Association and prediction of Life's Essential 8 score, genetic susceptibility with MCI, dementia, and MRI indices: A prospective cohort study.

BACKGROUND: To examine the associations of Life's Essential 8 (LE8) and its predictive performance with mild cognitive impairment (MCI), dementia and brain MRI indices. METHODS: We used cohort data from UK Biobank. LE8 was categorized into low (<50 score), moderate (50-79 score), and high (≥80 score) levels. Cox regression models considering death as a competing risk were used to estimate the hazard ratios (HRs) and 95%CI on the association between LE8 and MCI and dementia. Multivariable linear regression models were used to analyze LE8 every 10-score increase and brain MRI indices. Area under the curve (AUC) was used to measure the predictive performances of LE8. RESULTS: We included 126,785 participants with a mean (SD) age of 56.0 (8.0) years and 53.5 % were female. The median follow-up was 13.0 years. Compared to individuals with a low LE8 score, those with a high LE8 score were associated with decreased risk of MCI (0.49, 95%CI: 0.40-0.62), all-cause dementia (0.60, 0.44-0.80), vascular dementia (VD, 0.44, 0.21-0.94), and non-Alzheimer non-vascular dementia (NAVD, 0.55, 0.35-0.84). High LE8 score was associated with increased total brain volume, hippocampus volume, grey matter volume, and grey matter in hippocampus volume (p all ≤0.001). LE8 combined age and sex had good performance for predicting all-cause dementia (AUC: 84.1 %), AD (85.4 %), VD (87.6 %), NAVD (81.4 %), and MCI (75.3 %). LIMITATIONS: Our findings only reflect the characteristics of UKB participants. CONCLUSIONS: High LE8 score was associated with reduced risk of MCI and dementia. It was also linked to brain MRI indices. LE8 score had good predicting performance for future risk of MCI and dementia.

Direct interaction of the molecular chaperone GRP78/BiP with the Newcastle disease virus hemagglutinin-neuraminidase protein plays a vital role in viral attachment to and infection of culture cells.

Introduction: Glucose Regulated Proteins/Binding protein (GRP78/Bip), a representative molecular chaperone, effectively influences and actively participates in the replication processes of many viruses. Little is known, however, about the functional involvement of GRP78 in the replication of Newcastle disease virus (NDV) and the underlying mechanisms. Methods: The method of this study are to establish protein interactomes between host cell proteins and the NDV Hemagglutinin-neuraminidase (HN) protein, and to systematically investigate the regulatory role of the GRP78-HN protein interaction during the NDV replication cycle. Results: Our study revealed that GRP78 is upregulated during NDV infection, and its direct interaction with HN is mediated by the N-terminal 326 amino acid region. Knockdown of GRP78 by small interfering RNAs (siRNAs) significantly suppressed NDV infection and replication. Conversely, overexpression of GRP78 resulted in a significant increase in NDV replication, demonstrating its role as a positive regulator in the NDV replication cycle. We further showed that the direct interaction between GRP78 and HN protein enhanced the attachment of NDV to cells, and masking of GRP78 expressed on the cell surface with specific polyclonal antibodies (pAbs) inhibited NDV attachment and replication. Discussion: These findings highlight the essential role of GRP78 in the adsorption stage during the NDV infection cycle, and, importantly, identify the critical domain required for GRP78-HN interaction, providing novel insights into the molecular mechanisms involved in NDV replication and infection.

Healthy lifestyle and all-cause and cause-specific dementia in individuals with type 2 diabetes and the roles of diabetes duration and insulin use in UK Biobank cohort.

