Accelerating the design of pili-enabled living materials using an integrative technological workflow.

Bacteria can be programmed to create engineered living materials (ELMs) with self-healing and evolvable functionalities. However, further development of ELMs is greatly hampered by the lack of engineerable nonpathogenic chassis and corresponding programmable endogenous biopolymers. Here, we describe a technological workflow for facilitating ELMs design by rationally integrating bioinformatics, structural biology and synthetic biology technologies. We first develop bioinformatics software, termed Bacteria Biopolymer Sniffer (BBSniffer), that allows fast mining of biopolymers and biopolymer-producing bacteria of interest. As a proof-of-principle study, using existing pathogenic pilus as input, we identify the covalently linked pili (CLP) biosynthetic gene cluster in the industrial workhorse Corynebacterium glutamicum. Genetic manipulation and structural characterization reveal the molecular mechanism of the CLP assembly, ultimately enabling a type of programmable pili for ELM design. Finally, engineering of the CLP-enabled living materials transforms cellulosic biomass into lycopene by coupling the extracellular and intracellular bioconversion ability.

Relationships between brain structure-function coupling in normal aging and cognition: A cross-ethnicity population-based study.

Increased efforts in neuroscience seek to understand how macro-anatomical and physiological connectomes cooperatively work to generate cognitive behaviors. However, the structure-function coupling characteristics in normal aging individuals remain unclear. Here, we developed an index, the Coupling in Brain Structural connectome and Functional connectome (C-BSF) index, to quantify regional structure-function coupling in a large community-based cohort. C-BSF used diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) data from the Polyvascular Evaluation for Cognitive Impairment and Vascular Events study (PRECISE) cohort (2007 individuals, age: 61.15 ± 6.49 years) and the Sydney Memory and Ageing Study (MAS) cohort (254 individuals, age: 83.45 ± 4.33 years). We observed that structure-function coupling was the strongest in the visual network and the weakest in the ventral attention network. We also observed that the weaker structure-function coupling was associated with increased age and worse cognitive level of the participant. Meanwhile, the structure-function coupling in the visual network was associated with the visuospatial performance and partially mediated the connections between age and the visuospatial function. This work contributes to our understanding of the underlying brain mechanisms by which aging affects cognition and also help establish early diagnosis and treatment approaches for neurological diseases in the elderly.

Dynamic evolution of frontal-temporal network connectivity in temporal lobe epilepsy: A magnetoencephalography study.

Temporal lobe epilepsy (TLE) frequently involves an intricate, extensive epileptic frontal-temporal network. This study aimed to investigate the interactions between temporal and frontal regions and the dynamic patterns of the frontal-temporal network in TLE patients with different disease durations. The magnetoencephalography data of 36 postoperative seizure-free patients with long-term follow-up of at least 1 year, and 21 age- and sex-matched healthy subjects were included in this study. Patients were initially divided into LONG-TERM (n = 18, DURATION >10 years) and SHORT-TERM (n = 18, DURATION ≤10 years) groups based on 10-year disease duration. For reliability, supplementary analyses were conducted with alternative cutoffs, creating three groups: 0 < DURATION ≤7 years (n = 11), 7 < DURATION ≤14 years (n = 11), and DURATION >14 years (n = 14). This study examined the intraregional phase-amplitude coupling (PAC) between theta phase and alpha amplitude across the whole brain. The interregional directed phase transfer entropy (dPTE) between frontal and temporal regions in the alpha and theta bands, and the interregional cross-frequency directionality (CFD) between temporal and frontal regions from the theta phase to the alpha amplitude were further computed and compared among groups. Partial correlation analysis was conducted to investigate correlations between intraregional PAC, interregional dPTE connectivity, interregional CFD, and disease duration. Whole-brain intraregional PAC analyses revealed enhanced theta phase-alpha amplitude coupling within the ipsilateral temporal and frontal regions in TLE patients, and the ipsilateral temporal PAC was positively correlated with disease duration (r = 0.38, p <.05). Interregional dPTE analyses demonstrated a gradual increase in frontal-to-temporal connectivity within the alpha band, while the direction of theta-band connectivity reversed from frontal-to-temporal to temporal-to-frontal as the disease duration increased. Interregional CFD analyses revealed that the inhibitory effect of frontal regions on temporal regions gradually increased with prolonged disease duration (r = -0.36, p <.05). This study clarified the intrinsic reciprocal connectivity between temporal and frontal regions with TLE duration. We propose a dynamically reorganized triple-stage network that transitions from balanced networks to constrained networks and further develops into imbalanced networks as the disease duration increases.

