Replication Data Research Articles

NARA: Network-Aware Resource Allocation mechanism for minimizing quality-of-service impact while dealing with energy consumption in volunteer networks

A large-scale volunteer computing system is a type of distributed system in which contributors volunteer their computing resources, such as personal computers or mobile devices, to contribute to a larger computing effort. Volunteer resources are connected over the Internet and together form a powerful computing system capable of providing a service without depending on a service provider. Volunteer network resource allocation is the process of assigning computing tasks or services to a network of volunteer resources. The allocation process includes identifying the needed resources, selecting appropriate volunteers, and assigning tasks or services based on their capabilities. Volunteer computing systems consist of a large number of heterogeneous resources - in terms of processing power, storage, and availability - belonging to different authorities - users or organizations - and exhibiting uncertain behavior in terms of connection, disconnection, capacity, and failure. All of this makes resource allocation a challenging task in terms of ensuring a minimum quality of service, requiring complex algorithms and optimization techniques to ensure that services are efficiently allocated while respecting the constraints of the available resource. This paper introduces the Network-Aware Resource Allocation mechanism, which leverages the location, connectivity, and network latency of volunteer nodes to minimize the time a service runs with degraded quality of service and aims to deal with the energy consumption resulting from data replication requirements. This resource allocation mechanism applies to both the initial deployment of the service in the network and to the reallocation of nodes in the event that one of the allocated nodes fails or becomes unavailable. Our method has been validated in a simulation environment of a realistic volunteer system. The analysis of the results shows how our mechanism meets the quality requirements of users while minimizing the synchronization and replication times of service data replicas, as well as the time that services run with degraded quality of service, reducing the times by more than 70% in the service deployment phase and by more than 60% in the service execution phase. It also helps to reduce overall energy consumption.

The influence of drinking consequences on alcohol expectancy likelihoods and valences: an item-level multi-level approach.

Alcohol expectancy theory proposes that beliefs about drinking motivate or deter drinking. Although expectancies influence drinking, less is known about how the consequences of drinking influence expectancies. We modeled a feedback conceptualization of how the experience of specific consequences influenced people's beliefs about how likely a consequence will occur (i.e., likelihoods) and how positive the consequence will be (i.e., valences). We re-analyzed cross-sectional data from college drinkers (n = 504), using Bayesian cross-classified multilevel ordinal regressions to estimate associations between consequences, likelihoods, and valences. We performed a preregistered replication in new data (n = 362). Participants had higher likelihoods (95% CIStudy 1 = [2.06, 2.43], 95% CIStudy 2 = [1.75, 2.12]) and valences (95% CIStudy 1 = [0.28, 0.52], 95% CIStudy 2 = [0.33, 0.60]) when they had experienced consequences more often, but these associations leveled off at higher consequence frequencies. Participants also believed consequences to be more likely when they viewed them as more positive, and vice versa, and again, these associations leveled off at higher levels of the predictor. Critically, the strength of these associations varied across both people and consequences. Experiencing specific consequences more often was related to people judging them to be more likely and more positive in the future, aligning with alcohol expectancy theory. This may lead to experiencing negative consequences repeatedly because people are not being demotivated from drinking in the same fashion. Given the person and consequence level variability, clinicians should consider an individualized approach when targeting drinking consequences.

EFP Analyzer: A fast, accurate, and easy-to-teach program for analyzing Extracellular Field Potentials from iPSC-derived cardiomyocytes.

Induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) are an emerging model for determining drug effects and modeling disease. Specialized devices can generate Extracellular Field Potential (EFP) measurements from these cells, analogous to the ventricular complex of the electrocardiogram. The objective of this study was to develop an easy-to-use, easy-to-teach, reproducible software tool to measure EFPs. We present the EFP-Analyzer (EFPA), a semi-automized analyzer for EFP traces, which identifies and averages beats, identifies landmarks, and calculates intervals. We evaluated the tool in an analysis of 358 EFP traces from 22 patient-derived lines. We analyzed spontaneously beating iPSC-CMs, as well as optically paced iPSC-CMs through channelrhodopsin. We developed stringent quality criteria and measured EFP intervals, including Field Potential Duration (FPD). FPD from optically paced iPSC-CMs were shorter than those of spontaneously beating iPSC-CMs (283.7.0±54.2 vs. 293.0±47.5, p: 0.32, respectively). We further analyzed the usability and data replicability of EFPA through an inter-intra observer analysis. Correlation coefficient for inter-reader tangent and threshold measurements for these FPD ranged between r: 0.93-1.00. Bland-Altman plots comparing inter observer results for spontaneously beating and paced iPSC-CMs showed 95% limits of agreement (-13.6 to 19.4ms and -13.2 to 15.3ms, respectively). The EFP-analyzer could accurately detect FPD prolongation due to drug (moxifloxacin) or pathogenic loss of function mutations ( CACNA1C N639T). This program is available for download at https://github.com/kroncke-lab/EFPA . The instructions will be available at the same listed website under the README section of the Github main page. The EFP-Analyzer tool is a useful tool that enables the efficient use of iPSC-CMs as a model to study drug effects and disease.

Replicable and extensible spatial data acquisition

ABSTRACT Having the ability to reproduce empirical results is foundational to the scientific process. Increasingly, there is an emphasis within the research community to create workflows which are not only reproducible but also replicable in that analytical approaches can be applied to new data. However, a disproportionate number of technical solutions designed to foster reproducibility and replicability have focused on data analysis, particularly within geography. This paper argues for greater attention on a phase of the research process which precedes analysis and is often taken for granted – that of data acquisition. Through code examples using the R Project for Statistical Computing, this paper demonstrates a path toward replicable spatial data acquisition for both secondary and primary data acquisition, with a focus on crowdsourced geographic information. The modular approach demonstrated is flexible in that it allows for straightforward replication but also enables extension to new spatial questions. Such work is valuable because it conserves labor, promotes research provenance, and allows for more robust analyses.

Implementation of High Availability Based on Load Balancing and Failover Method in E-commerce Using MySQL Database

The rapid growth of e-commerce has brought about challenges in managing traffic loads and ensuring the availability of database servers. One critical issue is what happens when the main database server fails, potentially leading to downtime or data loss. This research aims to address these problems by optimizing server resource distribution and implementing data replication across multiple database servers. The goal is to enhance system efficiency, prevent server overloads, ensure data availability, and minimize downtime or data loss when server failures occur.This research utilizes a multi-stage methodology. First, data collection was conducted to gather relevant information. In the second stage, a needs analysis was performed to identify system requirements. The third stage involved designing the system architecture, including the creation of entity-relationship diagrams (ERD), flowcharts, and network topologies. In the final stage, the system was implemented through database replication and the application of failover methods.The results demonstrate that the failover mechanism operates effectively, ensuring that data is successfully replicated across all database servers. This confirms that the system achieves high availability and minimizes the risk of data loss or service downtime. The implications of this research provide a reliable solution for improving the resilience and performance of e-commerce systems, particularly in managing database server failures.

Optimizing Web-Based Survey Applications with Laravel and Cloud Computing

This study aims to optimize the performance of a web-based survey application through the implementation of the Laravel framework and cloud computing technology. Survey applications often face challenges regarding response speed and scalability as the number of users increases. In this research, performance testing of the application was conducted both before and after optimization. The test results show that the application's response time significantly decreased after the implementation of Laravel and cloud technology. Before optimization, the average response time reached 4.5 seconds at 1,000 users, while after optimization, it dropped to 2.2 seconds. This performance improvement was achieved through efficient caching implementation, database query optimization using Eloquent ORM, and balanced load distribution via load balancers. Additionally, the application's availability increased to 99.9% thanks to cloud features such as auto-scaling and data replication. The conclusion of this study indicates that the combination of Laravel and cloud computing technology is effective in enhancing the performance and reliability of web-based survey applications, providing practical guidance for other web application developers facing similar challenges.

