Somatomotor-visual resting state functional connectivity increases after 2 years in the UK Biobank longitudinal cohort.

Purpose: Functional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been observed in children, in which high connectivity among all brain regions changes to a more modular structure with maturation. We examine FC changes in older adults after 2 years of aging in the UK Biobank (UKB) longitudinal cohort. Approach: We process fMRI connectivity data using the Power264 atlas and then test whether the average internetwork FC changes in the 2722-subject longitudinal cohort are statistically significant using a Bonferroni-corrected t-test. We also compare the ability of Power264 and UKB-provided, independent component analysis (ICA)-based FC to determine which of a longitudinal scan pair is older. Finally, we investigate cross-sectional FC changes as well as differences due to differing scanner tasks in the UKB, Philadelphia Neurodevelopmental Cohort, and Alzheimer's Disease Neuroimaging Initiative datasets. Results: We find a 6.8% average increase in somatomotor network (SMT)-visual network (VIS) connectivity from younger to older scans (corrected p<10-15) that occurs in male, female, older subject (>65 years old), and younger subject (<55 years old) groups. Among all internetwork connections, the average SMT-VIS connectivity is the best predictor of relative scan age. Using the full FC and a training set of 2000 subjects, one is able to predict which scan is older 82.5% of the time using either the full Power264 FC or the UKB-provided ICA-based FC. Conclusions: We conclude that SMT-VIS connectivity increases with age in the UKB longitudinal cohort and that resting state FC increases with age in the UKB cross-sectional cohort.

Investigating the shared genetic architecture between breast and ovarian cancers.

High heritability and strong correlation have been observed in breast and ovarian cancers. However, their shared genetic architecture remained unclear. Linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics (ρ-HESS) were applied to estimate heritability and genetic correlations. Bivariate causal mixture model (MiXeR) was used to qualify the polygenic overlap. Then, stratified-LDSC (S-LDSC) was used to identify tissue and cell type specificity. Meanwhile, the adaptive association test called MTaSPUsSet was performed to identify potential pleiotropic genes. The Single Nucleotide Polymorphisms (SNP) heritability was 13% for breast cancer and 5% for ovarian cancer. There was a significant genetic correlation between breast and ovarian cancers (rg=0.21). Breast and ovarian cancers exhibited polygenic overlap, sharing 0.4 K out 2.8 K of causal variants. Tissue and cell type specificity displayed significant enrichment in female breast mammary, uterus, kidney tissues, and adipose cell. Moreover, the 74 potential pleiotropic genes were identified between breast and ovarian cancers, which were related to the regulation of cell cycle and cell death. We quantified the shared genetic architecture between breast and ovarian cancers and shed light on the biological basis of the co-morbidity. Ultimately, these findings facilitated the understanding of disease etiology.

Association of per- and polyfluoroalkyl substances with constipation: The National Health and Nutrition Examination Survey (2005-2010).

BACKGROUND: The impact of per- and polyfluoroalkyl substances (PFAS) on constipation, as mediated through gastrointestinal absorption and perturbations to the intestinal microecology, remains poorly understood. OBJECTIVE: This study seeks to explain the relationship between PFAS and constipation. METHODS: A total of 2945 adults from the National Health and Nutrition Examination Survey (NHANES) 2005-2010 were included in this study. Constipation was defined using the Bristol Stool Form Scale (BSFS) based on stool consistency. The relationship between PFAS and constipation was evaluated using weighted logistic regression and restricted cubic spline (RCS) analysis, while adjusting for confounding variables. RESULTS: The weighted median concentration of total PFAS (ΣPFAS) was significantly lower in individuals with constipation (19.01 µg/L) compared to those without constipation (23.30 µg/L) (p < 0.0001). Subgroup analysis revealed that the cumulative effect of PFAS was more pronounced in the elderly, men, individuals with obesity, high school education or equivalent, and high-income individuals (p < 0.05). After adjusting for confounding factors, multivariable analysis demonstrated an inverse association between PFOA [OR (95% CI), 0.666(0.486,0.914)] and PFHxS [OR (95% CI), 0.699(0.482,1.015)], and constipation. None of the personal and lifestyle factors showed a significant correlation with this negative association, as confirmed by subgroup analysis and interaction testing (p for interaction > 0.05). The RCS analysis demonstrated a linear inverse relationship between PFAS levels and constipation. CONCLUSION: The findings of this study provide evidence of a significant inverse correlation between serum concentrations of PFAS, particularly PFOA and PFHxS, and constipation.