AIMS: To examine the effect of a healthy lifestyle score derived from seven lifestyle factors recommended by the diabetes management guidelines on all-cause and cause-specific dementia in individuals with type 2 diabetes mellitus (T2DM), and how diabetes duration and insulin use status modify their association. MATERIALS AND METHODS: This study analysed data of 459 840 participants from the UK Biobank. We used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals for the association of an overall healthy lifestyle score with all-cause and cause-specific dementia of Alzheimer's disease, vascular dementia and non-Alzheimer non-vascular dementia. RESULTS: Using diabetes-free participants who scored 5-7 as the reference group, in diabetes-free participants, we observed higher healthy lifestyle score was related to lower risk of all-cause and cause-specific dementia. However, in people with T2DM, those scored 2-3, 4 and 5-7 all had around the two-time risk of all-cause dementia (HR: 2.20-2.36), while those scored 0-1 had over a three-time risk (HR: 3.14, 95% confidence interval 2.34-4.21). A dose-response trend was observed with vascular dementia (each 2-point increase: 0.75, 0.61-0.93) and no significant association with Alzheimer's disease (0.95, 0.77-1.16). The reduced risk of all-cause and cause-specific dementia with higher lifestyle score was observed in patients with a diabetes duration less than 10 years, or in patients with no insulin use. CONCLUSION: In people with T2DM, higher healthy lifestyle score was associated with lower risk of all-cause dementia. Diabetes duration and insulin use moderated the association between healthy lifestyle score and risk of dementia.

Age at menopause and all-cause and cause-specific dementia: a prospective analysis of the UK Biobank cohort.

STUDY QUESTION: Are there associations between natural or surgical menopause and incident dementia by age at menopause? SUMMARY ANSWER: Compared to age at menopause of 46-50 years, earlier natural menopause (≤40 and 41-45 years) was related to higher risk of all-cause dementia, while a U-shape relationship was observed between age at surgical menopause and risk of dementia. WHAT IS KNOWN ALREADY: Menopause marks the end of female reproductive period. Age at menopause reflects the length of exposure to endogenous estrogen. Evidence on the association between age at natural, surgical menopause, and risk of dementia has been inconsistent. STUDY DESIGN, SIZE, DURATION: A population-based cohort study involving 160 080 women who participated in the UK Biobank study. PARTICIPANTS/MATERIALS, SETTING, METHODS: Women with no dementia at baseline, and had no missing data on key exposure variables and covariates were included. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% CIs on the association of categorical menopause age with incident all-cause dementia, Alzheimer's disease (AD) and vascular dementia (VD). Restricted cubic splines were used to model the non-linear relationship between continuous age at natural, surgical menopause, and risk of dementia. In addition, we analyzed the interaction effect of ever-used menopausal hormone therapy (MHT) at baseline, income level, leisure activities, and age at menopause on risk of dementia. MAIN RESULTS AND THE ROLE OF CHANCE: Compared to women with age at menopause of 46-50 years, women with earlier natural menopause younger than 40 years (1.36, 1.01-1.83) and 41-45 years (1.19, 1.03-1.39) had a higher risk of all-cause dementia, while late natural menopause >55 years was linked to lower risk of dementia (0.83, 0.71-0.98). Compared to natural menopause, surgical menopause was associated with 10% higher risk of dementia (1.10, 0.98-1.24). A U-shape relationship was observed between surgical menopause and risk of dementia. Women with surgical menopause before age 40 years (1.94, 1.38-2.73) and after age 55 years (1.65, 1.21-2.24) were both linked to increased risk of all-cause dementia. Women with early natural menopause without ever taking MHT at baseline had an increased risk of AD. Also, in each categorized age at the menopause level, higher income level or higher number of leisure activities was linked to a lowers risk of dementia. LIMITATIONS, REASONS FOR CAUTION: Menopausal age was based on women's self-report, which might cause recall bias. WIDER IMPLICATION OF THE FINDINGS: Women who experienced natural menopause or had surgical menopause at an earlier age need close monitoring and engagement for preventive health measures to delay the development of dementia. STUDY FUNDING/COMPETING INTERESTS: This work was supported by the Start-up Foundation for Scientific Research in Shandong University (202099000066), Science Fund Program for Excellent Young Scholars of Shandong Provence (Overseas) (2022HWYQ-030), and the National Natural Science Foundation of China (82273702). There are no competing interests. TRIAL REGISTRATION NUMBER: N/A.

