Traditional art design expression based on embedded system development.

This article describes constructing an embedded system for a painting art and style presentation platform, achieving the automatic integration of digital painting art with traditional art design. The frontend components are designed using the Bootstrap framework, with Django as the web development framework and TensorFlow architecture integrated into the code. Furthermore, the Inception module and residual connections are introduced to optimize the visual geometry group (VGG) network for recognizing and analyzing image texture features. Compared to other models, experimental results indicate that the proposed model demonstrates a 2.6% increase in image style classification accuracy, reaching 87.34% and 95.33% in architectural and landscape image classification, respectively. The system's operational outcomes reveal that the proposed platform alleviates the burden on the logical function modules of the system, enhances scalability, and promotes the automated fusion of digital painting art with traditional art design expression.

Myeloid ectopic viral integration site 2 accelerates the progression of Alzheimer's disease.

Amyloid plaques, a major pathological hallmark of Alzheimer's disease (AD), are caused by an imbalance between the amyloidogenic and non-amyloidogenic pathways of amyloid precursor protein (APP). BACE1 cleavage of APP is the rate-limiting step for amyloid-ß production and plaque formation in AD. Although the alteration of BACE1 expression in AD has been investigated, the underlying mechanisms remain unknown. In this study, we determined MEIS2 was notably elevated in AD models and AD patients. Alterations in the expression of MEIS2 can modulate the levels of BACE1. MEIS2 downregulation improved the learning and memory retention of AD mice and decreased the number of amyloid plaques. MEIS2 binds to the BACE1 promoter, positively regulates BACE1 expression, and accelerates APP amyloid degradation in vitro. Therefore, our findings suggest that MEIS2 might be a critical transcription factor in AD, since it regulates BACE1 expression and accelerates BACE1-mediated APP amyloidogenic cleavage. MEIS2 is a promising early intervention target for AD treatment.

Development of a long-term care service integration self-assessment tool.

The World Health Organization emphasizes the importance of providing integrated care for older people. Taiwan is the fastest aging country in the world. In 2016, Taiwan implemented the Long-Term Care Plan 2.0 (TLTCP 2.0), aimed at providing integrated long-term care (LTC) services in communities. However, LTC service agencies have not been able to evaluate the level of integrated care they provide due to the lack of an effective assessment tool. To address this need, this study sets out to develop an integration assessment tool, namely the Self-Assessment for Service Integration in Long-Term Care (SASI-LTC), which will allow LTC agencies to self-evaluate their current level of integration from multiple perspectives. The SASI-LTC was developed based on Evashwick's framework, underwent two rounds of Delphi panels with twenty-six experts, and a pilot test with 243 valid questionnaires from administrators of Tier A agencies who are responsible for integrating LTC. The Delphi experts assessed the content with high levels of agreement using medians, the scale content validity index (SCVI) and item content validity index (ICVI). The SASI-LTC included four domains (inter-entity organization and management, integrated care coordination, integrated resources, and integrated information systems) with thirty items. The SASI-LTC showed good reliability (Cronbach's α = 0.94) and good validity, and a confirmatory factor analysis showed a good model fit index [χ2/df = 1.38; RMSEA = 0.040; CFI = 0.963; SRMR = 0.049] in pilot testing. While the SASI-LTC is a useful and feasible tool for Taiwan's LTC service agencies to evaluate their level of integration in providing LTC services, it could also be used in other countries with minor adjustments to localization of items related to financial integration.

FreqSNet: a multiaxial integration of frequency and spatial domains for medical image segmentation.

Objective.In recent years, convolutional neural networks, which typically focus on extracting spatial domain features, have shown limitations in learning global contextual information. However, frequency domain can offer a global perspective that spatial domain methods often struggle to capture. To address this limitation, we propose FreqSNet, which leverages both frequency and spatial features for medical image segmentation.Approach.To begin, we propose a frequency-space representation aggregation block (FSRAB) to replace conventional convolutions. FSRAB contains three frequency domain branches to capture global frequency information along different axial combinations, while a convolutional branch is designed to interact information across channels in local spatial features. Secondly, the multiplex expansion attention block extracts long-range dependency information using dilated convolutional blocks, while suppressing irrelevant information via attention mechanisms. Finally, the introduced Feature Integration Block enhances feature representation by integrating semantic features that fuse spatial and channel positional information.Main results.We validated our method on 5 public datasets, including BUSI, CVC-ClinicDB, CVC-ColonDB, ISIC-2018, and Luna16. On these datasets, our method achieved Intersection over Union (IoU) scores of 75.46%, 87.81%, 79.08%, 84.04%, and 96.99%, and Hausdorff distance values of 22.22 mm, 13.20 mm, 13.08 mm, 13.51 mm, and 5.22 mm, respectively. Compared to other state-of-the-art methods, our FreqSNet achieves better segmentation results.Significance.Our method can effectively combine frequency domain information with spatial domain features, enhancing the segmentation performance and generalization capability in medical image segmentation tasks.