Rational design of functional amyloid fibrillar assemblies.

Amyloid fibrillar assemblies, originally identified as pathological entities in neurodegenerative diseases, have been widely adopted by various proteins to fulfill diverse biological functions in living organisms. Due to their unique features, such as hierarchical assembly, exceptional mechanical properties, environmental stability, and self-healing properties, amyloid fibrillar assemblies have been employed as functional materials in numerous applications. Recently, with the rapid advancement in synthetic biology and structural biology tools, new trends in the functional design of amyloid fibrillar assemblies have begun to emerge. In this review, we provide a comprehensive overview of the design principles for functional amyloid fibrillar assemblies from an engineering perspective, as well as through the lens of structural insights. Initially, we introduce the fundamental structural configurations of amyloid assemblies and highlight the functions of representative examples. We then focus on the underlying design principles of two prevalent strategies for the design of functional amyloid fibrillar assemblies: (1) introducing new functions via protein modular design and/or hybridization, with typical applications encompassing catalysis, virus disinfection, biomimetic mineralization, bio-imaging, and biotherapy; and (2) dynamically regulating living amyloid fibrillar assemblies using synthetic gene circuits, with typical applications in pattern formation, leakage repair, and pressure sensing. Next, we summarize how breakthroughs in characterization techniques have contributed to unveiling the structural polymorphism of amyloid fibrils at the atomic level, and further clarifying the highly diverse regulation mechanisms of amyloid fibrillar assembly and disassembly fine-tuned by various factors. The structural knowledge may significantly aid in the structure-guided design of amyloid fibrillar assemblies with diverse bio-activities and adjustable regulatory properties. Finally, we envision that a new trend in functional amyloid design may emerge by integrating structural tunability, synthetic biology and artificial intelligence.

Multi-scale simulation of early kidney branching morphogenesis.

An important feature of the branch morphogenesis during kidney development is the termination of the tips on the outer surface of a kidney. This feature requires the avoidance of the intersection between the tips and existing ducts inside the kidney. Here, we started from a continuous model and implemented the coarse grained rules into a fast and discrete simulations. The ligand-receptor-based Turing mechanism suggests a repulsion that decreases exponentially with distance between interacting branches, preventing the intersection between neighboring branches. We considered this repulsive effect in numerical simulations and successfully reproduce the key features of the experimentally observed branch morphology for an E15.5 kidney. We examine the similarity of several geometrical parameters between the simulation results and experimental observations. The good agreement between the simulations and experiments suggests that the concentration decay caused by the absorption of glial cell line derived neurotrophic factor might be the key factor to affect the geometry in early kidney development.

Programmable and printable Bacillus subtilis biofilms as engineered living materials.

Bacterial biofilms can be programmed to produce living materials with self-healing and evolvable functionalities. However, the wider use of artificial biofilms has been hindered by limitations on processability and functional protein secretion capacity. We describe a highly flexible and tunable living functional materials platform based on the TasA amyloid machinery of the bacterium Bacillus subtilis. We demonstrate that genetically programmable TasA fusion proteins harboring diverse functional proteins or domains can be secreted and can assemble into diverse extracellular nano-architectures with tunable physicochemical properties. Our engineered biofilms have the viscoelastic behaviors of hydrogels and can be precisely fabricated into microstructures having a diversity of three-dimensional (3D) shapes using 3D printing and microencapsulation techniques. Notably, these long-lasting and environmentally responsive fabricated living materials remain alive, self-regenerative, and functional. This new tunable platform offers previously unattainable properties for a variety of living functional materials having potential applications in biomaterials, biotechnology, and biomedicine.

Variation in longitudinal trajectories of cortical sulci in normal elderly.

Sulcal morphology has been reported to change with age-related neurological diseases, but the trajectories of sulcal change in normal ageing in the elderly is still unclear. We conducted a study of sulcal morphological changes over seven years in 132 normal elderly participants aged 70-90 years at baseline, and who remained cognitively normal for the next seven years. We examined the fold opening and sulcal depth of sixteen (eight on each hemisphere) prominent sulci based on T1-weighted MRI using automated methods with visual quality control. The trajectory of each individual sulcus with respect to age was examined separately by linear mixed models. Fold opening was best modelled by cubic fits in five sulci, by quadratic models in six sulci and by linear models in five sulci, indicating an accelerated widening of a number of sulci in older age. Sulcal depth showed significant linear decline in three sulci and quadratic trend in one sulcus. Turning points of non-linear trajectories towards accelerated widening of the fold were found to be around the age between 75 and 80, indicating an accelerated atrophy of brain cortex starting in the age of late 70s. Our findings of cortical sulcal changes in normal ageing could provide a reference for studies of neurocognitive disorders, including neurodegenerative diseases, in the elderly.

