Access Patterns Research Articles

Loss of cold tolerance is conferred by absence of the WRKY34 promoter fragment during tomato evolution

Natural evolution has resulted in reduced cold tolerance in cultivated tomato (Solanum lycopersicum). Herein, we perform a combined analysis of ATAC-Seq and RNA-Seq in cold-sensitive cultivated tomato and cold-tolerant wild tomato (S. habrochaites). We identify that WRKY34 has the most significant association with differential chromatin accessibility and expression patterns under cold stress. We find that a 60 bp InDel in the WRKY34 promoter causes differences in its transcription and cold tolerance among 376 tomato accessions. This 60 bp fragment contains a GATA cis-regulatory element that binds to SWIBs and GATA29, which synergistically suppress WRKY34 expression under cold stress. Moreover, WRKY34 interferes with the CBF cold response pathway through regulating transcription and protein levels. Our findings emphasize the importance of polymorphisms in cis-regulatory regions and their effects on chromatin structure and gene expression during crop evolution.

Extremely-Compressed SSDs with I/O Behavior Prediction

As the data volume continues to grow exponentially, there is an increasing demand for large storage system capacity. Data compression techniques effectively reduce the volume of written data, enhancing space efficiency. As a result, many modern SSDs have already incorporated data compression capabilities. However, data compression introduces additional processing overhead in critical I/O paths, potentially affecting system performance. Currently, most compression solutions in flash-based storage systems employ fixed compression algorithms for all incoming data without leveraging differences among various data access patterns. This leads to sub-optimal compression efficiency. This article proposes a data-type-aware Flash Translation Layer (DAFTL) scheme to maximize space efficiency without compromising system performance. First, we propose an I/O behavior prediction method to forecast future access on specific data. Then, DAFTL matches data types with distinct I/O behaviors to compression algorithms of varying intensities, achieving an optimal balance between performance and space efficiency. Specifically, it employs higher-intensity compression algorithms for less frequently accessed data to maximize space efficiency. For frequently accessed data, it utilizes lower-intensity but faster compression algorithms to maintain system performance. Finally, an improved compact compression method is proposed to effectively eliminate page fragmentation and further enhance space efficiency. Extensive evaluations using a variety of real-world workloads, as well as the workloads with real data we collected on our platforms, demonstrate that DAFTL achieves more data reductions than other approaches. When compared to the state-of-the-art compression schemes, DAFTL reduces the total number of pages written to the SSD by an average of 8%, 21.3%, and 25.6% for data with high, medium, and low compressibility, respectively. In the case of workloads with real data, DAFTL achieves an average reduction of 10.4% in the total number of pages written to SSD. Furthermore, DAFTL exhibits comparable or even improved read and write performance compared to other solutions.

NeuroTAP: Thermal and Memory Access Pattern-aware Data Mapping on 3D DRAM for Maximizing DNN Performance

Deep neural networks (DNNs) have been widely adopted, owing to break-through performance and high accuracy. DNNs exhibit varying memory behavior involving specific and recognizable memory access patterns and access intensity, depending on the selected data reuse in different layers. Such applications have high memory bandwidth demands due to aggressive computations, performing several billion-floating-point-operations-per-second (BFLOPs). 3D DRAMs, providing very high memory access bandwidth, are extensively employed to break the memory wall , bridging the gap between compute and memory while running DNNs. However, the vertical integration in 3D DRAM introduces serious thermal issues, resulting from high power density and close proximity of memory cells, and requires dynamic thermal management (DTM). To unleash the true potential of 3D DRAM and exploit the enormous bandwidth under thermal constraints, there is a need to intelligently map the DNN application’s data across memory channels, pseudo-channels, and banks, minimizing the effective memory latency and reducing the thermal-induced application slowdown. The specific memory access patterns exhibited by a DNN layer execution are crucial to determine a favourable data mapping method for 3D DRAM dies that potentially causes minimal thermal impact, and also maximize DRAM bandwidth utilization. In this work, we propose an application-aware and thermal-sensitive data mapping that intelligently assigns portions of the 3D DRAM to DNN layers, leveraging the knowledge about layer’s memory access patterns and minimizing DTM-induced performance overheads. Additionally, we also deploy a DRAM low-power states based DTM mechanism to keep the 3D DRAM within safe thermal limits. Using our proposal, we observe a performance improvement of 1% to 61%, and memory energy savings of 1% to 55% for popular deep neural networks over state-of-the-art DTM strategies while running DNN inference.