Correlations between C-myc expression, BMI-1 expression, and vaginal microecology with HPV-DNA load in patients with different cervical lesions.

OBJECTIVE: To investigate the correlations between the expressions of proto-oncogenes C-myc and B-cell-specific Moloney leukemia virus integration site-1 (BMI-1), vaginal microecology, and human papillomavirus-DNA (HPV-DNA) load in patients with different cervical lesions. METHODS: A total of 51 patients with cervix squamous cell carcinoma (CSCC), 72 patients with cervical intraepithelial neoplasia (CIN) and 50 patients with normal cervix (NC) who were diagnosed or admitted between Jan. 1st 2020 and Dec. 31st 2022 at the Suzhou Hospital of Integrated Traditional Chinese and Western Medicine were selected and divided into three groups, i.e., the CSCC group, the CIN group and the NC group, for a retrospective analysis. Hybrid capture 2 (hc2) was used to detect the HPV-DNA load in each group. Immunohistochemistry was performed to detect C-myc and BMI-1 expressions in each group. The indicators of vaginal microecology in patients were compared among groups to analyze the correlations between C-myc, BMI-1 expressions, vaginal microecology and HPV-DNA load. RESULTS: The HPV-DNA load and expression levels of positive C-myc and BMI-1 in the CSCC group were all higher than those of the CIN and NC groups (P<0.05). The detection rate of lactobacillus in the CSCC group was lower than that of the CIN and NC groups. The percentages of leukocyte esterase (LE) positivity and pH ≥4.6 were higher in the CSCC group than those in the CIN and NC groups (P<0.05). The difference in the detection rate of spores among the three groups was not significant (P>0.05). Both C-myc and BMI-1 scores were positively correlated with HPV-DNA load in the 173 samples. CONCLUSION: The proto-oncogenes C-myc and BMI-1 were highly expressed in the cervical tissues of CIN and CSCC patients, whose vaginal microecology was also altered. Both may play an important role in the progression of cervical lesions.

Screening and characterization of integration sites based on CRISPR-Cpf1 in Pichia pastoris.

Pichia pastoris, a methylotrophic yeast, can utilize methanol as a carbon source and energy source to synthesize high-value chemicals, and is an ideal host for biomanufacturing. Constructing the P. pastoris cell factory is somewhat impeded due to the absence of genetic tools for manipulating multi-gene biosynthetic pathways. To broaden its application in the field of metabolic engineering, this study identified and screened 15 novel integration sites in P. pastoris using CRISPR-Cpf1 genome editing technology, with EGFP serving the reporter protein. These integration sites have integration efficiencies of 10-100 % and varying expression strengths, which allow for selection based on the expression levels of genes as needed. Additionally, these integrated sites are applied in the heterologous biosynthesis of P. pastoris, such as the astaxanthin biosynthetic pathway and the carbon dioxide fixation pathway of the Calvin-Benson-Bassham (CBB) cycle. During the three-site integration process, the 8 genes of the CBB cycle were integrated into the genome of P. pastoris. This indicates the potential of these integration sites for integrating large fragments and suggests their successful application in metabolic engineering of P. pastoris. This may lead to improved efficiency of genetic engineering in P. pastoris.

DeepIDA-GRU: a deep learning pipeline for integrative discriminant analysis of cross-sectional and longitudinal multiview data with applications to inflammatory bowel disease classification.