Improved the heterodimer protein complex prediction with protein language models.

AlphaFold-Multimer has greatly improved the protein complex structure prediction, but its accuracy also depends on the quality of the multiple sequence alignment (MSA) formed by the interacting homologs (i.e. interologs) of the complex under prediction. Here we propose a novel method, ESMPair, that can identify interologs of a complex using protein language models. We show that ESMPair can generate better interologs than the default MSA generation method in AlphaFold-Multimer. Our method results in better complex structure prediction than AlphaFold-Multimer by a large margin (+10.7% in terms of the Top-5 best DockQ), especially when the predicted complex structures have low confidence. We further show that by combining several MSA generation methods, we may yield even better complex structure prediction accuracy than Alphafold-Multimer (+22% in terms of the Top-5 best DockQ). By systematically analyzing the impact factors of our algorithm we find that the diversity of MSA of interologs significantly affects the prediction accuracy. Moreover, we show that ESMPair performs particularly well on complexes in eucaryotes.

Leaf Color Classification and Expression Analysis of Photosynthesis-Related Genes in Inbred Lines of Chinese Cabbage Displaying Minor Variations in Dark-Green Leaves.

The leaves of the Chinese cabbage which is most widely consumed come in a wide variety of colors. Leaves that are dark green can promote photosynthesis, effectively improving crop yield, and therefore hold important application and cultivation value. In this study, we selected nine inbred lines of Chinese cabbage displaying slight differences in leaf color, and graded the leaf color using the reflectance spectra. We clarified the differences in gene sequences and the protein structure of ferrochelatase 2 (BrFC2) among the nine inbred lines, and used qRT-PCR to analyze the expression differences of photosynthesis-related genes in inbred lines with minor variations in dark-green leaves. We found expression differences among the inbred lines of Chinese cabbage in photosynthesis-related genes involved in the porphyrin and chlorophyll metabolism, as well as in photosynthesis and photosynthesis-antenna protein pathway. Chlorophyll b content was significantly positively correlated with the expression of PsbQ, LHCA1_1 and LHCB6_1, while chlorophyll a content was significantly negatively correlated with the expression PsbQ, LHCA1_1 and LHCA1_2. Our results provide an empirical basis for the precise identification of candidate genes and a better understanding of the molecular mechanisms responsible for the production of dark-green leaves in Chinese cabbage.

Midlife Life's Simple 7, Psychosocial Health, and Physical Frailty, Hospital Frailty, and Comprehensive Frailty 10 Years Later.

This study aims to examine the associations between midlife Life's Simple 7 (LS7) status, psychosocial health (social isolation and loneliness), and late-life multidimensional frailty indicators, and to investigate their synergistic effect on frailty. We used cohort data from the UK Biobank. Frailty was assessed using physical frailty phenotype, hospital frailty risk score, and frailty index. Cox proportional-hazards models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) on the association between the LS7 score, psychosocial health, and frailty. For the association of LS7 with physical and comprehensive frailty, 39,047 individuals were included. After a median follow-up of 9.0 years, 1329 (3.4%) people were identified with physical frailty, and 5699 (14.6%) with comprehensive frailty. For the association of LS7 with hospital frailty, 366,570 people were included. After a median follow-up of 12.0 years, 18,737 (5.1%) people were identified with hospital frailty. Compared to people with a poor LS7 score, those with an intermediate (physical frailty: 0.64, 0.54-0.77; hospital frailty: 0.60, 0.58-0.62; and comprehensive frailty: 0.77, 0.69-0.86) and optimal LS7 score (physical frailty: 0.31, 0.25-0.39; hospital frailty: 0.39, 0.37-0.41; and comprehensive frailty: 0.62, 0.55-0.69) were associated with a lower risk of frailty. Poor psychosocial health was associated with an increased risk of frailty. People who had a poor psychosocial status and poor LS7 score had the highest risk of frailty. A better LS7 score in midlife was associated with a reduced risk of physical, hospital, and comprehensive frailty. There was a synergistic effect of psychosocial status and LS7 on frailty.