[Biomarker extraction of sustained attention based on brain functional network].

Although attention plays an important role in cognitive and perception, there is no simple way to measure one's attention abilities. We identified that the strength of brain functional network in sustained attention task can be used as the physiological indicator to predict behavioral performance. Behavioral and electroencephalogram (EEG) data from 14 subjects during three force control tasks were collected in this paper. The reciprocal of the product of force tolerance and variance were used to calculate the score of behavioral performance. EEG data were used to construct brain network connectivity by wavelet coherence method and then correlation analysis between each edge in connectivity matrices and behavioral score was performed. The linear regression model combined those with significantly correlated network connections into physiological indicator to predict participant's performance on three force control tasks, all of which had correlation coefficients greater than 0.7. These results indicate that brain functional network strength can provide a widely applicable biomarker for sustained attention tasks.

Deep Source Localization with Magnetoencephalography Based on Sensor Array Decomposition and Beamforming.

In recent years, the source localization technique of magnetoencephalography (MEG) has played a prominent role in cognitive neuroscience and in the diagnosis and treatment of neurological and psychological disorders. However, locating deep brain activities such as in the mesial temporal structures, especially in preoperative evaluation of epilepsy patients, may be more challenging. In this work we have proposed a modified beamforming approach for finding deep sources. First, an iterative spatiotemporal signal decomposition was employed for reconstructing the sensor arrays, which could characterize the intrinsic discriminant features for interpreting sensor signals. Next, a sensor covariance matrix was estimated under the new reconstructed space. Then, a well-known vector beamforming approach, which was a linearly constraint minimum variance (LCMV) approach, was applied to compute the solution for the inverse problem. It can be shown that the proposed source localization approach can give better localization accuracy than two other commonly-used beamforming methods (LCMV, MUSIC) in simulated MEG measurements generated with deep sources. Further, we applied the proposed approach to real MEG data recorded from ten patients with medically-refractory mesial temporal lobe epilepsy (mTLE) for finding epileptogenic zone(s), and there was a good agreement between those findings by the proposed approach and the clinical comprehensive results.

Glaucoma detection model by exploiting multi-region and multi-scan-pattern OCT images with dynamical region score.

Currently, deep learning-based methods have achieved success in glaucoma detection. However, most models focus on OCT images captured by a single scan pattern within a given region, holding the high risk of the omission of valuable features in the remaining regions or scan patterns. Therefore, we proposed a multi-region and multi-scan-pattern fusion model to address this issue. Our proposed model exploits comprehensive OCT images from three fundus anatomical regions (macular, middle, and optic nerve head regions) being captured by four scan patterns (radial, volume, single-line, and circular scan patterns). Moreover, to enhance the efficacy of integrating features across various scan patterns within a region and multiple regional features, we employed an attention multi-scan fusion module and an attention multi-region fusion module that auto-assign contribution to distinct scan-pattern features and region features adapting to characters of different samples, respectively. To alleviate the absence of available datasets, we have collected a specific dataset (MRMSG-OCT) comprising OCT images captured by four scan patterns from three regions. The experimental results and visualized feature maps both demonstrate that our proposed model achieves superior performance against the single scan-pattern models and single region-based models. Moreover, compared with the average fusion strategy, our proposed fusion modules yield superior performance, particularly reversing the performance degradation observed in some models relying on fixed weights, validating the efficacy of the proposed dynamic region scores adapted to different samples. Moreover, the derived region contribution scores enhance the interpretability of the model and offer an overview of the model's decision-making process, assisting ophthalmologists in prioritizing regions with heightened scores and increasing efficiency in clinical practice.

A Compact Graph Convolutional Network With Adaptive Functional Connectivity for Seizure Prediction.