Biomedical research now commonly integrates diverse data types or views from the same individuals to better understand the pathobiology of complex diseases, but the challenge lies in meaningfully integrating these diverse views. Existing methods often require the same type of data from all views (cross-sectional data only or longitudinal data only) or do not consider any class outcome in the integration method, which presents limitations. To overcome these limitations, we have developed a pipeline that harnesses the power of statistical and deep learning methods to integrate cross-sectional and longitudinal data from multiple sources. In addition, it identifies key variables that contribute to the association between views and the separation between classes, providing deeper biological insights. This pipeline includes variable selection/ranking using linear and nonlinear methods, feature extraction using functional principal component analysis and Euler characteristics, and joint integration and classification using dense feed-forward networks for cross-sectional data and recurrent neural networks for longitudinal data. We applied this pipeline to cross-sectional and longitudinal multiomics data (metagenomics, transcriptomics and metabolomics) from an inflammatory bowel disease (IBD) study and identified microbial pathways, metabolites and genes that discriminate by IBD status, providing information on the etiology of IBD. We conducted simulations to compare the two feature extraction methods.

A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques.

BACKGROUND: The growing abundance of in vitro omics data, coupled with the necessity to reduce animal testing in the safety assessment of chemical compounds and even eliminate it in the evaluation of cosmetics, highlights the need for adequate computational methodologies. Data from omics technologies allow the exploration of a wide range of biological processes, therefore providing a better understanding of mechanisms of action (MoA) related to chemical exposure in biological systems. However, the analysis of these large datasets remains difficult due to the complexity of modulations spanning multiple biological processes. RESULTS: To address this, we propose a strategy to reduce information overload by computing, based on transcriptomics data, a comprehensive metabolic sub-network reflecting the metabolic impact of a chemical. The proposed strategy integrates transcriptomic data to a genome scale metabolic network through enumeration of condition-specific metabolic models hence translating transcriptomics data into reaction activity probabilities. Based on these results, a graph algorithm is applied to retrieve user readable sub-networks reflecting the possible metabolic MoA (mMoA) of chemicals. This strategy has been implemented as a three-step workflow. The first step consists in building cell condition-specific models reflecting the metabolic impact of each exposure condition while taking into account the diversity of possible optimal solutions with a partial enumeration algorithm. In a second step, we address the challenge of analyzing thousands of enumerated condition-specific networks by computing differentially activated reactions (DARs) between the two sets of enumerated possible condition-specific models. Finally, in the third step, DARs are grouped into clusters of functionally interconnected metabolic reactions, representing possible mMoA, using the distance-based clustering and subnetwork extraction method. The first part of the workflow was exemplified on eight molecules selected for their known human hepatotoxic outcomes associated with specific MoAs well described in the literature and for which we retrieved primary human hepatocytes transcriptomic data in Open TG-GATEs. Then, we further applied this strategy to more precisely model and visualize associated mMoA for two of these eight molecules (amiodarone and valproic acid). The approach proved to go beyond gene-based analysis by identifying mMoA when few genes are significantly differentially expressed (2 differentially expressed genes (DEGs) for amiodarone), bringing additional information from the network topology, or when very large number of genes were differentially expressed (5709 DEGs for valproic acid). In both cases, the results of our strategy well fitted evidence from the literature regarding known MoA. Beyond these confirmations, the workflow highlighted potential other unexplored mMoA. CONCLUSION: The proposed strategy allows toxicology experts to decipher which part of cellular metabolism is expected to be affected by the exposition to a given chemical. The approach originality resides in the combination of different metabolic modelling approaches (constraint based and graph modelling). The application to two model molecules shows the strong potential of the approach for interpretation and visual mining of complex omics in vitro data. The presented strategy is freely available as a python module ( https://pypi.org/project/manamodeller/ ) and jupyter notebooks ( https://github.com/LouisonF/MANA ).

The impact of market integration on industrial green transformation: A case study of the yangtze river delta in China.

Regional integration plays an important role in dicarbon reduction and sustainability development. Based on dynamic panel model and spatial econometric model, this study analyzes the impact of market integration (MI) on industrial green transformation (IGT). The study finds that: (1) MI has a nonlinear relationship with the IGT. With the increasing of MI degree, the IGT shows the characteristic of first rising and then declining. (2) MI plays a role in the IGT mainly through industrial agglomeration, resource mismatch and green technological progress, respectively. (3) The impact of MI on IGT exhibits spatial heterogeneity. Although MI significantly promotes IGT in core cities, its impact on the IGT in peripheral cities is not significant. (4) The IGT has spatial spillover effect, and the improvement of MI degree in adjacent regions can also promote the IGT in the cities.

The new frontier in CHO cell line development: From random to targeted transgene integration technologies.