Circular RNA Vaccines against Monkeypox Virus Provide Potent Protection against Vaccinia Virus Infection in mice.

Since the outbreak of mpox in 2022, widespread concern has been placed on imposing an urgent demand for specific vaccines that offer safer and more effective protection. Using an efficient and scalable circular RNA (circRNA) platform, we constructed four circRNA vaccines that could induce robust neutralizing antibodies as well as T-cell responses by expressing different surface proteins of monkeypox virus (MPXV), resulting in potent protection against vaccinia virus (VACV) in mice. Strikingly, the combination of the four circular RNA vaccines demonstrated the best protection against VACV challenge among all the tested vaccines. Our study provides a favorable approach for developing MPXV-specific vaccines by using a circular mRNA platform and opens up novel avenues for future vaccine research.

An Efficient Vector-Based CRISPR/Cas9 System in Zebrafish Cell Line.

The clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) system has been widely applied in animals as an efficient genome editing tool. However, the technique is difficult to implement in fish cell lines partially due to the lack of efficient promoters to drive the expression of both sgRNA and the Cas9 protein within a single vector. In this study, it was indicated that the zebrafish U6 RNA polymerase III (ZFU6) promoter could efficiently induce tyrosinase (tyr) gene editing and lead to loss of retinal pigments when co-injection with Cas9 mRNA in zebrafish embryo. Furthermore, an optimized all-in-one vector for expression of the CRISPR/Cas9 system in the zebrafish fibroblast cell line (PAC2) was constructed by replacing the human U6 promoter with ZFU6 promoter, basing on the lentiCRISPRV2 system that widely applied in mammal cells. This new vector could successfully target the cellular communication network factor 2a (ctgfa) gene and demonstrated its function in the PAC2 cell. Notably, the vector could also be used to edit the endogenous EMX1 gene in the mammal 293 T cell line, implying its wide application potential. In conclusion, we established a new gene editing tool for zebrafish cell line, which could be a useful in vitro platform for high-throughput analyzing gene function in fish.

PCKRF: Point Cloud Completion and Keypoint Refinement With Fusion Data for 6D Pose Estimation.

Some robust point cloud registration approaches with controllable pose refinement magnitude, such as ICP and its variants, are commonly used to improve 6D pose estimation accuracy. However, the effectiveness of these methods gradually diminishes with the advancement of deep learning techniques and the enhancement of initial pose accuracy, primarily due to their lack of specific design for pose refinement. In this paper, we propose Point Cloud Completion and Keypoint Refinement with Fusion Data (PCKRF), a new pose refinement pipeline for 6D pose estimation. The pipeline consists of two steps. First, it completes the input point clouds via a novel pose-sensitive point completion network. The network uses both local and global features with pose information during point completion. Then, it registers the completed object point cloud with the corresponding target point cloud by our proposed Color supported Iterative KeyPoint (CIKP) method. The CIKP method introduces color information into registration and registers a point cloud around each keypoint to increase stability. The PCKRF pipeline can be integrated with existing popular 6D pose estimation methods, such as the full flow bidirectional fusion network, to further improve their pose estimation accuracy. Experiments demonstrate that our method exhibits superior stability compared to existing approaches when optimizing initial poses with relatively high precision. Notably, the results indicate that our method effectively complements most existing pose estimation techniques, leading to improved performance in most cases. Furthermore, our method achieves promising results even in challenging scenarios involving textureless and symmetrical objects. Our source code is available at https://github.com/zhanhz/KRF.

Developmentally sensitive multispectral cortical connectivity profiles serving visual selective attention.