Accessibility of the early care and education supply: Variation within the center-based provider sector

Center-based early care and education (ECE) is important for promoting positive early development and supporting families by providing child care so parents can work. However, the center-based supply varies substantially in terms of funding sources, indicators of quality, and services offered; and many families experience a lack of equitable access to providers that meet their needs. Using a nationally representative sample of the center-based supply from the 2019 National Survey of Early Care and Education, this study examined patterns in the services center-based providers offer that may facilitate or pose barriers to families’ access from a multidimensional lens. Two research questions were investigated using latent class analysis: First, are there patterns of provider accessibility within the center-based supply, and second, are there covariates (i.e., funding, community poverty/ urban density, geographic region) that explain patterns of ECE access? Center-based features selected as indicators were informed by a definition of access comprised of multiple dimensions: availability, affordability, supports children’s development, and meets parent’s needs. Class enumeration suggested that a three-class model provided the best fit, indicating three patterns of program accessibility: providers that were: flexible at cost (25.9%), free but inflexible (29.2%), and somewhat accessible (44.9%). Additional covariate analyses revealed providers were significantly more or less likely to be classified into the latent classes of access according to community and funding characteristics. Notably, programs receiving Head Start and public preschool funding and those in communities with high poverty and urban density were more likely to be assigned to the free but inflexible class. A key component driving ECE utilization is the accessibility of providers, and these findings inform recommendations for future research and policy to better serve the needs of families with young children.

Evaluating the level of access and equity of the bus rapid transit (BRT) system: The case of Dar-Es-Salaam, Tanzania

Bus Rapid Transit (BRT) systems have witnessed significant growth worldwide, particularly in rapidly urbanizing cities in the developing world, often implemented through public-private partnerships to foster economic growth. However, the success of such ambitions relies on ensuring equitable transit access for all residents, including those living with poverty or other social disadvantages. The purpose of this study is to explore transit access, coverage, and equity of the BRT system in Dar-es-Salaam, Tanzania using infrastructure-based measures of proximity to transit stations and stops. In particular, the equitable distribution of transit access patterns is explored in relation to neighborhood socio-economic strata in the catchment areas within a 30-min walking distance. The study's findings reveal notable disparities in the level of access among various socio-economic strata, with the least deprived population benefiting the most from the Dar-es-Salaam BRT system. As more BRT phases are implemented, there is a continuous increase in the Multidimensional Poverty Index (MPI) within the areas not covered by the BRT system. This suggests that access improvements predominantly favor the wealthier population rather than effectively addressing the needs of the impoverished population. To enhance access and achieve more equitable outcomes, it is crucial to implement targeted strategies and measures. These may include improved integration of the BRT with a feeder system, continuous improvement of Non-Motorized Transport (NMT) facilities, and the promotion of equitable transit-oriented development along the BRT corridor. These initiatives play a vital role in ensuring that vulnerable populations, especially the poor, have access to and can benefit from the BRT services.

