed framework exhibited outstanding performance relative to conventional statistical harmonization methods in terms of site effect removal, data distribution homogenization, and intrasubject similarity improvement. Finally, the proposed harmonization network provided fixable expandability, through which new sites could be linked to the target site via indirect schema without retraining the whole model. Together, the proposed method offers a powerful and interpretable deep learning-based harmonization framework for multisite neuroimaging data that can enhance reliability and reproducibility in multisite studies regarding brain development and brain disorders. [Display omitted]

Background Oral lichen planus (OLP), a common clinical oral disease, is associated with an increased risk of malignant transformation. The mechanism underlying the pathogenesis of OLP is unknown. Oral dysbacteriosis is reported to be one of the aetiological factors of OLP. Although Helicobacter pylori infection is associated with various oral diseases, the correlation between H. pylori infection and OLP is unclear. This study aimed to investigate the effect of H. pylori infection on OLP pathogenesis and oral microbiome composition in the Chinese population, which has a high incidence of H. pylori infection. Result In this study, saliva samples of 30 patients with OLP (OLP group) and 21 negative controls (NC group) were collected. H. pylori infection was detected using the carbon-13-labeled urea breath test (UBT). The saliva samples were divided into the following four groups based on the H. pylori status: H. pylori -positive OLP (OLP+), H. pylori -positive NC (NC+), H. pylori -negative OLP (OLP−), and H. pylori -negative NC (NC−). Oral microbiome compositions were significantly different between the OLP and NC groups and between the OLP− and OLP+ groups. Compared with those in the OLP− group, those in the OLP+ group had a higher incidence of erosive OLP and higher levels of salivary cytokines. In contrast, the oral microbiome composition and cytokine levels were not significantly different between the NC− and NC+ groups. Conclusions This is the first report to demonstrate that H. pylori infection is significantly correlated with the pathogenesis of erosive OLP.

The study of brain network interactions during naturalistic stimuli facilitates a deeper understanding of human brain function. To estimate large-scale brain networks evoked with naturalistic stimuli, a tensor component analysis (TCA) based framework was used to characterize shared spatio-temporal patterns across subjects in a purely data-driven manner. In this framework, a third-order tensor is constructed from the timeseries extracted from all brain regions from a given parcellation, for all participants, with modes of the tensor corresponding to spatial distribution, time series and participants. TCA then reveals spatially and temporally shared components, i.e., evoked networks with the naturalistic stimuli, their time courses of activity and subject loadings of each component. To enhance the reproducibility of the estimation with the adaptive TCA algorithm, a novel spectral clustering method, tensor spectral clustering, was proposed and applied to evaluate the stability of the TCA algorithm. We demonstrated the effectiveness of the proposed framework via simulations and real fMRI data collected during a motor task with a traditional fMRI study design. We also applied the proposed framework to fMRI data collected during passive movie watching to illustrate how reproducible brain networks are evoked by naturalistic movie viewing.

As a link between exercise and metabolism, irisin is assumed to be involved in increased total body energy expenditure, reduced body weight, and increased insulin sensitivity. Although our recent evidence supported the contribution of irisin to vascular endothelial cell (ECs) proliferation and apoptosis, further research of irisin involvement in the angiogenesis of ECs was not conclusive. In the current study, it was found that irisin promoted Human Umbilical Vein Endothelial Cell (HUVEC) angiogenesis via increasing migration and tube formation, and attenuated chemically-induced intersegmental vessel (ISV) angiogenic impairment in transgenic TG (fli1: GFP) zebrafish. It was further demonstrated that expression of matrix metalloproteinase (MMP) 2 and 9 were also up-regulated in endothelial cells. We also found that irisin activated extracellular signal-related kinase (ERK) signaling pathways. Inhibition of ERK signaling by using U0126 decreased the pro-migration and pro-angiogenic effect of irisin on HUVEC. Also, U0126 inhibited the elevated expression of MMP-2 and MMP-9 when they were treated with irisin. In summary, these findings provided direct evidence that irisin may play a pivotal role in maintaining endothelium homeostasis by promoting endothelial cell angiogenesis via the ERK signaling pathway.

Pooling magnetic resonance imaging (MRI) data across research studies, or utilizing shared data from imaging repositories, presents exceptional opportunities to advance and enhance reproducibility of neuroscience research. However, scanner confounds hinder pooling data collected on different scanners or across software and hardware upgrades on the same scanner, even when all acquisition protocols are harmonized. These confounds reduce power and can lead to spurious findings. Unfortunately, methods to address this problem are scant. In this study, we propose a novel denoising approach that implements a data-driven linked independent component analysis (LICA) to identify scanner-related effects for removal from multimodal MRI to denoise scanner effects. We utilized multi-study data to test our proposed method that were collected on a single 3T scanner, pre- and post-software and major hardware upgrades and using different acquisition parameters. Our proposed denoising method shows a greater reduction of scanner-related variance compared with standard GLM confound regression or ICA-based single-modality denoising. Although we did not test it here, for combining data across different scanners, LICA should prove even better at identifying scanner effects as between-scanner variability is generally much larger than within-scanner variability. Our method has great promise for denoising scanner effects in multi-study and in large-scale multi-site studies that may be confounded by scanner differences.