Throughout childhood and adolescence, the brain undergoes significant structural and functional changes that contribute to the maturation of multiple cognitive domains, including selective attention. Selective attention is crucial for healthy executive functioning and while key brain regions serving selective attention have been identified, their age-related changes in neural oscillatory dynamics and connectivity remain largely unknown. We examined the developmental sensitivity of selective attention circuitry in 91 typically developing youth aged 6 - 13 years old. Participants completed a number-based Simon task while undergoing magnetoencephalography (MEG) and the resulting data were preprocessed and transformed into the time-frequency domain. Significant oscillatory brain responses were imaged using a beamforming approach, and task-related peak voxels in the occipital, parietal, and cerebellar cortices were used as seeds for subsequent whole-brain connectivity analyses in the alpha and gamma range. Our key findings revealed developmentally sensitive connectivity profiles in multiple regions crucial for selective attention, including the temporoparietal junction (alpha) and prefrontal cortex (gamma). Overall, these findings suggest that brain regions serving selective attention are highly sensitive to developmental changes during the pubertal transition period.

Rationally Designing the Supramolecular Interfaces of Nanoparticle Superlattices with Multivalent Polymers.

In supramolecular materials, multiple weak binding groups can act as a single collective unit when confined to a localized volume, thereby producing strong but dynamic bonds between material building blocks. This principle of multivalency provides a versatile means of controlling material assembly, as both the number and the type of supramolecular moieties become design handles to modulate the strength of intermolecular interactions. However, in materials with building blocks significantly larger than individual supramolecular moieties (e.g., polymer or nanoparticle scaffolds), the degree of multivalency is difficult to predict or control, as sufficiently large scaffolds inherently preclude separated supramolecular moieties from interacting. Because molecular models commonly used to examine supramolecular interactions are intrinsically unable to examine any trends or emergent behaviors that arise due to nanoscale scaffold geometry, our understanding of the thermodynamics of these massively multivalent systems remains limited. Here we address this challenge via the coassembly of polymer-grafted nanoparticles and multivalent polymers, systematically examining how multivalent scaffold size, shape, and spacing affect their collective thermodynamics. Investigating the interplay of polymer structure and supramolecular group stoichiometry reveals complicated but rationally describable trends that demonstrate how the supramolecular scaffold design can modulate the strength of multivalent interactions. This approach to self-assembled supramolecular materials thus allows for the manipulation of polymer-nanoparticle composites with controlled thermal stability, nanoparticle organization, and tailored meso- to microscopic structures. The sophisticated control of multivalent thermodynamics through precise modulation of the nanoscale scaffold geometry represents a significant advance in the ability to rationally design complex hierarchically structured materials via self-assembly.

Development of Serum Lactate Level-Based Nomograms for Predicting Diabetic Kidney Disease in Type 2 Diabetes Mellitus Patients.

Purpose: To establish nomograms integrating serum lactate levels and traditional risk factors for predicting diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) patients. Patients and methods: A total of 570 T2DM patients and 100 healthy subjects were enrolled. T2DM patients were categorized into normal and high lactate groups. Univariate and multivariate logistic regression analyses were employed to identify independent predictors for DKD. Then, nomograms for predicting DKD were established, and the model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration, and decision curve analysis (DCA). Results: T2DM patients exhibited higher lactate levels compared to those in healthy subjects. Glucose, platelet, uric acid, creatinine, and hypertension were independent factors for DKD in T2DM patients with normal lactate levels, while diabetes duration, creatinine, total cholesterol, and hypertension were indicators in high lactate levels group (P<0.05). The AUC values were 0.834 (95% CI, 0.776 to 0.891) and 0.741 (95% CI, 0.688 to 0.795) for nomograms in both normal lactate and high lactate groups, respectively. The calibration curve demonstrated excellent agreement of fit. Furthermore, the DCA revealed that the threshold probability and highest Net Yield were 17-99% and 0.36, and 24-99% and 0.24 for the models in normal lactate and high lactate groups, respectively. Conclusion: The serum lactate level-based nomogram models, combined with traditional risk factors, offer an effective tool for predicting DKD probability in T2DM patients. This approach holds promise for early risk assessment and tailored intervention strategies.

Efficacy of transcranial photobiomodulation in the treatment for major depressive disorder: A TMS-EEG and pilot study.