Taylor Neural Network for Real-World Image Super-Resolution.

Due to the difficulty of collecting paired Low-Resolution (LR) and High-Resolution (HR) images, the recent research on single image Super-Resolution (SR) has often been criticized for the data bottleneck of the synthetic image degradation between LRs and HRs. Recently, the emergence of real-world SR datasets, e.g., RealSR and DRealSR, promotes the exploration of Real-World image Super-Resolution (RWSR). RWSR exposes a more practical image degradation, which greatly challenges the learning capacity of deep neural networks to reconstruct high-quality images from low-quality images collected in realistic scenarios. In this paper, we explore Taylor series approximation in prevalent deep neural networks for image reconstruction, and propose a very general Taylor architecture to derive Taylor Neural Networks (TNNs) in a principled manner. Our TNN builds Taylor Modules with Taylor Skip Connections (TSCs) to approximate the feature projection functions, following the spirit of Taylor Series. TSCs introduce the input connected directly with each layer at different layers, to sequentially produces different high-order Taylor maps to attend more image details, and then aggregate the different high-order information from different layers. Only via simple skip connections, TNN is compatible with various existing neural networks to effectively learn high-order components of the input image with little increase of parameters. Furthermore, we have conducted extensive experiments to evaluate our TNNs in different backbones on two RWSR benchmarks, which achieve a superior performance in comparison with existing baseline methods.

Sex-specific associations between diabetes and dementia: the role of age at onset of disease, insulin use and complications.

BACKGROUND: Whether the association of type 2 diabetes (T2DM) with dementia was differed by sex remains unclear, and the roles of age at onset of disease, insulin use and diabetes' complications in their association are unknown. METHODS: This study analyzed data of 447 931 participants from the UK Biobank. We used Cox proportional hazards models to estimate sex-specific hazard ratios (HRs) and 95% confidence intervals (CI), and women-to-men ratio of HRs (RHR) for the association between T2DM and incident dementia [all-cause dementia, Alzheimer's disease (AD), and vascular dementia (VD)]. The roles of age at onset of disease, insulin use and diabetes' complications in their association were also analyzed. RESULTS: Compared to people with no diabetes at all, people with T2DM had increased risk of all-cause dementia (HR 2.85, 95% CI 2.56-3.17). The HRs between T2DM and AD were higher in women than men, with an RHR (95%CI) of 1.56 (1.20, 2.02). There was a trend that people who experienced T2DM before age 55 had higher risk of VD than those who had T2DM after age 55. In addition, there was a trend that T2DM had higher effect on VD that occurred before age 75 years than events that occurred after age 75. Patients with T2DM using insulin had higher risk of all-cause dementia than those without insulin, with an RHR (95%CI) of 1.54 (1.00-2.37). People with complications had doubled risk of all-cause dementia, AD and VD. CONCLUSIONS: Adopting a sex-sensitive strategy to address the risk of dementia in patients with T2DM is instrumental for a precision medicine approach. Meanwhile, it is warranted to consider patients' age at onset of T2DM, insulin use status and complications conditions.

Research on the Influence of International Communication of Chinese Literature on Teenagers' Physical and Mental Health in the Digital Humanistic Environment.

