Global Correlation Research Articles

GSGSL: Gravity-driven self-supervised graph structure learning

The existing graph structure learning methods heavily rely on the original graph structure and often fail to capture potential high-level abstract features and global correlations within the graph. To address these issues, this paper proposes a Universal Gravitational-driven Self-supervised Graph Structure Learning method (GSGSL), overcoming the limitations of current graph structure learning methods in advanced feature extraction. GSGSL models the graph structure using the universal force of gravity in a dynamic, globally adaptive manner. Additionally, it employs a multi-perspective contrastive learning approach to eliminate the need for external labels, jointly optimizing graph structure learning with downstream tasks. Extensive experimental results on public datasets demonstrate that, in comparative experiments without considering the original adjacency matrix, the GSGSL method outperforms baseline models by 0.5% to 17.3%. In comparative experiments optimizing the original adjacency matrix, the GSGSL method exhibits improvements ranging from 0.9% to 12%, validating that the GSGSL approach to simulating gravitational fields has better dynamic characteristics. It effectively captures advanced abstract features and global characteristics of graph data, surpassing the limitations of baseline methods in graph structure learning.

Transient hygro- and hydro-expansion of freely and restrained dried paper: the fiber-network coupling

AbstractThe transient dimensional changes during hygro-expansion and hydro-expansion of freely and restrained dried, softwood and hardwood sheets and fibers is monitored, to unravel the governing micro-mechanisms occurring during gradual water saturation. The response of individual fibers is measured using a full-field global digital height correlation method, which has been extended to monitor the transient hydro-expansion of fibers from dry to fully saturated. The hygro- and hydro-expansion is larger for freely versus restrained dried and softwood versus hardwood handsheets. The transient sheet-scale hydro-expansion reveals a sudden strain and moisture content step. It is postulated that the driving mechanism is the moisture-induced softening of the so-called ”dislocated regions” in the fiber’s cellulose micro-fibrils, unlocking further fiber swelling. The strain step is negligible for restrained dried handsheets, which is attributed to the ”dislocated cellulose regions” being locked in their stretched configuration during restrained drying, which is supported by the single fiber hydro-expansion measurements. Finally, an inter-fiber bond model is exploited and adapted to predict the sheet-scale hygro-expansion from the fiber level characteristics. The model correctly predicts the qualitative differences between freely versus restrained dried and softwood versus hardwood handsheets, yet, its simplified geometry does not allow for more quantitative predictions of the sheet-scale hydro-expansion.

Full waveform inversion using frequency shift envelope-based global correlation norm for ultrasound computed tomography.

Many studies have been carried out on ultrasound computed tomography (USCT) for its ability to offer quantitative measurements of tissue sound speed. Full waveform inversion (FWI) is a technique for reconstructing high-resolution sound speed images by iteratively minimizing the difference between the observed ultrasound data and the synthetic data based on the waveform equation. However, FWI suffers from cycle-skipping, which usually causes FWI convergence at a local minimum. Cycle-skipping occurs when the phase difference between the observed data and the synthetic data exceeds half a cycle. The simplest way to avoid cycle-skipping is to use low-frequency information for reconstruction. Nevertheless, in imaging systems, the response bandwidth of the probe is limited, and reliable low-frequency information often exceeds the response band. Therefore, it is a challenge to perform FWI imaging and avoid cycle-skipping problems without low-frequency information. In this paper, we propose a frequency shift envelope-based global correlation norm (FSEGCN), where an artificial source wavelet with a lower frequency is adopted to calculate synthetic data. FSEGCN compared with FWI, envelope inversion (EI), global correlation norm (GCN), envelope-based global correlation norm (EGCN) through concentric circle phantom without low-frequency information. The experimental results demonstrated the capability of the proposed method to recover the sound speed close to the exact model in the absence of low-frequency information, whereas FWI, EI, GCN, and
EGCN cannot. Experiments on phantoms of the human head and calf show that artificial source wavelets can reduce image artifacts and enhance reconstruction robustness, when original low-frequency information is absent.