BACKGROUND: Major depressive disorder (MDD) was a prevalent mental condition that may be accompanied by decreased excitability of left frontal pole (FP) and abnormal brain connections. An 820 nm tPBM can induce an increase in stimulated cortical excitability. The purpose of our study was to establish how clinical symptoms and time-varying brain network connectivity of MDD were affected by transcranial photobiomodulation (tPBM). METHODS: A total of 11 patients with MDD received 820 nm tPBM targeting the left FP for 14 consecutive days. The severity of symptoms was evaluated by neuropsychological assessments at baseline, after treatment, 4-week and 8-week follow-up; 8-min transcranial magnetic stimulation combined electroencephalography (TMS-EEG) was performed for five healthy controls and five patients with MDD before and after treatment, and time-varying EEG network was analyzed using the adaptive-directed transfer function. RESULTS: All of scales scores in the 11 patients decreased significantly after 14-day tPBM (p < .01) and remained at 8-week follow-up. The time-varying brain network analysis suggested that the brain regions with enhanced connection information outflow in MDD became gradually more similar to healthy controls after treatment. CONCLUSIONS: This study showed that tPBM of the left FP could improve symptoms of patients with MDD and normalize the abnormal network connections.

Vertical migration behavior simulation and prediction of Pb and Cd in co-contaminated soil around Pb-Zn smelting slag site.

Heavy metal migration in soil poses a serious threat to the soil and groundwater. Understanding the migration pattern of heavy metals (HMs) under different factors could provide a more reasonable position for pollution evaluation and targetoriented treatment of soil heavy metal. In this study, the migration behavior of Pb and Cd in co-contaminated soil under different pH and ionic strength (NaCl concentration) was simulated using convective dispersion equation (CDE). We predicted the migration trends of Pb and Cd in soils after 5, 10, and 20 years via PHREEQC. The results showed that the migration time of Cd in the soil column experiment was about 60 days faster than that of Pb, and the migration trend was much steeper. The CDE was proved to describe the migration behavior of Pb and Cd (R2 > 0.75) in soil. The predicted results showed that Cd migrated to 15-20 cm of soil within 7 years and Pb stayed mainly in the top 0-6 cm of soil within 5 years as the duration of irrigation increased. Overall, our study is expected to provide new insight into the migration of heavy metal in soil ecosystems and guidance for reducing risk of heavy metal in the environment.

Relationship between fatty acid intake and aging: a Mendelian randomization study.

BACKGROUND: Observational studies have previously shown a possible link between fatty acids and aging-related diseases, raising questions about its health implications. However, the causal relationship between the two remains uncertain. METHODS: Univariable and multivariable Mendelian randomization (MR) was used to analyze the relationship between five types of fatty acids-polyunsaturated fatty acid (PUFA), monounsaturated fatty acid (MUFA), saturated fatty acid (SFA), Omega-6 fatty acid (Omega-6 FA), and Omega-3 fatty acid (Omega-3 FA) and three markers of aging: telomere length (TL), frailty index (FI), and facial aging (FclAg). The primary approach for Mendelian randomization (MR) analysis involved utilizing the inverse variance weighted (IVW) method, with additional supplementary methods employed. RESULTS: Univariate MR analysis revealed that MUFA, PUFA, SFA, and Omega-6 fatty acids were positively associated with TL (MUFA OR: 1.019, 95% CI: 1.006-1.033; PUFA OR: 1.014, 95% CI: 1.002-1.026; SFA OR: 1.016, 95% CI: 1.002-1.031; Omega-6 FAs OR=1.031, 95% CI: 1.006-1.058). PUFA was also associated with a higher FI (OR: 1.033, 95% CI: 1.009-1.057). In multivariate MR analysis, after adjusting for mutual influences among the five fatty acids, MUFA and PUFA were positively independently associated with TL (MUFA OR: 1.1508, 95% CI = 1.0724-1.2350; PUFA OR: 1.1670, 95% CI = 1.0497-1.2973, while SFA was negatively correlated (OR: 0.8005, 95% CI: 0.7045-0.9096). CONCLUSIONS: Our research presents compelling evidence of a causal association between certain fatty acids and indicators of the aging process. In particular, MUFA and PUFA may play a role in slowing down the aging process, while SFAs may contribute to accelerated aging. These findings could have significant implications for dietary recommendations aimed at promoting healthy aging.

Multiview hyperedge-aware hypergraph embedding learning for multisite, multiatlas fMRI based functional connectivity network analysis.