Exploring the potential for a 9000-year tree-ring chronology consisting of subfossil oak material from southern Scandinavia

In this study, we present the initial results from a long-term initiative to develop a multi-millennial tree-ring width (TRW) chronology for subfossil oak (Quercus spp.) trees in South Scandinavia. We have constructed 40 TRW chronologies, primarily from oak trunks extracted from peatlands, archaeological sites, drainage ditches, and infrastructure projects, as well as some submerged material. The trees included in these chronologies have been dendrochronologically dated and/or dated by radiocarbon, showing a temporal spread from 6911 ± 246 BCE to 1117 CE. Significant assemblages of trees are found during the periods 6000–4500 BCE, 3500–2600 BCE, 1750–1200 BCE, 900–400 BCE, and 200 BCE–500 CE, with intervening periods exhibiting low sample replication or gaps. The paleoclimatic information extracted from TRW and replication data may vary based on the substrate in which the trees were rooted, geographical locations, and the prevailing climate conditions during their growth. Nevertheless, extending the oak TRW chronologies is crucial because (1) no such long and annually resolved climate records from southern Scandinavia exist, (2) there is a widespread lack of detailed paleoclimatic proxies spanning multi-millennial periods, and (3) it will provide an essential dating tool for wooden archaeological artifacts. This study is the first in a series aimed at refining paleoclimatic and ecological reconstructions in southern Scandinavia. It demonstrates the potential to develop South Scandinavian TRW chronologies covering the last 9000 years, filling a geographical gap in an ecologically sensitive region.

Comprehensive data security and compliance framework for SMEs

Small and Medium-sized Enterprises (SMEs) are increasingly relying on cloud platforms to support critical business operations, making effective disaster recovery (DR) strategies essential for ensuring business continuity. This review proposes a robust disaster recovery framework tailored for SMEs, designed to minimize downtime and data loss in the event of a system failure, cyberattack, or natural disaster. The framework integrates advanced cloud technologies to create a cost-effective, scalable solution that aligns with the resource constraints of SMEs while providing enterprise-grade resilience. Key components of the disaster recovery framework include cloud-based data replication, automated backup solutions, and geo-redundant storage to ensure that data is continuously available and recoverable. This model employs real-time data synchronization and incremental backups to minimize Recovery Point Objectives (RPO), ensuring that critical data is not lost during an unexpected outage. Additionally, the framework leverages automated failover mechanisms to achieve low Recovery Time Objectives (RTO), allowing businesses to restore operations quickly after an interruption. Cloud orchestration tools such as AWS Elastic Disaster Recovery or Azure Site Recovery are utilized to automate disaster recovery processes, reducing manual intervention and improving the speed of recovery. The framework also incorporates regular testing of disaster recovery plans, using simulation tools to identify weaknesses and optimize response times. For SMEs, cost-effectiveness and ease of management are crucial. The framework emphasizes a pay-as-you-go model for cloud resources, allowing businesses to scale their disaster recovery solutions as they grow without incurring excessive upfront costs. By providing continuous monitoring and proactive threat detection, this disaster recovery framework ensures that SMEs can maintain uninterrupted business operations on cloud platforms, thereby enhancing resilience and mitigating the financial and operational risks associated with data loss and system downtime.

Beyond hinges and spires: A critical examination of archaeomalacological quantification methodologies using coral reef molluscan assemblages from Jiigurru (Lizard Island Group), northern Great Barrier Reef