Characterizing the polygenic overlap and shared loci between rheumatoid arthritis and cardiovascular diseases

BackgroundDespite substantial research revealing that patients with rheumatoid arthritis (RA) have excessive morbidity and mortality of cardiovascular disease (CVD), the mechanism underlying this association has not been fully known. This study aims to systematically investigate the phenotypic and genetic correlation between RA and CVD.MethodsBased on UK Biobank, we conducted two cohort studies to evaluate the phenotypic relationships between RA and CVD, including atrial fibrillation (AF), coronary artery disease (CAD), heart failure (HF), and stroke. Next, we used linkage disequilibrium score regression, Local Analysis of [co]Variant Association, and bivariate causal mixture model (MiXeR) methods to examine the genetic correlation and polygenic overlap between RA and CVD, using genome-wide association summary statistics. Furthermore, we explored specific shared genetic loci by conjunctional false discovery rate analysis and association analysis based on subsets.ResultsCompared with the general population, RA patients showed a higher incidence of CVD (hazard ratio [HR] = 1.21, 95% confidence interval [CI]: 1.15–1.28). We observed positive genetic correlations of RA with AF and stroke, and a mixture of negative and positive local genetic correlations underlying the global genetic correlation for CAD and HF, with 13 ~ 33% of shared genetic variants for these trait pairs. We further identified 23 pleiotropic loci associated with RA and at least one CVD, including one novel locus (rs7098414, TSPAN14, 10q23.1). Genes mapped to these shared loci were enriched in immune and inflammatory-related pathways, and modifiable risk factors, such as high diastolic blood pressure.ConclusionsThis study revealed the shared genetic architecture of RA and CVD, which may facilitate drug target identification and improved clinical management.

Genetic influences and causal pathways shared between cannabis use disorder and other substance use traits.

Cannabis use disorder (CanUD) has increased with the legalization of the use of cannabis. Around 20% of individuals using cannabis develop CanUD, and the number of users has grown with increasing ease of access. CanUD and other substance use disorders (SUDs) are associated phenotypically and genetically. We leveraged new CanUD genomics data to undertake genetically-informed analyses with unprecedented power, to investigate the genetic architecture and causal relationships between CanUD and lifetime cannabis use with risk for developing SUDs and substance use traits. Analyses included calculating local and global genetic correlations, genomic structural equation modeling (genomicSEM), and Mendelian Randomization (MR). Results from the genetic correlation and genomicSEM analyses demonstrated that CanUD and cannabis use differ in their relationships with SUDs and substance use traits. We found significant causal effects of CanUD influencing all the analyzed traits: opioid use disorder (OUD) (Inverse variant weighted, IVW β = 0.925 ± 0.082), problematic alcohol use (PAU) (IVW β = 0.443 ± 0.030), drinks per week (DPW) (IVW β = 0.182 ± 0.025), Fagerström Test for Nicotine Dependence (FTND) (IVW β = 0.183 ± 0.052), cigarettes per day (IVW β = 0.150 ± 0.045), current versus former smokers (IVW β = 0.178 ± 0.052), and smoking initiation (IVW β = 0.405 ± 0.042). We also found evidence of bidirectionality showing that OUD, PAU, smoking initiation, smoking cessation, and DPW all increase risk of developing CanUD. For cannabis use, bidirectional relationships were inferred with PAU, smoking initiation, and DPW; cannabis use was also associated with a higher risk of developing OUD (IVW β = 0.785 ± 0.266). GenomicSEM confirmed that CanUD and cannabis use load onto different genetic factors. We conclude that CanUD and cannabis use can increase the risk of developing other SUDs. This has substantial public health implications; the move towards legalization of cannabis use may be expected to increase other kinds of problematic substance use. These harmful outcomes are in addition to the medical harms associated directly with CanUD.

Phenotypic and genetic effect of carotid intima-media thickness on the risk of stroke.