Monitoring the security system at pistol storage based on a web server in army units

The employment of security features is crucial to the operation of many modern electronic gadgets in the current 5.0 era as time and technology advance. This means that we must keep coming up with new ideas and producing instruments that will enhance security. Among the many issues that come up is the fact that guns are still registered, checked out, and returned using public books, which leaves space for data tampering or the chance that the books are lost. The primary goal of this research is to create and execute a web-based system that uses fingerprints to monitor the borrowing and return of firearms. Because of its nature online-his, this system will streamline operations and enable users to conveniently view inventory from any location. Researchers employ literature methodology, which includes reading, debating, synthesizing, and developing the concepts employed, in addition to employing laboratory techniques and the necessary tools to accomplish their objectives. The web server on which the security monitoring system for keeping firearms is based has undergone extensive testing and analysis, and the findings indicate that generally, the device can operate as intended. This conclusion is based on traffic analysis and access patterns on the web server. Following the completion of five tests with a 95% accuracy rate. Finally, the security monitoring system for the storage of firearms is based on a web server and has undergone extensive testing and analysis with a 90% speed and accuracy rate; the findings indicate that, on the whole, the instrument can perform as intended

From space to service: Measuring the accessibility in a space-time heterogeneity perspective

Accessibility has been identified as the primary motivating factor for the use of daily service facilities. Existing methods for measuring the accessibility of service facilities primarily focus on location accessibility, while little research extends it to service accessibility, which includes both reaching the location and obtaining the services. This paper proposes a novel approach to measuring service accessibility, termed the Space-Time Heterogeneous Gaussian Two-Step Floating Catchment Area (STH-G2SFCA). Firstly, a fine-grain heterogeneity analysis of service accessibility is constructed based on the supply-demand relationship. Secondly, a space-time search strategy is proposed with a Gaussian distance attenuation function, optimized by the decay rate of service attraction. Lastly, we introduce a supply-demand score and the Global Moran's index to assess the space-time patterns of service accessibility. A case study is conducted in the central urban area of Wuhan, China, with catering facilities as an example. The experimental results show that the STH-G2SFCA method can more precisely assess service accessibility, which is often overestimated in classic methods, and capture the temporal dynamics process of service accessibility within a day. The proposed method promises to be replicated in other service facilities, providing urban planners with a valuable tool for digital planning.

Patterns of digital health access and use among US adults: a latent class analysis

BackgroundDigital technologies allow users to engage in health-related behaviors associated with positive outcomes. We aimed to identify classes of US adults with distinct digital technologies access and health use patterns and characterize class composition. Data came from Health Information National Trends Survey Wave 5 Cycles 1–4, a nationally representative cross-sectional survey of US adults (N = 13,993). We used latent class analysis to identify digital technologies access and health use patterns based on 32 ternary variables of behaviors and access to requisite technologies and platforms, including the internet, internet-enabled devices, health monitors, and electronic health records (EHRs). We ran a multinomial logistic regression to identify sociodemographic and health correlates of class membership (n = 10,734).ResultsTen classes captured patterns of digital technology access and health use among US adults. This included a digitally isolated, a mobile-dependent, and a super user class, which made up 8.9%, 7.8%, and 13.6% of US adults, respectively, and captured access patterns from only basic cellphones and health monitors to near complete access to web-, mobile-, and EHR-based platforms. Half of US adults belonged to classes that lacked access to EHRs and relied on alternative web-based tools typical of patient portals. The proportion of class members who used digital technologies for health purposes varied from small to large. Older and less educated adults had lower odds of belonging to classes characterized by access or engagement in health behaviors. Hispanic and Asian adults had higher odds of belonging to the mobile-dependent class. Individuals without a regular healthcare provider and those who had not visited a provider in the past year were more likely to belong to classes with limited digital technologies access or health use.DiscussionOnly one third of US adults belonged to classes that had near complete access to digital technologies and whose members engaged in almost all health behaviors examined. Sex, age, and education were associated with membership in classes that lacked access to 1 + digital technologies or exhibited none to limited health uses of such technologies. Results can guide efforts to improve access and health use of digital technologies to maximize associated health benefits and minimize disparities.