Quantification, as one of the pillars of the zooarchaeological subdiscipline, is an invaluable component of the toolkit researchers use to study past people-animal interactions. Despite being the subject of rigorous (zoo)archaeological debate, the calculation of Minimum Number of Individuals (MNI) values remains one of the main methods of quantifying the relative abundance of taxa within faunal assemblages. Choosing the appropriate quantification protocol to calculate the MNI of archaeological invertebrate assemblages can be challenging due to regional taxonomic considerations and the myriad of quantification methodologies and frameworks available in the global archaeomalacological literature. In an Australian context, methodologies for quantifying coral reef molluscan assemblages have not been explicitly evaluated. Using archaeological molluscan assemblages from two midden sites (Freshwater Bay Midden and Mangrove Beach Headland Midden) on the northern Great Barrier Reef island group of Jiigurru (the Lizard Island Group), we critically examine two commonly adopted archaeomalacological quantification methodologies: the NRE MNI and tMNI protocols. The NRE MNI methodology uses one to two non-repetitive elements (NREs) of molluscs, whilst the tMNI protocol includes a wider range of elements akin to vertebrate MNI quantification methodologies. Through a comparison of taxa abundances and statistical analyses, results show that the tMNI protocol, with some modification, is best suited for the Jiigurru assemblages. Higher MNI values and an increased assemblage diversity, evenness, and richness were recorded for the molluscan assemblages at both midden sites when the tMNI protocol was applied. This study foregrounds the importance of data transparency when reporting quantification protocols and outcomes to ensure the highest degree of data quality, replicability, and usability.

Self-supervised contrastive learning improves machine learning discrimination of full thickness macular holes from epiretinal membranes in retinal OCT scans.

There is a growing interest in using computer-assisted models for the detection of macular conditions using optical coherence tomography (OCT) data. As the quantity of clinical scan data of specific conditions is limited, these models are typically developed by fine-tuning a generalized network to classify specific macular conditions of interest. Full thickness macular holes (FTMH) present a condition requiring urgent surgical repair to prevent vision loss. Other works on automated FTMH classification have tended to use supervised ImageNet pre-trained networks with good results but leave room for improvement. In this paper, we develop a model for FTMH classification using OCT B-scans around the central foveal region to pre-train a naïve network using contrastive self-supervised learning. We found that self-supervised pre-trained networks outperform ImageNet pre-trained networks despite a small training set size (284 eyes total, 51 FTMH+ eyes, 3 B-scans from each eye). On three replicate data splits, 3D spatial contrast pre-training yields a model with an average F1-score of 1.0 on holdout data (50 eyes total, 10 FTMH+), compared to an average F1-score of 0.831 for FTMH detection by ImageNet pre-trained models. These results demonstrate that even limited data may be applied toward self-supervised pre-training to substantially improve performance for FTMH classification, indicating applicability toward other OCT-based problems.

PCA, PC-CVA, and Random Forest of GCIB-SIMS Data for the Elucidation of Bacterial Envelope Differences in Antibiotic Resistance Research.

Antibiotic resistance can rapidly spread through bacterial populations via bacterial conjugation. The bacterial membrane has an important role in facilitating conjugation, thus investigating the effects on the bacterial membrane caused by conjugative plasmids, antibiotic resistance, and genes involved in conjugation is of interest. Analysis of bacterial membranes was conducted using gas cluster ion beam-secondary ion mass spectrometry (GCIB-SIMS). The complexity of the data means that data analysis is important for the identification of changes in the membrane composition. Preprocessing of data and several analytical methods for identification of changes in bacterial membranes have been investigated. GCIB-SIMS data from Escherichia coli samples were subjected to principal components analysis (PCA), principal components-canonical variate analysis (PC-CVA), and Random Forests (RF) data analysis with the aim of extracting the maximum biological information. The influence of increasing replicate data was assessed, and the effect of diminishing biological variation was studied. Optimized m/z region-specific scaling provided improved clustering, with an increase in biologically significant peaks contributing to the loadings. PC-CVA improved clustering, provided clearer loadings, and benefited from larger data sets collected over several months. RF required larger sample numbers and while showing overlap with the PC-CVA, produced additional peaks of interest. The combination of PC-CVA and RF allowed very subtle differences between bacterial strains and growth conditions to be elucidated for the first time. Specifically, comparative analysis of an E. coli strain with and without the F-plasmid revealed changes in cyclopropanation of fatty acids, where the addition of the F-plasmid led to a reduction in cyclopropanation.