While carotid intima-media thickness (cIMT) as a noninvasive surrogate measure of atherosclerosis is widely considered a risk factor for stroke, the intrinsic link underlying cIMT and stroke has not been fully understood. We aimed to evaluate the clinical value of cIMT in stroke through the investigation of phenotypic and genetic relationships between cIMT and stroke. We evaluated phenotypic associations using observational data from UK Biobank (N = 21,526). We then investigated genetic relationships leveraging genomic data conducted in predominantly European ancestry for cIMT (N = 45,185) and any stroke (AS, Ncase/Ncontrol=40,585/406,111). Observational analyses suggested an increased hazard of stroke per one standard deviation increase in cIMT (cIMTmax-AS: hazard ratio (HR) = 1.39, 95%CI = 1.09-1.79; cIMTmean-AS: HR = 1.39, 95%CI = 1.09-1.78; cIMTmin-AS: HR = 1.32, 95%CI = 1.04-1.68). A positive global genetic correlation was observed (cIMTmax-AS: [Formula: see text]=0.23, P=9.44 × 10-5; cIMTmean-AS: [Formula: see text]=0.21, P=3.00 × 10-4; cIMTmin-AS: [Formula: see text]=0.16, P=6.30 × 10-3). This was further substantiated by five shared independent loci and 15 shared expression-trait associations. Mendelian randomization analyses suggested no causal effect of cIMT on stroke (cIMTmax-AS: odds ratio (OR)=1.12, 95%CI=0.97-1.28; cIMTmean-AS: OR=1.09, 95%CI=0.93-1.26; cIMTmin-AS: OR=1.03, 95%CI = 0.90-1.17). A putative association was observed for genetically predicted stroke on cIMT (AS-cIMTmax: beta=0.07, 95%CI = 0.01-0.13; AS-cIMTmean: beta=0.08, 95%CI = 0.01-0.15; AS-cIMTmin: beta = 0.08, 95%CI = 0.01-0.16) in the reverse direction MR, which attenuated to non-significant in sensitivity analysis. Our work does not find evidence supporting causal associations between cIMT and stroke. The pronounced cIMT-stroke association is intrinsic, and mostly attributed to shared genetic components. The clinical value of cIMT as a surrogate marker for stroke risk in the general population is likely limited.

Micro-Doppler based Human Activity Recognition using ABOA based Dual Spatial Convolution with Gated Recurrent Unit

The through-wall capability, device-free detection of radar-based human activity recognition are drawing a lot of interest from both academics and industry. The majority of radar-based systems do not yet combine signal analysis and feature extraction in the frequency domain and the time domain. Applications like smart homes, assisted living, and monitoring rely on human identification and activity recognition (HIAR). Radar has a number of advantages over other sensing modalities, such as the ability to shield users' privacy and conduct contactless sensing. The article introduces a new human tracking system that uses radar and a classifier called Dual Spatial Convolution Gated Recurrent Unit (DSC-GRU) to identify the subject and their behavior. The system follows the person and identifies the type of motion whenever it detects movement. One important feature is the integration of the GRU with the DSC unit, which allows the model to simultaneously capture the spatiotemporal dependence. Present prediction models just take into account spatial features that are immediately adjacent to each other, disregarding or just superimposing global spatial features when taking spatial correlation into account. A new dependency graph is created by calculating the correlation among nodes using the correlation coefficient; this graph represents the global spatial dependence, while the classic static graph represents the neighboring spatial dependence in the DSC unit. The DSC unit goes a step further by using a modified gated mechanism to quantify the various contributions of both local and global spatial correlation. While previous models performed worse, the suggested model outperformed them with an accuracy of 99.45 percent and a precision of 97.15 percent.

A novel transformer autoencoder for multi-modal emotion recognition with incomplete data

Multi-modal signals have become essential data for emotion recognition since they can represent emotions more comprehensively. However, in real-world environments, it is often impossible to acquire complete data on multi-modal signals, and the problem of missing modalities causes severe performance degradation in emotion recognition. Therefore, this paper represents the first attempt to use a transformer-based architecture, aiming to fill the modality-incomplete data from partially observed data for multi-modal emotion recognition (MER). Concretely, this paper proposes a novel unified model called transformer autoencoder (TAE), comprising a modality-specific hybrid transformer encoder, an inter-modality transformer encoder, and a convolutional decoder. The modality-specific hybrid transformer encoder bridges a convolutional encoder and a transformer encoder, allowing the encoder to learn local and global context information within each particular modality. The inter-modality transformer encoder builds and aligns global cross-modal correlations and models long-range contextual information with different modalities. The convolutional decoder decodes the encoding features to produce more precise recognition. Besides, a regularization term is introduced into the convolutional decoder to force the decoder to fully leverage the complete and incomplete data for emotional recognition of missing data. 96.33%, 95.64%, and 92.69% accuracies are attained on the available data of the DEAP and SEED-IV datasets, and 93.25%, 92.23%, and 81.76% accuracies are obtained on the missing data. Particularly, the model acquires a 5.61% advantage with 70% missing data, demonstrating that the model outperforms some state-of-the-art approaches in incomplete multi-modal learning.