Research Background. With the enhancement of China's international influence, China has stepped into the international ecological environment in the fields of economy, politics, culture, art, and so on, accompanied by the spread of China's culture and literature. China's 5000-year-old cultural history and artistic characteristics are integrated into the ecological field of the international environment. Research Purpose. The international spread of Chinese literature also has a certain impact on the physical and mental health of teenagers. This paper studies and discusses the impact of international communication of Chinese literature on teenagers' physical and mental health in the digital humanistic environment. Under the development environment of Digital Humanities in China, the impact of Chinese literature on the healthy psychology of teenagers, the impact content, and the characteristics of each were evaluated. Through the comparison of physical and mental health growth of adolescents in different ages, the characteristics of physical and mental health of adolescents are explored. The coupling degree of physical and mental health of adolescents in different development environments is compared and analyzed. Research Results. Through the research, it is found that with the development of Digital Humanities, the spread of Chinese literature with internationalization has a positive impact on the physical and mental health and psychological quality construction of teenagers and plays a promoting role in promoting mental health, increasing personal quality, and improving teenagers' correct values. The various ideological and moral qualities of Chinese literature can play a greater role in helping and growing up the physical and mental health education of young people in the new era and are also the literary value of promoting the quality education of the next generation of young people in the world.

RA V-Net: deep learning network for automated liver segmentation.

Objective.Segmenting liver from CT images is the first step for doctors to diagnose a patient's disease. Processing medical images with deep learning models has become a current research trend. Although it can automate segmenting region of interest of medical images, the inability to achieve the required segmentation accuracy is an urgent problem to be solved.Approach.Residual Attention V-Net (RA V-Net) based on U-Net is proposed to improve the performance of medical image segmentation. Composite Original Feature Residual Module is proposed to achieve a higher level of image feature extraction capability and prevent gradient disappearance or explosion. Attention Recovery Module is proposed to add spatial attention to the model. Channel Attention Module is introduced to extract relevant channels with dependencies and strengthen them by matrix dot product.Main results.Through test, evaluation index has improved significantly. Lits2017 and 3Dircadb are chosen as our experimental datasets. On the Dice Similarity Coefficient, RA V-Net exceeds U-Net 0.1107 in Lits2017, and 0.0754 in 3Dircadb. On the Jaccard Similarity Coefficient, RA V-Net exceeds U-Net 0.1214 in Lits2017, and 0.13 in 3Dircadb.Significance.Combined with all the innovations, the model performs brightly in liver segmentation without clear over-segmentation and under-segmentation. The edges of organs are sharpened considerably with high precision. The model we proposed provides a reliable basis for the surgeon to design the surgical plans.

Deep graph learning of inter-protein contacts.

MOTIVATION: Inter-protein (interfacial) contact prediction is very useful for in silico structural characterization of protein-protein interactions. Although deep learning has been applied to this problem, its accuracy is not as good as intra-protein contact prediction. RESULTS: We propose a new deep learning method GLINTER (Graph Learning of INTER-protein contacts) for interfacial contact prediction of dimers, leveraging a rotational invariant representation of protein tertiary structures and a pretrained language model of multiple sequence alignments. Tested on the 13th and 14th CASP-CAPRI datasets, the average top L/10 precision achieved by GLINTER is 54% on the homodimers and 52% on all the dimers, much higher than 30% obtained by the latest deep learning method DeepHomo on the homodimers and 15% obtained by BIPSPI on all the dimers. Our experiments show that GLINTER-predicted contacts help improve selection of docking decoys. AVAILABILITY AND IMPLEMENTATION: The software is available at https://github.com/zw2x/glinter. The datasets are available at https://github.com/zw2x/glinter/data. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Correction to: Predicting protein inter-residue contacts using composite likelihood maximization and deep learning.

Following publication of the original article [1], the author explained that there are several errors in the original article.

Predicting protein inter-residue contacts using composite likelihood maximization and deep learning.