RESOURCE PROCUREMENT AND CLIMATE SMART AGRICULTURE IN LAIKIPIA COUNTY, KENYA

Objective: The objective of this study is to determine how resource procurement influences climate-smart agriculture projects in Laikipia County, Kenya. Theoretical Framework: This study is anchored on Transaction Cost Economics Theory, which states that a project will attract transaction costs all through the procurement process. This study anchors on this theory as small-scale farmers need to be innovative to reduce procurement costs and have bargaining power over exploitive stockists. Method: A concurrent mixed method approach that adopted the descriptive cross-sectional survey and correlational design was employed to study two World Bank-sponsored Kenya Climate Smart Agriculture projects. Stratified and Simple random sampling were employed to get a sample of 225 small-scale farmers and purposeful sampling identified four key informants. Data was collected using questionnaires and an interview guide and analyzed using descriptive, inferential, and content data analysis techniques. Results and Discussion: The composite mean and standard deviation of 3.25 and 1.163 respectively. The relationship between resource procurement and the performance of climate-smart agriculture projects had a fairly strong linear coefficient of correlation (r= 0.524) and p-value of (p=0.000<0.05) indicating a fairly strong and significant relationship. Therefore, effective procurement of inputs, machinery, and other resources is critical in sustainable agriculture. Research Implications: Consequently, it is critical to have policies that promote effective procurement and safeguard small-scale farmers from exploitive stockists. Originality/Value: This study contributes to the literature by providing reliable and triangulated empirical data through authentic methodology enhancing suitability for data generalisability and replicability. The relevance and value of this research is evidenced by the need to promote food security in a healthy ecology

RESOURCE ALLOCATION AND CLIMATE SMART AGRICULTURE IN LAIKIPIA COUNTY, KENYA

Objective: The objective of this study was to determine how resource allocation influences climate-smart agriculture projects in Laikipia County, Kenya. Theoretical Framework: Pareto Efficiency Theory states that at a certain level, it is not possible to utilize more resources to improve one party, without jeopardizing the other party. The study sought to heighten the level of food production to guarantee food security without threatening the environment. Method: A concurrent mixed method approach that adopted the descriptive cross-sectional survey and correlational design was employed to study two World Bank-sponsored Kenya Climate Smart Agriculture projects. Stratified and Simple random sampling were employed to get a sample of 225 small-scale farmers and purposeful sampling identified four key informants. Data was collected using questionnaires and an interview guide and analyzed using descriptive, inferential, and content data analysis techniques. Results and Discussion: The composite mean and standard deviation of the respondents' opinions on resource allocation were 3.52 and 1.143, respectively. The correlation coefficient of resource allocation and the performance of climate-smart agriculture projects was weak, with r=-0.220 and p-value p=0.000<0.05. Therefore, though resource allocation is a crucial factor in food production, judicious and effective resource allocation is critical to realizing the full potential of the projects. Research Implications: Consequently, it is critical to have policies that promote the effective allocation of public and private goods to promote food production coupled with enhanced healthy ecology. Originality/Value: This study contributes to the literature by providing reliable and triangulated empirical data through authentic methodology enhancing suitability for data generalisability and replicability. The relevance and value of this research is evidenced by the need to promote food security in a healthy ecology.

RESOURCE STEWARDSHIP AND CLIMATE SMART AGRICULTURE IN LAIKIPIA COUNTY, KENYA

Objective: To examine the extent to which resource stewardship influences the performance of climate-smart agriculture projects in Laikipia County, Kenya. Theoretical Framework: The study was anchored on the Stewardship theory that envisages that unsupervised managers, will derive their satisfaction from the organization's success hence, remain accountable stewards. Judicious stewardship is the only way to achieve food security in a healthy ecology. Method: A concurrent mixed method approach that adopted the descriptive cross-sectional survey and correlational design was employed to study two World Bank-sponsored Kenya Climate Smart Agriculture projects. Stratified and Simple random sampling were employed to get a sample of 225 small-scale farmers and purposeful sampling identified four key informants. Data was collected using questionnaires and interview guide and analyzed using descriptive, inferential, and content data analysis techniques. Results and Discussion: The respondents’ opinions on resource stewardship had a composite mean and standard deviation of 3.47 and 1.138 respectively. The relationship between resource stewardship and the performance of climate-smart projects had a weak linear correlation of (r= -0.007) and p-value (p=0.923>0.05) indicating a weak and insignificant relationship. Despite the portrayed confidence in resource stewardship, stewardship is not a quick-fix approach hence, detested in the use of resources. Research Implications: Consequently, it is vital to have policies that enforce, promote, and subsidize activities that stimulates stewardship in food production. Originality/Value: This study contributes to the literature by providing reliable and triangulated empirical data through authentic methodology enhancing suitability for data generalisability and replicability. The relevance and value of this research are evidenced by the need to promote food security in pursuance of the achievement of sustainable development goals.