A new multi-picture architecture for learned video deinterlacing and demosaicing with parallel deformable convolution and self-attention blocks

Despite the fact real-world video deinterlacing and demosaicing are well-suited to supervised learning from synthetically degraded data because the degradation models are known and fixed, learned video deinterlacing and demosaicing have received much less attention compared to denoising and super-resolution tasks. We propose a new multi-picture architecture for video deinterlacing or demosaicing by aligning multiple supporting pictures with missing data to a reference picture to be reconstructed, benefiting from both local and global spatio-temporal correlations in the feature space using modified deformable convolution blocks and a novel residual efficient top-k self-attention (kSA) block, respectively. Separate reconstruction blocks are used to estimate different types of missing data. Our extensive experimental results, on synthetic or real-world datasets, demonstrate that the proposed novel architecture provides superior results that significantly exceed the state-of-the-art for both tasks in terms of PSNR, SSIM, and perceptual quality. Ablation studies are provided to justify and show the benefit of each novel modification made to the deformable convolution and residual efficient kSA blocks. Code is available: https://github.com/KUIS-AI-Tekalp-Research-Group/Video-Deinterlacing.

Transformative skeletal motion analysis: optimization of exercise training and injury prevention through graph neural networks.

Exercise is pivotal for maintaining physical health in contemporary society. However, improper postures and movements during exercise can result in sports injuries, underscoring the significance of skeletal motion analysis. This research aims to leverage advanced technologies such as Transformer, Graph Neural Networks (GNNs), and Generative Adversarial Networks (GANs) to optimize sports training and mitigate the risk of injuries. The study begins by employing a Transformer network to model skeletal motion sequences, facilitating the capture of global correlation information. Subsequently, a Graph Neural Network is utilized to delve into local motion features, enabling a deeper understanding of joint relationships. To enhance the model's robustness and adaptability, a Generative Adversarial Network is introduced, utilizing adversarial training to generate more realistic and diverse motion sequences. In the experimental phase, skeletal motion datasets from various cohorts, including professional athletes and fitness enthusiasts, are utilized for validation. Comparative analysis against traditional methods demonstrates significant enhancements in specificity, accuracy, recall, and F1-score. Notably, specificity increases by ~5%, accuracy reaches around 90%, recall improves to around 91%, and the F1-score exceeds 89%. The proposed skeletal motion analysis method, leveraging Transformer and Graph Neural Networks, proves successful in optimizing exercise training and preventing injuries. By effectively amalgamating global and local information and integrating Generative Adversarial Networks, the method excels in capturing motion features and enhancing precision and adaptability. Future research endeavors will focus on further advancing this methodology to provide more robust technological support for healthy exercise practices.

PIS-Net: Efficient Medical Image Segmentation Network with Multivariate Downsampling for Point-of-Care

Recently, with more portable diagnostic devices being moved to people anywhere, point-of-care (PoC) imaging has become more convenient and more popular than the traditional “bed imaging”. Instant image segmentation, as an important technology of computer vision, is receiving more and more attention in PoC diagnosis. However, the image distortion caused by image preprocessing and the low resolution of medical images extracted by PoC devices are urgent problems that need to be solved. Moreover, more efficient feature representation is necessary in the design of instant image segmentation. In this paper, a new feature representation considering the relationships among local features with minimal parameters and a lower computational complexity is proposed. Since a feature window sliding along a diagonal can capture more pluralistic features, a Diagonal-Axial Multi-Layer Perceptron is designed to obtain the global correlation among local features for a more comprehensive feature representation. Additionally, a new multi-scale feature fusion is proposed to integrate nonlinear features with linear ones to obtain a more precise feature representation. Richer features are figured out. In order to improve the generalization of the models, a dynamic residual spatial pyramid pooling based on various receptive fields is constructed according to different sizes of images, which alleviates the influence of image distortion. The experimental results show that the proposed strategy has better performance on instant image segmentation. Notably, it yields an average improvement of 1.31% in Dice than existing strategies on the BUSI, ISIC2018 and MoNuSeg datasets.