Standards of Accessibility to Health, Education, Culture and Leisure and The Association With Quality of Life For People With Disabilities

Objective: To evaluate the association between standards of accessibility to health, education, culture and leisure and the quality of life of people with disabilities. Theoretical Framework: Four fundamental rights play a vital role in human development: health, education, culture and leisure. These components are crucial for the general well-being and quality of life of all people, including those with some type of disability (DOS SANTOS; PEREIRA, 2023; MITRA et al., 2013; BEST et al., 2022). Method: 101 patients from the Specialized Center for Rehabilitation for People with Disabilities (CER IV), Rio Grande do Sul participated in the study. The Two Step Cluster method was used as an analytical procedure, which analyzes data in two stages and means comparisons. Results and Discussion: There are two patterns of accessibility to health, education, culture and leisure, with one group being characterized by having high levels of accessibility and another group being characterized by a group of participants with low levels of accessibility. High levels of accessibility to health, education, culture and leisure are associated with significantly higher levels of quality of life, both in general and in the physical, psychological, social relationships and environmental domains. Research Implications: The results presented may be useful to professionals who work with people with disabilities. Originality/Value: These results may also be relevant to guide public policies and interventions aimed at improving accessibility to these resources that are fundamental to promoting a higher quality of life in this population.

Blockchain-Based Caching Architecture for DApp Data Security and Delivery.

Decentralized applications (DApps) built on blockchain technology offer a promising solution to issues caused by centralization. However, traditional DApps leveraging off-chain storage face performance challenges due to factors such as storage location, network speed, and hardware conditions. For example, decentralized storage solutions such as IPFS suffer from diminished download performance due to I/O constraints influenced by data access patterns. Aiming to enhance the Quality of Service (QoS) in DApps built on blockchain technology, this paper proposes a blockchain node-based distributed caching architecture that guarantees real-time responsiveness for users. The proposed architecture ensures data integrity and user data ownership through blockchain while maintaining cache data consistency through local blockchain data. By implementing local cache clusters on blockchain nodes, our system achieves rapid response times. Additionally, attribute-based encryption is applied to stored content, enabling secure content sharing and access control, which prevents data leakage and unauthorized access in unreliable off-chain storage environments. Comparative analysis shows that our proposed system achieves a reduction in request processing latency of over 89% compared to existing off-chain solutions, maintaining cache data consistency and achieving response times within 65 ms. This demonstrates the model's effectiveness in providing secure and high-performance DApp solutions.

Evaluating accessibility benefits and ridership of bike-transit integration through a social equity lens

Integration of bike usage and transit services is an effective way to enhance accessibility for both transportation modes. Using high-resolution transit data, the study analyzes bike-transit multimodal accessibility and usage patterns through a social equity lens. Two types of accessibility increment are studied: bicycle increment to public transit – the benefits of using bicycle for transit riders, and transit increment to cycling – the merits of using transit for cyclists. Results show that bike-transit integration benefits both public transit riders and cyclists, expanding their accessible opportunities by up to 70% and enabling longer trips for cyclists while providing continuous benefits for public transit users. Meanwhile, better infrastructure significantly improves multimodal accessibility, resulting in more increment for public transit riders but less increment for cyclists. The paper also shows the spatiotemporal patterns of multimodal ridership. The research highlights disparities in bike-transit activities for Black communities due to inadequate local biking infrastructure. Black people majority neighborhoods enjoy less increment compared to other neighborhoods for shorter and very long trips; they also have disproportionately lower multimodal ridership despite much higher transit ridership and better transit access. Enhancing biking infrastructure in these areas can improve physical accessibility increment and promote social equity. The paper provides practical insights for transit planning, emphasizing the importance of connecting bike lanes and creating safer streets for cycling.