RESOURCE CAPACITY PLANNING AND CLIMATE SMART AGRICULTURE IN LAIKIPIA COUNTY, KENYA

Objective: The purpose of this study was to establish the extent to which capacity planning influences the performance of climate-smart agriculture projects in Laikipia County. Theoretical Framework: The study was anchored on the theory of change whose pillars are planning, participation, and evaluation. These factors are critical in capacity planning of scarce resources and evaluating the effectiveness of the process. Method: A concurrent mixed method approach that adopted the descriptive cross-sectional survey and correlational design was employed to study two World Bank-sponsored Kenya Climate Smart Agriculture projects. Stratified and Simple random sampling were employed to get a sample of 225 small-scale farmers and purposeful sampling identified four key informants. Data was collected using questionnaires and interview guide and analyzed using descriptive, inferential, and content data analysis techniques. Results and Discussion: The respondent’s opinion on capacity planning had a composite mean and standard deviation of 2.88 and 1.219 respectively. Capacity planning and the performance of climate-smart agriculture projects had a strong correlation coefficient of r=0.644 and p-value p=0.000<0.05. Therefore, resource capacity planning is fundamental in enhancing climate-smart projects, through proactive decisions, risk management, and cost reduction. Research Implications: Consequently, it is imperative to have policies that prioritize and promote capacity planning of the scarce public and private goods to enhance their allocation. Originality/Value: This study contributes to the literature by providing reliable and triangulated empirical data through authentic methodology enhancing suitability for data generalisability and replicability. The relevance and value of this research was evidenced by the need to promote food security in pursuance of the achievement of sustainable development goals.

Consensus in Data Management: With Use Cases in Edge-Cloud and Blockchain Systems

Consensus is a fundamental problem in distributed systems, involving the challenge of achieving agreement among distributed nodes. It plays a critical role in various distributed data management problems. This tutorial aims to provide a comprehensive primer for data management researchers on the topic of consensus and its fundamental and modern applications in data management. We begin by exploring the basic principles of consensus, including the problem statement, system models, failure scenarios, and various consensus algorithms such as Paxos and its variants. The tutorial then delves into the applications of consensus in distributed data management, focusing on distributed atomic commitment and data replication. We explain how consensus is integral to these areas and present examples of research and industry work that apply consensus to data management. The tutorial extends to modern use cases of consensus in the evolving landscapes of edge-cloud systems and blockchain technology. We discuss how consensus mechanisms are being adapted and applied in these areas, highlighting their growing importance in emerging areas of data management. By exploring these cutting-edge applications, participants will gain insights into how consensus is shaping ongoing and future research on distributed data management. The tutorial builds on the authors' recent book "Consensus in Data Management: from Distributed Commit to Blockchain". The book will serve as the foundation and reading material for the tutorial. This tutorial targets data management researchers and practitioners to equip them with the knowledge and perspective needed to innovate in these emerging fields. This includes graduate students and junior researchers starting their careers in the area of distributed data management. Also, it includes researchers in other areas of data management who wish to explore the area of distributed data management with the goal of utilizing it in their own fields.

Standardizing zebrafish laboratory husbandry to ensure replicability and reproducibility of data in neurobehavioral research.

Standardizing zebrafish laboratory husbandry to ensure replicability and reproducibility of data in neurobehavioral research.