Anthropogenic nest material use in a global sample of birds.

As humans increasingly modify the natural world, many animals have responded by changing their behaviour. Understanding and predicting the extent of these responses is a key step in conserving these species. For example, the tendency for some species of birds to incorporate anthropogenic items-particularly plastic material-into their nests is of increasing concern, as in some cases, this behaviour has harmful effects on adults, young and eggs. Studies of this phenomenon, however, have to date been largely limited in geographic and taxonomic scope. To investigate the global correlates of anthropogenic (including plastic) nest material use, we used Bayesian phylogenetic mixed models and a data set of recorded nest materials in 6147 species of birds. We find that, after controlling for research effort and proximity to human landscape modifications, anthropogenic nest material use is correlated with synanthropic (artificial) nesting locations, breeding environment and the number of different nest materials the species has been recorded to use. We also demonstrate that body mass, range size, conservation status and brain size do not explain variation in the recorded use of anthropogenic nest materials. These results indicate that anthropogenic materials are more likely to be included in nests when they are more readily available, as well as potentially by species that are more flexible in their nest material choice.

On the interplay between state-dependent reconfigurations of global signal correlation and BOLD fluctuations: An fMRI study

BackgroundThe dynamics of global, state-dependent reconfigurations in brain connectivity are yet unclear. We aimed at assessing reconfigurations of the global signal correlation coefficient (GSCORR), a measure of the connectivity between each voxel timeseries and the global signal, from resting-state to a stop-signal task. The secondary aim was to assess the relationship between GSCORR and blood-oxygen-level-dependent (BOLD) activations or deactivation across three different trial-conditions (GO, STOP-correct, and STOP-incorrect). MethodsAs primary analysis we computed whole-brain, voxel-wise GSCORR during resting-state (GSCORR-rest) and stop-signal task (GSCORR-task) in 107 healthy subjects aged 21–50, deriving GSCORR-shift as GSCORR-task minus GSCORR-rest. GSCORR-tr and trGSCORR-shift were also computed on the task residual time series to quantify the impact of the task-related activity during the trials. To test the secondary aim, brain regions were firstly divided in one cluster showing significant task-related activation and one showing significant deactivation across the three trial conditions. Then, correlations between GSCORR-rest/task/shift and activation/deactivation in the two clusters were computed. As sensitivity analysis, GSCORR-shift was computed on the same sample after performing a global signal regression and GSCORR-rest/task/shift were correlated with the task performance. ResultsSensory and temporo-parietal regions exhibited a negative GSCORR-shift. Conversely, associative regions (ie. left lingual gyrus, bilateral dorsal posterior cingulate gyrus, cerebellum areas, thalamus, posterolateral parietal cortex) displayed a positive GSCORR-shift (FDR-corrected p < 0.05). GSCORR-shift showed similar patterns to trGSCORR-shift (magnitude increased) and after global signal regression (magnitude decreased). Concerning BOLD changes, Brodmann area 6 and inferior parietal lobule showed activation, while posterior parietal lobule, cuneus, precuneus, middle frontal gyrus showed deactivation (FDR-corrected p < 0.05). No correlations were found between GSCORR-rest/task/shift and beta-coefficients in the activation cluster, although negative correlations were observed between GSCORR-task and GO/STOP-correct deactivation (Pearson rho=-0.299/-0.273; Bonferroni-p < 0.05). Weak associations between GSCORR and task performance were observed (uncorrected p < 0.05). ConclusionGSCORR state-dependent reconfiguration indicates a reallocation of functional resources to associative areas during stop-signal task. GSCORR, activation and deactivation may represent distinct proxies of brain states with specific neurofunctional relevance.