Leveraging Machine Learning to Improve Access Control Mechanisms in Data Warehousing

This paper delves into integrating Machine Learning (ML) algorithms into Access Control Mechanisms (ACM) within Data Warehouses (DW) to enhance both security and operational efficiency. Traditional ACMs, notably role-based access control (RBAC), often struggle with adapting to dynamic threats and complex access patterns. ML tackles these issues by improving access decision accuracy through real-time anomaly detection and adaptive control based on user behavior and contextual insights. The study examines the implementation and benefits of ML-enhanced ACMs in DW environments, highlighting their effectiveness in mitigating unauthorized access and insider threats. This research also addresses critical considerations, such as privacy protections and the interpretability of ML models, crucial for maintaining regulatory compliance and stakeholder trust. Ultimately, this study emphasizes ML's pivotal contribution to advancing security practices within Data Warehouses (DWs) and proposes future research directions to enhance adaptive security measures in evolving digital landscapes.

Multidimensional Forensic Analysis of Onion Sites Using LSTM and Fuzzy Encoded Techniques: Insights and Applications in Digital Investigations

When it comes to digital investigations, the fact that onion sites make people anonymous makes investigative research much harder. For multidimensional forensic analysis of onion sites, this study suggests a new method that combines Long Short-Term Memory (LSTM) networks with fuzzy encoding methods. When you use the Tor network to get to onion sites, both users and services become anonymous, which makes it harder to do regular investigations. LSTM networks are used to look at trends in how people use the site and when material is updated over time. These networks are good at modeling sequential data. Fuzzy encoding methods work with LSTM to handle the uncertainty that comes with analyzing anonymous data, which makes investigative results more accurate. The method involves keeping time logs of what people do on onion sites and turning them into fuzzy sets to show how user interactions and content changes change over time. LSTM models are taught on these recorded sequences to find patterns that point to suspicious activity, like sudden changes in content or strange patterns of user access. This method can be used to find illegal activities, find secret services that host illegal content, and make profiles of how users behave in an anonymous network. Investigators can better track actions across onion sites when they use LSTM's ability to see how events depend on each other over time and fuzzy encoding's ability to deal with inaccurate data. Real-world onion site data used for experiments shows that the proposed method can find secret behaviors and patterns that are hard for traditional investigative methods to find. The results show that the accuracy and efficiency of detecting are much better than with traditional methods.

Efficient Heuristic Management of Cloud Data Lakes for Enhanced Big Data Handling in Cloud Computing Environments

Efficient control of cloud data lakes is important for improving big data dealing in cloud computing settings. Cloud data lakes serve as stores for vast amounts of organized, semi-structured, and unstructured data from varied sources, offering scaling and freedom in data storage and processing. However, the sheer amount and range of data offer difficulties in improving storage, access, and analysis processes. This study suggests a heuristic method to handle these issues by improving the order and recovery of data within cloud data lakes. The heuristic method focuses on automatically handling data splitting, storage, and caching strategies based on data access trends and task factors. By leveraging heuristic principles, such as adaptable splitting and predictive caching, the suggested method aims to reduce delay and improve total query speed. Key components of the heuristic management include adaptable splitting methods that actively change data partition sizes based on access frequency and data distribution patterns. This flexible method ensures that frequently accessed data is easily available, while less accessed data is efficiently saved to improve storage usage. Furthermore, predictive storing methods expect future data access patterns using machine learning models or previous data trends. By strategically saving relevant datasets in memory or high-speed storage, the system reduces delay for repeated questions, thereby improving real-time data processing capabilities. The success of the suggested heuristic management method is tested through models and benchmarks using real big data tasks. Comparative research against standard static splitting and caching methods shows significant changes in query response times and resource utilization efficiency.