Associations between genetically predicted concentrations of plasma proteins and the risk of prostate cancer

BackgroundProstate cancer (PCa) is a leading cause of cancer-related death in men. Understanding the proteomic landscape associated with PCa risk can provide insights into its molecular mechanisms and pave the way for potential therapeutic interventions.MethodsA proteome-wide Mendelian randomization (MR) analysis was employed to determine associations between genetically predicted protein concentrations in plasma and PCa risk. From an initial list of 4,364 proteins, significant associations were identified and validated. Multiple sensitivity analyses were also conducted to enhance the robustness of our findings.ResultsOf the 4,364 genetically predicted proteins, 308 exhibited preliminary associations with PCa risk. After rigorous statistical refinement, genetically predicted concentrations of 14 proteins showed positive associations with PCa risk, with odds ratios spanning from 1.55 (95% CI 1.28–1.87) for ATG4B to 2.67 (95% CI 1.94–3.67) for HCN1. In contrast, genetically predicted concentrations of ATG7, B2M, MSMB, and TMEM108 demonstrated inverse associations with PCa. The replication analysis further substantiated positive associations for MDH1 and LSM1, and a negative one for MSMB with PCa. A meta-analysis harmonizing primary and replication data mirrored these findings. Furthermore, the MVMR analysis pinpointed B2M and MSMB as having significant associations with PCa risk.ConclusionThe genetic evidence unveils a refined set of proteins associated with PCa risk. The findings underscore the potential of these proteins as molecular markers or therapeutic targets for PCa, calling for deeper mechanistic studies and exploration into their translational relevance.

Benchmarking a universal smoking machine adaptor for tobacco product testing

SignificanceCharacterisation of tobacco product emissions is an important step in assessing their impact on public health. Accurate and repeatable emissions data require that a leak-tight seal be made between the...

Plasma proteins associated with plant-based diets: Results from the Atherosclerosis Risk in Communities (ARIC) study and Framingham Heart Study (FHS)

Background & AimsPlant-based diets are associated with a lower risk of chronic diseases. Large-scale proteomics can identify objective biomarkers of plant-based diets, and can improve our understanding of the pathways that link plant-based diets to health outcomes. This study investigated the plasma proteome of four different plant-based diets [overall plant-based diet (PDI), provegetarian diet, healthful plant-based diet (hPDI), and unhealthful plant-based diet (uPDI)] in the Atherosclerosis Risk in Communities (ARIC) Study and replicated the findings in the Framingham Heart Study (FHS) Offspring cohort. MethodsARIC Study participants at visit 3 (1993-1995) with completed food frequency questionnaire (FFQ) data and proteomics data were divided into internal discovery (n=7,690) and replication (n=2,543) data sets. Multivariable linear regression was used to examine associations between plant-based diet indices (PDIs) and 4,955 individual proteins in the discovery sample. Then, proteins that were internally replicated in the ARIC Study were tested for external replication in FHS (n=1,358). Pathway overrepresentation analysis was conducted for diet-related proteins. C-statistics were used to predict if the proteins improved prediction of plant-based diet indices beyond participant characteristics. ResultsIn ARIC discovery, a total of 837 diet-protein associations (PDI=233; provegetarian=182; hPDI=406; uPDI=16) were observed at false discovery rate (FDR) <0.05. Of these, 453 diet-protein associations (PDI=132; provegetarian=104; hPDI=208; uPDI=13) were internally replicated. In FHS, 167/453 diet-protein associations were available for external replication, of which 8 proteins (PDI=1; provegetarian=0; hPDI=8; uPDI=0) replicated. Complement and coagulation cascades, cell adhesion molecules, and retinol metabolism were over-represented. C-C motif chemokine 25 for PDI and 8 proteins for hPDI modestly but significantly improved the prediction of these indices individually and collectively (P value for difference in C-statistics<0.05 for all tests) ConclusionsUsing large-scale proteomics, we identified potential candidate biomarkers of plant-based diets, and pathways that may partially explain the associations between plant-based diets and chronic conditions.