Seizure prediction using EEG has significant implications for the daily monitoring and treatment of epilepsy patients. However, the task is challenging due to the underlying spatiotemporal correlations and patient heterogeneity. Traditional methods often use large-scale models with independent components to capture the spatial and temporal features of EEG separately or explore shared patterns among patients with the help of pre-defined functional connectivity. In this paper, we propose a compact model, called the graph convolutional network based on adaptive functional connectivity (AFC-GCN), for seizure prediction. The model can adaptively infer evolution of functional connectivity in epilepsy patients during seizures through data-driven methods and synchronously analyze spatiotemporal response of functional connectivity in multiple topologies. On CHB-MIT datasets, the experimental results demonstrate that AFC-GCN achieves accurate and robust performance with low complexity. (AUC: 0.9820, accuracy: 0.9815, sensitivity: 0.9802, FPR: 0.0172). The proposed method has the potential to predict seizure during daily monitoring.

Semi-supervised point consistency network for retinal artery/vein classification.

Recent deep learning methods with convolutional neural networks (CNNs) have boosted advance prosperity of medical image analysis and expedited the automatic retinal artery/vein (A/V) classification. However, it is challenging for these CNN-based approaches in two aspects: (1) specific tubular structures and subtle variations in appearance, contrast, and geometry, which tend to be ignored in CNNs with network layer increasing; (2) limited well-labeled data for supervised segmentation of retinal vessels, which may hinder the effectiveness of deep learning methods. To address these issues, we propose a novel semi-supervised point consistency network (SPC-Net) for retinal A/V classification. SPC-Net consists of an A/V classification (AVC) module and a multi-class point consistency (MPC) module. The AVC module adopts an encoder-decoder segmentation network to generate the prediction probability map of A/V for supervised learning. The MPC module introduces point set representations to adaptively generate point set classification maps of the arteriovenous skeleton, which enjoys its prediction flexibility and consistency (i.e. point consistency) to effectively alleviate arteriovenous confusion. In addition, we propose a consistency regularization between the predicted A/V classification probability maps and point set representations maps for unlabeled data to explore the inherent segmentation perturbation of the point consistency, reducing the need for annotated data. We validate our method on two typical public datasets (DRIVE, HRF) and a private dataset (TR280) with different resolutions. Extensive qualitative and quantitative experimental results demonstrate the effectiveness of our proposed method for supervised and semi-supervised learning.

Learning with limited annotations: A survey on deep semi-supervised learning for medical image segmentation.

Medical image segmentation is a fundamental and critical step in many image-guided clinical approaches. Recent success of deep learning-based segmentation methods usually relies on a large amount of labeled data, which is particularly difficult and costly to obtain, especially in the medical imaging domain where only experts can provide reliable and accurate annotations. Semi-supervised learning has emerged as an appealing strategy and been widely applied to medical image segmentation tasks to train deep models with limited annotations. In this paper, we present a comprehensive review of recently proposed semi-supervised learning methods for medical image segmentation and summarize both the technical novelties and empirical results. Furthermore, we analyze and discuss the limitations and several unsolved problems of existing approaches. We hope this review can inspire the research community to explore solutions to this challenge and further advance the field of medical image segmentation.

Application of HFO and scaling analysis of neuronal oscillations in the presurgical evaluation of focal epilepsy.

PURPOSE: To explore the utility of high frequency oscillations (HFO) and long-range temporal correlations (LRTCs) in preoperative assessment of epilepsy. METHODS: MEG ripples were detected in 59 drug-resistant epilepsy patients, comprising 5 with parietal lobe epilepsy (PLE), 21 with frontal lobe epilepsy (FLE), 14 with lateral temporal lobe epilepsy (LTLE), and 19 with mesial temporal lobe epilepsy (MTLE) to identify the epileptogenic zone (EZ). The results were compared with clinical MEG reports and resection area. Subsequently, LRTCs were quantified at the source-level by detrended fluctuation analysis (DFA) and life/waiting -time at 5 bands for 90 cerebral cortex regions. The brain regions with larger DFA exponents and standardized life-waiting biomarkers were compared with the resection results. RESULTS: Compared to MEG sensor-level data, ripple sources were more frequently localized within the resection area. Moreover, source-level analysis revealed a higher proportion of DFA exponents and life-waiting biomarkers with relatively higher rankings, primarily distributed within the resection area (p<0.01). Moreover, these two LRCT indices across five distinct frequency bands correlated with EZ. CONCLUSION: HFO and source-level LRTCs are correlated with EZ. Integrating HFO and LRTCs may be an effective approach for presurgical evaluation of epilepsy.

Conformal and conductive biofilm-bridged artificial Z-scheme system for visible light-driven overall water splitting.