Regional variation in access to and quality of acute stroke care: results of Germany’s Health System Performance Assessment pilot, 2014–2020

BackgroundHealth System Performance Assessments (HSPA) and analyses of unwarranted regional variation in health care both aim at identifying strengths and weaknesses of health systems to improve care. Applying HSPA’s conceptual approach of interrelated health system dimensions (e.g., access, quality) to regional levels might help to better understand variation in care to reduce inequity and improve performance.MethodsWe use four indicators identified and analysed in a pilot study for a German HSPA to assess variation in access to and quality of acute stroke care between Germany’s 16 federal states and urban vs. rural regions from 2014 to 2020. Stroke unit (SU) density, share of the population reaching a SU within 30 min by car, share of inpatient stroke cases treated in a hospital with a SU, and inpatient mortality were computed based on hospital quality reports and discharge data covering all acute care hospitals. Inpatient mortality was adjusted for age, sex, stroke type, and comorbidities.ResultsAbout 500 SU were identified, i.e., 2.0 per 1,000 inpatient stroke cases. Almost 95% of Germans could reach a SU hospital within 30 min (rural: 90%; urban: 99%; > 88% in all states but one). The share of inpatient stroke cases treated in a SU hospital increased to 93% with a decreasing span between rural (92%) and urban (95%) regions and between states (74–98%). Inpatient mortality stagnated around 8.5% and increased to 9.0% in 2020 (rural: 8.7%; urban: 9.2%; states: 7.0–9.7%, one outlier of 13.3%).ConclusionsThe results especially revealed varying performance patterns in access to and quality of acute stroke care between the federal states, indicating different areas for improvement which might be addressed by more targeted policy measures in the future.

A single-cell chromatin accessibility dataset of human primed and naïve pluripotent stem cell-derived teratoma

Teratoma, due to its remarkable ability to differentiate into multiple cell lineages, is a valuable model for studying human embryonic development. The similarity of the gene expression and chromatin accessibility patterns in these cells to those observed in vivo further underscores its potential as a research tool. Notably, teratomas derived from human naïve (pre-implantation epiblast-like) pluripotent stem cells (PSCs) have larger embryonic cell diversity and contain extraembryonic lineages, making them more suitable to study developmental processes. However, the cell type-specific epigenetic profiles of naïve PSC teratomas have not been yet characterized. Using single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq), we analyzed 66,384 cell profiles from five teratomas derived from human naïve PSCs and their post-implantation epiblast-like (primed) counterparts. We observed 17 distinct cell types from both embryonic and extraembryonic lineages, resembling the corresponding cell types in human fetal tissues. Additionally, we identified key transcription factors specific to different cell types. Our dataset provides a resource for investigating gene regulatory programs in a relevant model of human embryonic development.

TSAPP: Threshold Single-Sign-On Authentication Preserving Privacy

Single-sign-on (SSO) authentication enables a user to gain a token from the identity server, with which the user accesses multiple services. To address single-point-of-failure of SSO, threshold SSO, where a group of identity servers issue a user with a token in the threshold manner, is introduced. SSO including threshold schemes suffers from privacy disclosure. One can learn a user's identity and access pattern from her/his token. Recent works focus on privacy preservation of SSO. However, these works merely consider scenarios of one single identity server SSO. No works that address privacy preservation of threshold SSO have emerged. In this work, we propose TSAPP, a threshold SSO authentication scheme preserving privacy. Each identity server issues a user with a partial token which is a signature on the user's pseudonym. With a threshold number of partial tokens, the user constructs a token, blinds the token with random numbers and accesses services with blinded tokens. Such mechanism preserves the user's identity, simultaneously protects the user's access pattern since adversaries cannot link the user's accesses, even if identity servers are corrupted. Security analysis demonstrates that TSAPP satisfies properties of anonymity, unlinkability, unforgeability and password-safety. The performance evaluation demonstrates that TSAPP is efficient in practice.