Cell line development represents a crucial step in the development process of a therapeutic glycoprotein. Chinese hamster ovary (CHO) cells are the most frequently employed mammalian host cell system for the industrial manufacturing of biologics. The predominant application of CHO cells for heterologous recombinant protein expression lies in the relative simplicity of stably introducing ectopic DNA into the CHO host cell genome. Since CHO cells were first used as expression host for the industrial production of biologics in the late 1980s, stable genomic transgene integration has been achieved almost exclusively by random integration. Since then, random transgene integration had become the gold standard for generating stable CHO production cell lines due to a lack of viable alternatives. However, it was eventually demonstrated that this approach poses significant challenges on the cell line development process such as an increased risk of inducing cell line instability. In recent years, significant discoveries of new and highly potent (semi)-targeted transgene integration systems have paved the way for a technological revolution in the cell line development sector. These advanced methodologies comprise the application of transposase-, recombinase- or Cas9 nuclease-mediated site-specific genomic integration techniques, which enable a scarless transfer of the transgene expression cassette into transcriptionally active loci within the host cell genome. This review summarizes recent advancements in the field of transgene integration technologies for CHO cell line development and compare them to the established random integration approach. Moreover, advantages and limitations of (semi)-targeted integration techniques are discussed, and benefits and opportunities for the biopharmaceutical industry are outlined.

Social integration and long-term physical and psychosocial quality of life among prostate cancer survivors in the Health Professionals Follow-up Study.

PURPOSE: Prostate cancer survivors may benefit from a supportive social environment. We investigated associations of social integration and long-term physical and psychosocial quality of life among prostate cancer survivors who were participants in the Health Professionals Follow-up Study. METHODS: We included 1,428 individuals diagnosed with non-metastatic prostate cancer between 2008 and 2016. Social integration was measured by the Berkman-Syme Social Network Index (SNI) and marital status. We fit generalized linear mixed effect models for associations of SNI and marital status with patient reported outcome measures on physical and psychosocial quality of life captured between 2008 and 2020, adjusting for age, race, employment status, body mass index, comorbidities, smoking history, and clinical factors. RESULTS: Among those with baseline SNI (N = 1,362), 46.4% were socially integrated, 20.3% were moderately integrated, 27.4% were moderately isolated, and 5.9% were socially isolated. Among those reporting baseline marital status (N = 1,428), 89.5% were married. Socially integrated survivors (vs. socially isolated) reported fewer depressive signs and better psychosocial wellbeing. Physical quality of life did not differ by social integration. Married survivors (vs. not married) reported fewer urinary symptoms, but there were no differences in bowel, sexual, or vitality/hormonal symptoms. CONCLUSIONS: Among prostate cancer survivors, being socially integrated was associated with fewer depressive signs and better psychosocial wellbeing, and married prostate cancer survivors had fewer urinary symptoms. IMPLICATIONS FOR CANCER SURVIVORS: This study highlighted aspects of long-term physical and psychosocial quality of life that are more favorable among prostate cancer survivors with a supportive social environment.

Rapid generation of single-insertion transgenics by Tol2 transposition in zebrafish.

BACKGROUND: The Tol2 transposable element is the most widely used transgenesis tool in zebrafish. However, its high activity almost always leads to multiple unlinked integrations of the transgenic cassette in F1 fish. Each of these transgenes is susceptible to positional effects from the surrounding regulatory landscape, which can lead to altered expression and, consequently, activity. Scientists therefore must strike a balance between the need to maximize reproducibility by establishing single-insertion transgenic lines and the need to complete experiments within a reasonable timeframe. RESULTS: In this article, we introduce a simple competitive dilution strategy for rapid generation of single-insertion transgenics. By using cry:BFP reporter plasmid as a competitor, we achieved a nearly fourfold reduction in the number of the transgene of interest integrations while simultaneously increasing the proportion of single-insertion F1 generation transgenics to over 50%. We also observed variations in transgene of interest expression among independent single-insertion transgenics, highlighting that the commonly used ubiquitous ubb promoter is susceptible to position effects. CONCLUSIONS: Wide application of our competitive dilution strategy will save time, reduce animal usage, and improve reproducibility of zebrafish research.

Leveraging In Situ Simulation for Implementation of Teleobstetric Consultation Services in Rural and Community Hospitals.