BACKGROUND: Accurate prediction of inter-residue contacts of a protein is important to calculating its tertiary structure. Analysis of co-evolutionary events among residues has been proved effective in inferring inter-residue contacts. The Markov random field (MRF) technique, although being widely used for contact prediction, suffers from the following dilemma: the actual likelihood function of MRF is accurate but time-consuming to calculate; in contrast, approximations to the actual likelihood, say pseudo-likelihood, are efficient to calculate but inaccurate. Thus, how to achieve both accuracy and efficiency simultaneously remains a challenge. RESULTS: In this study, we present such an approach (called clmDCA) for contact prediction. Unlike plmDCA using pseudo-likelihood, i.e., the product of conditional probability of individual residues, our approach uses composite-likelihood, i.e., the product of conditional probability of all residue pairs. Composite likelihood has been theoretically proved as a better approximation to the actual likelihood function than pseudo-likelihood. Meanwhile, composite likelihood is still efficient to maximize, thus ensuring the efficiency of clmDCA. We present comprehensive experiments on popular benchmark datasets, including PSICOV dataset and CASP-11 dataset, to show that: i) clmDCA alone outperforms the existing MRF-based approaches in prediction accuracy. ii) When equipped with deep learning technique for refinement, the prediction accuracy of clmDCA was further significantly improved, suggesting the suitability of clmDCA for subsequent refinement procedure. We further present a successful application of the predicted contacts to accurately build tertiary structures for proteins in the PSICOV dataset. CONCLUSIONS: Composite likelihood maximization algorithm can efficiently estimate the parameters of Markov Random Fields and can improve the prediction accuracy of protein inter-residue contacts.

NFPscanner: a webtool for knowledge-based deciphering of biomedical networks.

BACKGROUND: Many biological pathways have been created to represent different types of knowledge, such as genetic interactions, metabolic reactions, and gene-regulating and physical-binding relationships. Biologists are using a wide range of omics data to elaborately construct various context-specific differential molecular networks. However, they cannot easily gain insight into unfamiliar gene networks with the tools that are currently available for pathways resource and network analysis. They would benefit from the development of a standardized tool to compare functions of multiple biological networks quantitatively and promptly. RESULTS: To address this challenge, we developed NFPscanner, a web server for deciphering gene networks with pathway associations. Adapted from a recently reported knowledge-based framework called network fingerprint, NFPscanner integrates the annotated pathways of 7 databases, 4 algorithms, and 2 graphical visualization modules into a webtool. It implements 3 types of network analysis: Fingerprint: Deciphering gene networks and highlighting inherent pathway modules Alignment: Discovering functional associations by finding optimized node mapping between 2 gene networks Enrichment: Calculating and visualizing gene ontology (GO) and pathway enrichment for genes in networks Users can upload gene networks to NFPscanner through the web interface and then interactively explore the networks' functions. CONCLUSIONS: NFPscanner is open-source software for non-commercial use, freely accessible at http://biotech.bmi.ac.cn/nfs .

BiRen: predicting enhancers with a deep-learning-based model using the DNA sequence alone.

MOTIVATION: Enhancer elements are noncoding stretches of DNA that play key roles in controlling gene expression programmes. Despite major efforts to develop accurate enhancer prediction methods, identifying enhancer sequences continues to be a challenge in the annotation of mammalian genomes. One of the major issues is the lack of large, sufficiently comprehensive and experimentally validated enhancers for humans or other species. Thus, the development of computational methods based on limited experimentally validated enhancers and deciphering the transcriptional regulatory code encoded in the enhancer sequences is urgent. RESULTS: We present a deep-learning-based hybrid architecture, BiRen, which predicts enhancers using the DNA sequence alone. Our results demonstrate that BiRen can learn common enhancer patterns directly from the DNA sequence and exhibits superior accuracy, robustness and generalizability in enhancer prediction relative to other state-of-the-art enhancer predictors based on sequence characteristics. Our BiRen will enable researchers to acquire a deeper understanding of the regulatory code of enhancer sequences. AVAILABILITY AND IMPLEMENTATION: Our BiRen method can be freely accessed at https://github.com/wenjiegroup/BiRen . CONTACT: shuwj@bmi.ac.cn or boxc@bmi.ac.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.