Semi-artificial Z-scheme systems offer promising potential toward efficient solar-to-chemical conversion, yet sustainable and stable designs are currently lacking. Here, we developed a sustainable hybrid Z-scheme system capable for visible light-driven overall water splitting by integrating the durability of inorganic photocatalysts with the interfacial adhesion and regenerative property of bacterial biofilms. The Z-scheme configuration is fabricated by drop casting a mixture of photocatalysts onto a glass plate, followed by the growth of biofilms for conformal conductive paste through oxidative polymerization of pyrrole molecules. Notably, the system exhibited scalability indicated by consistent catalytic efficiency across various sheet areas, resistance observed by remarkable maintaining of photocatalytic efficiency across a range of background pressures, and high stability as evidenced by minimal decay of photocatalytic efficiency after 100-hour reaction. Our work thus provides a promising avenue toward sustainable and high-efficiency artificial photosynthesis, contributing to the broader goal of sustainable energy solutions.

A novel neuroprotective method against ischemic stroke by accelerating the drainage of brain interstitial fluid.

Ischemic stroke is a leading cause of death and disability worldwide. Inflammatory response after stroke determines the outcome of ischemic injury. A recent study has reported an efficient method, epidural arterial implantation (EAI), for accelerating interstitial fluid (ISF) drainage, which provides a promising strategy to clear pro-inflammatory cytokines in the brain extracellular space (ECS). In this study, the method of EAI was modified (m-EAI) to control its function of accelerating the ISF drainage at different time points following ischemic attack. The neuroprotective effect of m-EAI on ischemic stroke was evaluated with the transient middle cerebral artery occlusion (tMCAO) rat model. The results demonstrated the accumulation of IL-1ß, IL-6, and TNF-α was significantly decreased by activating m-EAI at 7 d before and immediately after ischemic attack in tMCAO rats, accompanied with decreased infarct volume and improved neurological function. This study consolidates the hypothesis of exacerbated ischemic damage by inflammatory response and provides a new perspective to treat encephalopathy via brain ECS. Further research is essential to investigate whether m-EAI combined with neuroprotective drugs could enhance the therapeutic effect on ischemic stroke.

Cost-efficient and glaucoma-specifical model by exploiting normal OCT images with knowledge transfer learning.

Monitoring the progression of glaucoma is crucial for preventing further vision loss. However, deep learning-based models emphasize early glaucoma detection, resulting in a significant performance gap to glaucoma-confirmed subjects. Moreover, developing a fully-supervised model is suffering from insufficient annotated glaucoma datasets. Currently, sufficient and low-cost normal OCT images with pixel-level annotations can serve as valuable resources, but effectively transferring shared knowledge from normal datasets is a challenge. To alleviate the issue, we propose a knowledge transfer learning model for exploiting shared knowledge from low-cost and sufficient annotated normal OCT images by explicitly establishing the relationship between the normal domain and the glaucoma domain. Specifically, we directly introduce glaucoma domain information to the training stage through a three-step adversarial-based strategy. Additionally, our proposed model exploits different level shared features in both output space and encoding space with a suitable output size by a multi-level strategy. We have collected and collated a dataset called the TongRen OCT glaucoma dataset, including pixel-level annotated glaucoma OCT images and diagnostic information. The results on the dataset demonstrate our proposed model outperforms the un-supervised model and the mixed training strategy, achieving an increase of 5.28% and 5.77% on mIoU, respectively. Moreover, our proposed model narrows performance gap to the fully-supervised model decreased by only 1.01% on mIoU. Therefore, our proposed model can serve as a valuable tool for extracting glaucoma-related features, facilitating the tracking progression of glaucoma.

Uncertainty-guided mutual consistency learning for semi-supervised medical image segmentation.

Medical image segmentation is a fundamental and critical step in many clinical approaches. Semi-supervised learning has been widely applied to medical image segmentation tasks since it alleviates the heavy burden of acquiring expert-examined annotations and takes the advantage of unlabeled data which is much easier to acquire. Although consistency learning has been proven to be an effective approach by enforcing an invariance of predictions under different distributions, existing approaches cannot make full use of region-level shape constraint and boundary-level distance information from unlabeled data. In this paper, we propose a novel uncertainty-guided mutual consistency learning framework to effectively exploit unlabeled data by integrating intra-task consistency learning from up-to-date predictions for self-ensembling and cross-task consistency learning from task-level regularization to exploit geometric shape information. The framework is guided by the estimated segmentation uncertainty of models to select out relatively certain predictions for consistency learning, so as to effectively exploit more reliable information from unlabeled data. Experiments on two publicly available benchmark datasets showed that: (1) Our proposed method can achieve significant performance improvement by leveraging unlabeled data, with up to 4.13% and 9.82% in Dice coefficient compared to supervised baseline on left atrium segmentation and brain tumor segmentation, respectively. (2) Compared with other semi-supervised segmentation methods, our proposed method achieve better segmentation performance under the same backbone network and task settings on both datasets, demonstrating the effectiveness and robustness of our method and potential transferability for other medical image segmentation tasks.