Background: Our institution implemented acute-care obstetric (OB) telemedicine (TeleOB) to address rural disparities across our health system. We sought to determine whether in situ simulations with embedded TeleOB consultation increase participants' comfort managing OB emergencies and comfort with and likelihood of using TeleOB. Methods: Rural site care teams participated in multidisciplinary in situ OB emergency simulations. Physicians in OB and neonatology at the referral center assisted via telemedicine consultation. Participants were surveyed before and after the simulations and six months later regarding their experience during the simulations. Results: Participants reported increased comfort with TeleOB activation, indications, and workflow processes, as well as increased comfort managing OB emergencies. Participants also reported significantly increased likelihood of using TeleOB in the future. Conclusions: Consistent with previous work, in situ simulation with embedded telemedicine consultations is an effective approach to facilitate telemedicine implementation and promote use by rural clinicians.

Achromatic CMOS-Integrated Four-Bit Orbital Angular Momentum Mode Detector at Three Wavelengths.

The simultaneous detection of the orbital angular momentum (OAM) and wavelength offers new opportunities for optical multiplexing. However, because of the dispersion of lens functions for Fourier transformation, the mode conversions at distinct wavelengths cannot be achieved in the same plane. Here we propose an ultracompact achromatic complementary metal oxide semiconductor (CMOS)-integrated OAM mode detector. Specifically, a spatial multiplexed scheme, randomly interleaving the phase distributions for distributing the superposed OAM modes into preset positions at distinct wavelengths, is presented. In addition, such a nanoprinted achromatic OAM detector featuring a microscale size and a short focal length can be integrated onto a CMOS chip. Consequently, the four-bit incident light beams at three discrete wavelengths (633, 532, and 488 nm) can be distinguished with a high degree of accuracy evaluated by the average standardized Euclidean distance of ∼0.75 between the analytical and target results. Our results showcase a miniaturized platform for achieving high-capacity information processing.

Velocity dependence of sensory reweighting in human balance control.

The relative contributions of proprioceptive, vestibular, and visual sensory cues to balance control change depending on their availability and reliability. This sensory reweighting is classically supported by non-linear sway responses to increasing visual surround and/or surface tilt amplitudes. However, recent evidence indicates that visual cues are reweighted based on visual tilt velocity rather than tilt amplitude. Therefore, we designed a study to specifically test the hypothesized velocity dependence of reweighting while expanding on earlier findings for visual reweighting by testing proprioceptive reweighting for standing balance on a tilting surface. Twenty healthy young adults stood with their eyes closed on a toes-up/-down tilting platform. We designed four pseudo-random tilt sequences with either a slow (S) or a fast (F) tilt velocity and different peak-to-peak amplitudes. We used model-based interpretations of measured sway characteristics to estimate the proprioceptive sensory weight (Wprop) within each trial. Additionally, root-mean-square values of measured body centre of mass sway amplitude (RMS) and velocity (RMSv) were calculated for each tilt sequence. Wprop, RMS, and RMSv values varied depending on the stimulus velocity, exhibiting large effects (all Cohen's d's > 1.10). In contrast, we observed no significant differences across stimulus amplitudes for Wprop (Cohen's d's: 0.02-0.16) and, compared to the differences in velocity, there were much smaller changes in RMS and RMSv values (Cohen's d's: 0.05 - 0.91). These results confirmed the hypothesized velocity, rather than amplitude, dependence of sensory reweighting.

Thermally Oxidized Memristor and 1T1R Integration for Selector Function and Low-Power Memory.

Resistive switching memories have garnered significant attention due to their high-density integration and rapid in-memory computing beyond von Neumann's architecture. However, significant challenges are posed in practical applications with respect to their manufacturing process complexity, a leakage current of high resistance state (HRS), and the sneak-path current problem that limits their scalability. Here, a mild-temperature thermal oxidation technique for the fabrication of low-power and ultra-steep memristor based on Ag/TiOx/SnOx/SnSe2/Au architecture is developed. Benefiting from a self-assembled oxidation layer and the formation/rupture of oxygen vacancy conductive filaments, the device exhibits an exceptional threshold switching behavior with high switch ratio exceeding 106, low threshold voltage of ≈1 V, long-term retention of >104 s, an ultra-small subthreshold swing of 2.5 mV decade-1 and high air-stability surpassing 4 months. By decreasing temperature, the device undergoes a transition from unipolar volatile to bipolar nonvolatile characteristics, elucidating the role of oxygen vacancies migration on the resistive switching process. Further, the 1T1R structure is established between a memristor and a 2H-MoTe2 transistor by the van der Waals (vdW) stacking approach, achieving the functionality of selector and multi-value memory with lower power consumption. This work provides a mild-thermal oxidation technology for the low-cost production of high-performance memristors toward future in-memory computing applications.