HRU-TNet: Hybrid Residual U-Transformer Network for diabetic retinopathy multi-lesion segmentation

The automatic segmentation of diabetic retinopathy (DR) holds significant importance for assisting physicians in diagnosis and treatment. Given the complexity, high inter-class similarity, and uncertainty of DR, it is crucial to integrate multiscale information between lesions and establish global correlations among them. To address these issues, a novel HRU-TNet (Hybrid Residual U-Transformer Network) algorithm for retinal lesion segmentation is proposed. In this framework, the network is augmented with lightweight self-attention residual U-modules (LSA-RSU) to capture high-frequency details of the lesions and global contextual information. The skip connections are then enhanced through interactive residual transformer fusion modules (IRTF) and channel-cross attention (CCA), promoting dependencies among features at different scales and filtering out interfering information to guide feature fusion and eliminate ambiguity. Additionally, a novel retinal image enhancement technique is devised, employing local wavelet transformations to capture detailed components of the retinal images, thereby enhancing the representational capacity of the segmentation network. Data augmentation is also performed to ensure network adaptability to small datasets. Comprehensive experiments conducted on the publicly available IDRID and e_ophtha datasets yielded average AUC_PR values of 0.709 and 0.451, respectively. The proposed approach demonstrated superior generalization on the DDR dataset compared to other methods mentioned in the literature. These results demonstrate that our proposed method is better suited for small retinal datasets, exhibiting improved segmentation accuracy and generalization compared to existing approaches.

Reference Section of the Campanian Stage of the Southwestern Crimea: Problems of Substage Subdivision and Global Correlation

Reference Section of the Campanian Stage of the Southwestern Crimea: Problems of Substage Subdivision and Global Correlation

Data-unbalanced traffic accident prediction via adaptive graph and self-supervised learning

Traffic accident prediction is an important research problem, which can help to identify dangerous situations on the road in advance and take appropriate measures. Nonetheless, real-world traffic accident data suffers from a significant data unbalance problem, as accident occurrences vary unevenly in both spatial and data domains. This unbalance can easily lead to the prediction methods biased towards the side with more data. Recently, researchers have proposed a series of effective prediction methods based on deep learning and graph theory. Existing graph-based methods always adopt the predefined distance graph. However, these methods cannot fully capture the spatial correlations among regions that are far away from each other but share similar accident patterns. To address these challenges, we propose a traffic accident prediction method that combines Adaptive Graphs with Self-Supervised Learning (AGSSL). In the proposed method, we can adaptively construct graph structures to learn global spatial correlations among urban regions. Meanwhile, two self-supervised learning modules called Graph Infomax and Focal Contrastive Regularization are used to learn a robust representation of traffic accidents data under an unbalanced distribution. Experiment results show that AGSSL outperforms SOTA methods in traffic accident prediction.

Geochronological constraints on the Hangenberg Event of the latest Devonian in South China

The Hangenberg Event was one of the most significant mass extinctions accompanied by large-scale climate changes at the end of the Devonian period. Lack of precise and accurate geochronological constraints in the eastern Paleotethys has hindered a high-resolution global correlation of the Hangenberg Event. Here, we report the U–Pb dates of zircons from the bentonite samples intercalated with strata recording the Hangenberg Event in the Muhua region, Guizhou, South China, using both sensitive high-resolution ion microprobe (SHRIMP) and chemical abrasion - isotope dilution - thermal ionization mass spectrometry (CA-ID-TIMS) methods. A bentonite within the Hangenberg Event strata in the Muhua area was dated at 360.47 ± 0.68 Ma by CA-ID-TIMS, which is more precise than the results obtained by SHRIMP methods (361.5 ± 2.9 Ma and 359.8 ± 1.5 Ma). Thus, we propose that the Hangenberg Event took place at 360.47 ± 0.68 Ma in South China, based on the first precise CA-ID-TIMS zircon age from the Daposhang section. The age obtained from South China for the Devonian–Carboniferous boundary overlaps with that in Geologic Time Scale 2020, within the total uncertainties.

Generalized Venous Prominence on Susceptibility-Weighted Imaging Correlates With Global Cerebral Blood Flow Decline.