Recently, functional magnetic resonance imaging (fMRI) based functional connectivity network (FCN) analysis via graph convolutional networks (GCNs) has shown promise for automated diagnosis of brain diseases by regarding the FCNs as irregular graph-structured data. However, multiview information and site influences of the FCNs in a multisite, multiatlas fMRI scenario have been understudied. In this paper, we propose a Class-consistency and Site-independence Multiview Hyperedge-Aware HyperGraph Embedding Learning (CcSi-MHAHGEL) framework to integrate FCNs constructed on multiple brain atlases in a multisite fMRI study. Specifically, for each subject, we first model brain network as a hypergraph for every brain atlas to characterize high-order relations among multiple vertexes, and then introduce a multiview hyperedge-aware hypergraph convolutional network (HGCN) to extract a multiatlas-based FCN embedding where hyperedge weights are adaptively learned rather than employing the fixed weights precalculated in traditional HGCNs. In addition, we formulate two modules to jointly learn the multiatlas-based FCN embeddings by considering the between-subject associations across classes and sites, respectively, i.e., a class-consistency module to encourage both compactness within every class and separation between classes for promoting discrimination in the embedding space, and a site-independence module to minimize the site dependence of the embeddings for mitigating undesired site influences due to differences in scanning platforms and/or protocols at multiple sites. Finally, the multiatlas-based FCN embeddings are fed into a few fully connected layers followed by the soft-max classifier for diagnosis decision. Extensive experiments on the ABIDE demonstrate the effectiveness of our method for autism spectrum disorder (ASD) identification. Furthermore, our method is interpretable by revealing ASD-relevant brain regions that are biologically significant.

sh- Ambra1 inhibits IRS-1/PI3K/Akt signalling pathway to reduce autophagy in gestational diabetes.

INTRODUCTION: Gestational diabetes mellitus (GDM) is the most common metabolic disease in pregnancy. However, studies of activating molecule of Beclin1-regulated autophagy (Ambra1) affecting the insulin substrate receptor 1/phosphatidylinositol 3 kinase/protein kinase B (IRS-1/PI3K/Akt) signalling pathway in GDM have not been reported. The aim of the study was to detect the difference of Ambra1 expression in the placenta of normal pregnant women and GDM patients. MATERIAL AND METHODS: An in vitro model of gestational diabetes mellitus was established by inducing HTR8/Svneo cells from human chorionic trophoblast layer with high glucose. The changes of cell morphology were observed by inverted microscope, and the expression levels of Ambra1 gene and protein in model cells were detected. After this, Ambra1 gene was silenced by shRNA transfection, and PI3K inhibitor was added to detect changes in Ambra1, autophagy, and insulin (INS) signalling pathways. RESULTS: The protein expression levels of Ambra1, Bcl-2 interacting protein (Beclin-1), and microtubule-associated proteins 1A/1B light chain 3B (LC3-II) in the placentas of GDM pregnant women were higher than those of normal pregnant women. High glucose induces morphological changes in HTR8/Svneo cells and increases Ambra1 transcription and translation levels. sh-Ambra1 increased survival of HTR8/SvNEO-HG cells and inhibited Ambra1, Beclin1, and LC3-II transcription and translation levels. Also, sh-Ambra1 increased IRS-1/PI3K/Akt protein phosphorylation levels and inhibited the IRS-1/PI3K/Akt signalling pathway and its resulting autophagy. CONCLUSIONS: sh-Ambra1 increased IRS-1/PI3K/Akt protein phosphorylation levels to reduce autophagy in gestational diabetes.

Better with age: Developmental changes in oscillatory activity during verbal working memory encoding and maintenance.

Numerous investigations have characterized the oscillatory dynamics serving working memory in adults, but few have probed its relationship with chronological age in developing youth. We recorded magnetoencephalography during a modified Sternberg verbal working memory task in 82 youth participants aged 6-14 years old. Significant oscillatory responses were identified and imaged using a beamforming approach and the resulting whole-brain maps were probed for developmental effects during the encoding and maintenance phases. Our results indicated robust oscillatory responses in the theta (4-7 Hz) and alpha (8-14 Hz) range, with older participants exhibiting stronger alpha oscillations in left-hemispheric language regions. Older participants also had greater occipital theta power during encoding. Interestingly, there were sex-by-age interaction effects in cerebellar cortices during encoding and in the right superior temporal region during maintenance. These results extend the existing literature on working memory development by showing strong associations between age and oscillatory dynamics across a distributed network. To our knowledge, these findings are the first to link chronological age to alpha and theta oscillatory responses serving working memory encoding and maintenance, both across and between male and female youth; they reveal robust developmental effects in crucial brain regions serving higher order functions.