The association of magnetoencephalography high-frequency oscillations with epilepsy types and a ripple-based method with source-level connectivity for mapping epilepsy sources.

OBJECTIVE: To explore the association between high-frequency oscillations (HFOs) and epilepsy types and to improve the accuracy of source localization. METHODS: Magnetoencephalography (MEG) ripples of 63 drug-resistant epilepsy patients were detected. Ripple rates, distribution, spatial complexity, and the clustering coefficient of ripple channels were used for the preliminary classification of lateral temporal lobe epilepsy (LTLE), mesial temporal lobe epilepsy (MTLE), and nontemporal lobe epilepsy (NTLE), mainly frontal lobe epilepsy (FLE). Furthermore, the seizure site identification was improved using the Tucker LCMV method and source-level betweenness centrality. RESULTS: Ripple rates were significantly higher in MTLE than in LTLE and NTLE (p < 0.05). The LTLE and MTLE were mainly distributed in the temporal lobe, followed by the parietal lobe, occipital lobe, and frontal lobe, whereas MTLE ripples were mainly distributed in the frontal lobe, then parietal lobe and occipital lobe. Nevertheless, the NTLE ripples were primarily in the frontal lobe and partially in the occipital lobe (p < 0.05). Meanwhile, the spatial complexity of NTLE was significantly higher than that of LTLE and MTLE and was lowest in MTLE (p < 0.01). However, an opposite trend was observed for the standardized clustering coefficient compared with spatial complexity (p < 0.01). Finally, the tucker algorithm showed a higher percentage of ripples at the surgical site when the betweenness centrality was added (p < 0.01). CONCLUSION: This study demonstrated that HFO rates, distribution, spatial complexity, and clustering coefficient of ripple channels varied considerably among the three epilepsy types. Additionally, tucker MEG estimation combined with ripple rates based on the source-level functional connectivity is a promising approach for presurgical epilepsy evaluation.

Retinal vessel caliber and tortuosity and prediction of 5-year incidence of hypertension.

PURPOSE: With arterial hypertension as a global risk factor for cerebrovascular and cardiovascular diseases, we examined whether retinal blood vessel caliber and tortuosity assessed by a vessel-constraint network model can predict the incidence of hypertension. METHODS: The community-based prospective study included 9230 individuals who were followed for 5 years. Ocular fundus photographs taken at baseline were analyzed by a vessel-constraint network model. RESULTS: Within the 5-year follow-up, 1279 (18.8%) and 474 (7.0%) participants out of 6813 individuals free of hypertension at baseline developed hypertension and severe hypertension, respectively. In multivariable analysis, a higher incidence of hypertension was related to a narrower retinal arteriolar diameter ( P  < 0.001), wider venular diameter ( P  = 0.005), and a smaller arteriole-to-venule diameter ratio ( P  < 0.001) at baseline. Individuals with the 5% narrowest arteriole or the 5% widest venule diameter had a 17.1-fold [95% confidence interval (CI):7.9, 37.2] or 2.3-fold (95% CI: 1.4, 3.7) increased risk for developing hypertension, as compared with those with the 5% widest arteriole or the 5% narrowest venule. The area under the receiver operator characteristic curve for predicting the 5-year incidence of hypertension and severe hypertension was 0.791 (95% CI: 0.778, 0.804) and 0.839 (95% CI: 0.821, 0.856), respectively. Although the venular tortuosity was positively associated with the presence of hypertension at baseline ( P  = 0.01), neither arteriolar tortuosity nor venular tortuosity was associated with incident hypertension (both P  ≥ 0.10). CONCLUSION AND RELEVANCE: Narrower retinal arterioles and wider venules indicate an increased risk for incident hypertension within 5 years, while tortuous retinal venules are associated with the presence rather than the incidence of hypertension. The automatic assessment of retinal vessel features performed well in identifying individuals at risk of developing hypertension.