Fluctuation of fine motor skills throughout the menstrual cycle in women.

The effect of the menstrual cycle on fine motor skills is unclear. This study determined whether the menstrual cycle affected fine motor skills and related neural activities. Nineteen women with regular menstrual cycles were tested for fine motor skills using two types of tasks: grooved pegboard task (GPT), which evaluates motor control with high freedom of movements, and force modulation task (FMT), which evaluates more complex and fine motor control with low freedom of movements. We also assessed primary motor cortex intracortical circuits and sensorimotor integration using paired-pulse transcranial magnetic stimulation to reveal why the menstrual cycle affects fine motor skills. The present study indicated that fine motor skills assessed by FMT varied throughout the menstrual cycle while those measured by GPT did not. These results suggest that fine motor skills requiring more complex and fine control may be affected by the menstrual cycle. Additionally, changes in fine motor skills throughout the menstrual cycle may be associated with the severity of menstruation-related symptoms.

A reduced vector model predictive controller for a three-level neutral point clamped inverter with common-mode voltage suppression.

This paper presents a novel, state-of-the-art predictive control architecture that addresses the computational complexity and limitations of conventional predictive control methodologies while enhancing the performance efficacy of predictive control techniques applied to three-level voltage source converters (NPC inverters). This framework's main goal is to decrease the number of filtered voltage lifespan vectors in each sector, which will increase the overall efficiency of the control system and allow for common mode voltage reduction in three-level voltage source converters. Two particular tactics are described in order to accomplish this. First, a statistical approach is presented for the proactive detection of potential voltage vectors, with an emphasis on selecting and including the vectors that are most frequently used. This method lowers the computational load by limiting the search space needed to find the best voltage vectors. Then, using statistical analysis, a plan is presented to split the sectors into two separate parts, so greatly limiting the number of voltage vectors. The goal of this improved predictive control methodology is to reduce computing demands and mitigate common mode voltage. The suggested strategy's resilience is confirmed in a range of operational scenarios using simulations and empirical evaluation. The findings indicate a pronounced enhancement in computational efficiency and a notable diminution in common mode voltage, thereby underscoring the efficacy of the proposed methodology. This increases their ability to incorporate renewable energy sources into the electrical grid.

Unveiling the molecular landscape of cognitive aging: insights from polygenic risk scores, DNA methylation, and gene expression.

BACKGROUND: Aging represents a significant risk factor for the occurrence of cerebral small vessel disease, associated with white matter (WM) lesions, and to age-related cognitive alterations, though the precise mechanisms remain largely unknown. This study aimed to investigate the impact of polygenic risk scores (PRS) for WM integrity, together with age-related DNA methylation, and gene expression alterations, on cognitive aging in a cross-sectional healthy aging cohort. The PRSs were calculated using genome-wide association study (GWAS) summary statistics for magnetic resonance imaging (MRI) markers of WM integrity, including WM hyperintensities, fractional anisotropy (FA), and mean diffusivity (MD). These scores were utilized to predict age-related cognitive changes and evaluate their correlation with structural brain changes, which distinguish individuals with higher and lower cognitive scores. To reduce the dimensionality of the data and identify age-related DNA methylation and transcriptomic alterations, Sparse Partial Least Squares-Discriminant Analysis (sPLS-DA) was used. Subsequently, a canonical correlation algorithm was used to integrate the three types of omics data (PRS, DNA methylation, and gene expression data) and identify an individual "omics" signature that distinguishes subjects with varying cognitive profiles. RESULTS: We found a positive association between MD-PRS and long-term memory, as well as a correlation between MD-PRS and structural brain changes, effectively discriminating between individuals with lower and higher memory scores. Furthermore, we observed an enrichment of polygenic signals in genes related to both vascular and non-vascular factors. Age-related alterations in DNA methylation and gene expression indicated dysregulation of critical molecular features and signaling pathways involved in aging and lifespan regulation. The integration of multi-omics data underscored the involvement of synaptic dysfunction, axonal degeneration, microtubule organization, and glycosylation in the process of cognitive aging. CONCLUSIONS: These findings provide valuable insights into the biological mechanisms underlying the association between WM coherence and cognitive aging. Additionally, they highlight how age-associated DNA methylation and gene expression changes contribute to cognitive aging.