Objective This study investigated the global correlation between cerebral blood flow (CBF) decline and increased venous prominence, utilizing arterial spin labeling (ASL) and susceptibility-weighted imaging (SWI) MRI techniques. Methods The study was conducted at the Department of Diagnostic Imaging, St. Marina University Hospital, Varna, Bulgaria. Through a retrospective analysis, we examined data from 115 patients undergoing neurological assessment. CBF decline was assessed through ASL MRI, while global venous visibility was evaluated using SWI MRI. Results The analysis revealed a significant positive correlation between CBF decline and venous prominence (Spearman's rho = 0.261, p = 0.005), indicating a systemic interaction between cerebral perfusion and the venous system. Logistic regression further underscored CBF decline as a significant predictive factor for increased venous visibility (odds ratio (OR) = 1.690, p = 0.004). The assessments' high inter-rater reliability (Cohen's kappa = 0.82) supports the consistency and validity of our findings. Conclusion The integration of ASL and SWI MRI provides critical insights into cerebral hemodynamics, emphasizing the significance of these imaging modalities in both neurovascular research and clinical practice. Our findings suggest a systemic relationship between CBF decline and venous system alterations, underscoring the potential for these techniques to enhance our understanding of neurovascular disorders. Future studies should pursue longitudinal and quantitative analyses to deepen our comprehension of these relationships and their clinical implications.

DNA methylation haplotype block signatures responding to Staphylococcus aureus subclinical mastitis and association with production and health traits

BackgroundDNA methylation has been documented to play vital roles in diseases and biological processes. In bovine, little is known about the regulatory roles of DNA methylation alterations on production and health traits, including mastitis.ResultsHere, we employed whole-genome DNA methylation sequencing to profile the DNA methylation patterns of milk somatic cells from sixteen cows with naturally occurring Staphylococcus aureus (S. aureus) subclinical mastitis and ten healthy control cows. We observed abundant DNA methylation alterations, including 3,356,456 differentially methylated cytosines and 153,783 differential methylation haplotype blocks (dMHBs). The DNA methylation in regulatory regions, including promoters, first exons and first introns, showed global significant negative correlations with gene expression status. We identified 6435 dMHBs located in the regulatory regions of differentially expressed genes and significantly correlated with their corresponding genes, revealing their potential effects on transcriptional activities. Genes harboring DNA methylation alterations were significantly enriched in multiple immune- and disease-related pathways, suggesting the involvement of DNA methylation in regulating host responses to S. aureus subclinical mastitis. In addition, we found nine discriminant signatures (differentiates cows with S. aureus subclinical mastitis from healthy cows) representing the majority of the DNA methylation variations related to S. aureus subclinical mastitis. Validation of seven dMHBs in 200 cows indicated significant associations with mammary gland health (SCC and SCS) and milk production performance (milk yield).ConclusionsIn conclusion, our findings revealed abundant DNA methylation alterations in milk somatic cells that may be involved in regulating mammary gland defense against S. aureus infection. Particularly noteworthy is the identification of seven dMHBs showing significant associations with mammary gland health, underscoring their potential as promising epigenetic biomarkers. Overall, our findings on DNA methylation alterations offer novel insights into the regulatory mechanisms of bovine subclinical mastitis, providing further avenues for the development of effective control measures.Graphical

Genetic Links Between Metabolic Syndrome and Osteoarthritis: Insights from Cross-Trait Analysis.

Previous observational studies have indicated a bidirectional association between metabolic syndrome (MetS) and osteoarthritis (OA). However, it remains unclear whether these bidirectional associations reflect causal relationships or shared genetic factors, and the underlying biological mechanisms of this association are not fully understood. Leveraging summary statistics from genome-wide association studies (GWASs) conducted by the UK Biobank and the Glucose and Insulin-related Traits Consortium (MAGIC), we performed global genetic correlation analyses, genome-wide cross-trait meta-analyses, and a bidirectional two-sample Mendelian randomization analyses using summary statistics from GWASs to comprehensively assess the relationship of MetS and OA. We first detected an extensive genetic correlation between MetS and OA (rg=0.393, P=1.52×10-18), which was consistent in four MetS components, including waist circumference, triglycerides, hypertension and high-density lipoprotein cholesterol and OA with rg ranging from -0.229 to 0.490. We then discovered 32 variants jointly associated with MetS and OA through multi-trait Analysis of GWAS. Co-localization analysis founded 12 genes shared between MetS and OA, with functional implications in several biological pathways. Finally, MR analysis suggested genetic liability to MetS significantly increased the risk of OA, but no reverse causality was found. Our results illustrate a common genetic architecture, pleiotropic loci, as well as causality between MetS and OA, potentially enhancing our knowledge of high comorbidity and genetic processes that overlap between the two disorders.