Enhancing Big Data Performance Through Graph Coloring-Based Locality of Reference

Efficiency is a crucial factor when handling the retrieval and storage of data from vast amounts of records in a Big Data repository. These systems require a subset of data that can be accommodated within the combined physical memory of a cluster of servers. It becomes impractical to analyze all of the data if its size exceeds the available memory capacity. Retrieving data from virtual storage, primarily hard disks, is significantly slower compared to accessing data from main memory, resulting in increased access time and diminished performance. To address this, a proposed model aims to enhance performance by identifying the most suitable data locality structure within a big data set and reorganizing the data schema accordingly; by locality, it has been referred to as a particular access pattern. This allows transactions to be executed on data residing in the fastest memory layer, such as cache, main memory, or disk cache

Corneal Opacity in the United States: An American Academy of Ophthalmology IRIS® Registry (Intelligent Research in Sight) Study

This study assesses the case frequencies, underlying causes, and vision outcomes in patients with a diagnosis of corneal opacity in the United States. Retrospective cohort study. Patients in the IRIS® Registry (Intelligent Research in Sight) who received a diagnosis of corneal opacity between January 1, 2013, and November 30,2020. The IRIS Registry contains demographic and clinical data of 79 887 324 patients who sought treatment at eye clinics during the study period. We identified patients with corneal opacity using International Classification of Diseases (ICD), Ninth and Tenth Revisions, codes of 371 (corneal scar) and H17 (corneal opacity), respectively. The analyzed data comprised demographic parameters including age, sex, race, ethnicity, and geographical location. We evaluated clinical data including laterality, cause, disease descriptors, and best-corrected visual acuity (VA) up to 1 year before the onset (± 30 days), at the time of diagnosis, and at 1 year after diagnosis (± 30 days). Case frequencies, causes, and vision outcomes in patients with a diagnosis of corneal opacity. We identified 5 220 382 patients who received a diagnosis of corneal opacity and scars using H17 (ICD, Tenth Revision) and 371.0 (ICD, Ninth Revision) codes over 7 years. The case frequency of corneal opacity during the study period was 6535 cases per 100 000 patients (6.5%). The mean age of the patients was 63.36 ± 18.14 years, and most were female (57.6%). In the cohort, 38.39% and 30.00% of patients had bilateral and unilateral corneal opacity, respectively. Most of the patients affected by corneal opacity were White (69.13%), followed by Black or African American (6.84%). Corneal dystrophies (64.66%) were the most common cause of corneal opacity in the study cohort. Visual acuity of the patients worsened significantly because of corneal opacity (0.46 ± 0.74 logarithm of the minimum angle of resolution [logMAR]) and did not improve to the baseline (0.37 ± 0.68 logMAR) after management (0.43 ± 0.77 logMAR). The multiple linear regression analysis showed worse vision outcomes in female patients (compared with male patients), and Asian, Black or African American, and American Indian or Alaska Native (compared with White) patients. Additionally, worse vision outcomes were observed in patients with opacity associated with corneal malformation, degenerative disorders, edema, injury, and ulcer compared with those with hereditary corneal dystrophy. Our study showed that corneal opacity was diagnosed in 6.5% of patients in the IRIS Registry and primarily associated with corneal dystrophies. The final vision outcomes in patients with corneal opacity were significantly worse compared with baseline. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

IRF8 configures enhancer landscape in postnatal microglia and directs microglia specific transcriptional programs.

Microglia are innate immune cells in the brain. Transcription factor IRF8 is highly expressed in microglia. However, its role in postnatal microglia development is unknown. We demonstrate that IRF8 binds stepwise to enhancer regions of postnatal microglia along with Sall1 and PU.1, reaching a maximum after day 14. IRF8 binding correlated with a stepwise increase in chromatin accessibility, which preceded the initiation of microglia-specific transcriptome. Constitutive and postnatal Irf8 deletion led to a loss of microglia identity and gain of disease-associated microglia-like genes. Combined analysis of scRNA-seq and scATAC-seq revealed a correlation between chromatin accessibility and transcriptome at a single-cell level. IRF8 was also required for microglia-specific DNA methylation patterns. Lastly, in the 5xFAD model, constitutive and postnatal Irf8 deletion reduced the interaction of microglia with Aβ plaques and the size of plaques, lessening neuronal loss. Together, IRF8 sets the epigenetic landscape, which is required for postnatal microglia gene expression.