Exploring General Intelligence via Gated Graph Transformer in Functional Connectivity Studies.

Functional connectivity (FC) as derived from fMRI has emerged as a pivotal tool in elucidating the intricacies of various psychiatric disorders and delineating the neural pathways that underpin cognitive and behavioral dynamics inherent to the human brain. While Graph Neural Networks (GNNs) offer a structured approach to represent neuroimaging data, they are limited by their need for a predefined graph structure to depict associations between brain regions, a detail not solely provided by FCs. To bridge this gap, we introduce the Gated Graph Transformer (GGT) framework, designed to predict cognitive metrics based on FCs. Empirical validation on the Philadelphia Neurodevelopmental Cohort (PNC) underscores the superior predictive prowess of our model, further accentuating its potential in identifying pivotal neural connectivities that correlate with human cognitive processes.

Learning Spatiotemporal Brain Dynamics in Adolescents via Multimodal MEG and fMRI Data Fusion Using Joint Tensor/Matrix Decomposition.

OBJECTIVE: Brain function is understood to be regulated by complex spatiotemporal dynamics, and can be characterized by a combination of observed brain response patterns in time and space. Magnetoencephalography (MEG), with its high temporal resolution, and functional magnetic resonance imaging (fMRI), with its high spatial resolution, are complementary imaging techniques with great potential to reveal information about spatiotemporal brain dynamics. Hence, the complementary nature of these imaging techniques holds much promise to study brain function in time and space, especially when the two data types are allowed to fully interact. METHODS: We employed coupled tensor/matrix factorization (CMTF) to extract joint latent components in the form of unique spatiotemporal brain patterns that can be used to study brain development and function on a millisecond scale. RESULTS: Using the CMTF model, we extracted distinct brain patterns that revealed fine-grained spatiotemporal brain dynamics and typical sensory processing pathways informative of high-level cognitive functions in healthy adolescents. The components extracted from multimodal tensor fusion possessed better discriminative ability between high- and low-performance subjects than single-modality data-driven models. CONCLUSION: Multimodal tensor fusion successfully identified spatiotemporal brain dynamics of brain function and produced unique components with high discriminatory power. SIGNIFICANCE: The CMTF model is a promising tool for high-order, multimodal data fusion that exploits the functional resolution of MEG and fMRI, and provides a comprehensive picture of the developing brain in time and space.

Cannabidiol regulates the activation of hepatic stellate cells by modulating the NOX4 and NF-κB pathways.

Cannabidiol (CBD) is an extract of natural cannabinoids that has therapeutic implications for a variety of ailments, such as neurological diseases, cardiomyopathy, and diabetes, due to its strong anti-inflammatory and oxidative stress properties. Our purpose was to reveal the possible underlying mechanisms and effect of CBD on the glucose oxidase (GO)-induced activation of HSC-T6 and LX-2 cells. The results showed that CBD effectively inhibited the proliferation and activation of HSC-T6 and LX-2 cells, and reduced the production of profibrotic factors to different degrees. CBD disrupted the NOX4 signalling pathway in activated HSC-T6 and LX-2 cells, reduced ROS and MDA levels, and increased SOD and GSH levels, thereby stabilizing the oxidative imbalance. CBD significantly inhibited the phosphorylation and degradation of NF-κB and IκBα, and decreased the release of TNF-α, IL-1ß and IL-6. Moreover, CBD and an NF-κB-specific inhibitor (CAPE) effectively inhibited the expression of α-SMA, COL I, TNF-α and IL-1ß to promote collagen metabolism and inhibit the inflammatory response. Overall, CBD inhibited HSCs activation through a and the mechanism involving the inhibition of NOX4 and NF-κB-dependent ROS regulation, thereby reducing inflammation and ameliorating oxidative imbalances.