Replication Data Research Articles

Features of aging replicative and biochemical aspects among females with non-iatrogenic hypergonadotropic hypogonadism

Estrogenic deficiency is the basic condition of human ageing that leads to hypergonadotropic hypogonadism. The existence of correlation between hypergonadotropic hypogonadism, replicative (leukocyte telomere length) and biochemical data is widely supposed among females with physiological (menopausal) and pathological (primary ovarian insufficiency) estrogenic deficiency is not unreasonable. To evaluate features of replicative (telomere length) and biochemical (metabolic syndrome) ageing markers among females with physiological (menopausal) and pathological (primary ovarian insufficiency) estrogenic deficiency. Research has been provided in collaboration between Endocrinology Research Centre of the Russian Ministry of Health and Lomonosov Moscow State University Medical Research and Educational Centre in the period since 10.01.2021 until 01.08.2022.110 females (20-75y.o.) have participated in the present research.Group 1: 26 females receiving menopausal hormonal therapy (MHT) ≥ 5 years with 0,5; 1; 2 mg estrogenic component.Group 2: 27 females in physiological menopause without MHTGroup 3: 33 females with primary ovarian insufficiency and receiving sex-steroid replacement therapy.Group 4: 24 healthy reproductive age females without sex-steroid replacement therapy.Patients have undergone laboratory genetic (leucocyte telomere length), biochemical analyses.DNA extraction - with Qiagen DNA blood mini kit (Germany). Biological material was cito conserved with Ficoll solution. Leukocyte telomere length - with real-time polymerase chain reaction PCR (Flow-fish).Soft program IBM SPSS Statistics (version 26,0 for Windows) has been used for statical analysis. 1.Menopausal females receiving MHT were inclined to highest HDL-P levels (p<0,006).2.Females with primary ovarian insufficiency were inclined to relatively highest serum creatinine level (p<0,001).3.Reproductive age females had relatively highest telomere length (p<0,001).4.FSH level correlates negatively and moderately (ρ= - 0,434) leukocyte telomere length (р<0,001) among females. Females with premature ovarian insufficiency are most sensible to ageing due to features of replicative and biochemical markers.

Systematic Approaches to Data Placement, Replication and Migration in Heterogeneous Edge-Cloud Computing Systems: A Comprehensive Literature Review

Systematic Approaches to Data Placement, Replication and Migration in Heterogeneous Edge-Cloud Computing Systems: A Comprehensive Literature Review

Optimized quantification of intra-host viral diversity in SARS-CoV-2 and influenza virus sequence data.

High error rates of viral RNA-dependent RNA polymerases lead to diverse intra-host viral populations during infection. Errors made during replication that are not strongly deleterious to the virus can lead to the generation of minority variants. However, accurate detection of minority variants in viral sequence data is complicated by errors introduced during sample preparation and data analysis. We used synthetic RNA controls and simulated data to test seven variant-calling tools across a range of allele frequencies and simulated coverages. We show that choice of variant caller and use of replicate sequencing have the most significant impact on single-nucleotide variant (SNV) discovery and demonstrate how both allele frequency and coverage thresholds impact both false discovery and false-negative rates. When replicates are not available, using a combination of multiple callers with more stringent cutoffs is recommended. We use these parameters to find minority variants in sequencing data from SARS-CoV-2 clinical specimens and provide guidance for studies of intra-host viral diversity using either single replicate data or data from technical replicates. Our study provides a framework for rigorous assessment of technical factors that impact SNV identification in viral samples and establishes heuristics that will inform and improve future studies of intra-host variation, viral diversity, and viral evolution. IMPORTANCE When viruses replicate inside a host cell, the virus replication machinery makes mistakes. Over time, these mistakes create mutations that result in a diverse population of viruses inside the host. Mutations that are neither lethal to the virus nor strongly beneficial can lead to minority variants that are minor members of the virus population. However, preparing samples for sequencing can also introduce errors that resemble minority variants, resulting in the inclusion of false-positive data if not filtered correctly. In this study, we aimed to determine the best methods for identification and quantification of these minority variants by testing the performance of seven commonly used variant-calling tools. We used simulated and synthetic data to test their performance against a true set of variants and then used these studies to inform variant identification in data from SARS-CoV-2 clinical specimens. Together, analyses of our data provide extensive guidance for future studies of viral diversity and evolution.

High Availability and Fault Tolerance in AWS

With the increasing adoption of cloud computing, ensuring high availability and fault tolerance has become paramount for organizations. Amazon Web Services (AWS) offers a robust infrastructure for hosting applications, but it requires careful architectural design and implementation to achieve desired levels of availability and fault tolerance. This research paper explores two innovative concepts, namely Cloud Fractal and Decentralized Replication and Orchestration, and their application in achieving high availability and fault tolerance in AWS. We present a comprehensive analysis of these concepts and provide practical guidelines for their implementation in real-world scenarios. Our findings demonstrate the effectiveness of Cloud Fractal and Decentralized Replication and Orchestration in enhancing the reliability and resilience of AWS deployments. Keywords: Fault Isolation, High Availability, Load Balancing, Auto-scaling, Disaster Recovery, Data Replication, Backup and Restore, Fault Tolerance, Cloud Computing, Amazon Elastic Compute Cloud (EC2), Amazon Relational Database Service (RDS), Amazon Simple Storage Service (S3), AWS Elastic Load Balancer (ELB), Complexity, Operational Management, Cloud Fractal, AWS Lambda, Amazon SQS, Decentralized Replication and Orchestration (DOI).

Life Cycle Assessment (LCA) of Worsted and Woollen processing in wool production: ReviWool® noils and other wool co-products

The textile and fashion industry is becoming increasingly active in measuring its environmental performance. As far as wool is concerned, there is quite abundant literature on environmental impacts available. However, previous studies very rarely distinguish between the different co-products of the wool transformation, and often attribute the same impact to fibers produced from worsted processing (longer and more expensive fibers) and woollen processing (shorter and cheaper fibers). This study firstly provides a detailed mapping of processes and products involved in the wool production chain, from sheep grazing to yarn production, with particular attention to the shorter fibers, which have been mostly neglected in previous literature. Secondly, this study uses the Life Cycle Assessment (LCA) methodology to analyze the environmental impacts of the different intermediate co-products. In particular, when multi-output processes occur, impacts are distributed proportionally to their relative economic value, using therefore an economic allocation. This approach enabled the calculation of environmental impacts of fibers used both in the worsted and woollen processing. It results that shorter fibers generally have lower impacts than longer fibers used for the production of fine yarns. Specifically, most short fibers have an impact on climate change ranging from 25 to 30 kg CO2 eq/kg, while, longer fibers have an impact of 78–97 kg CO2 eq/kg. The physiological variation in the ratio between worsted and woolen co-products of multi-output processes appears to have little effect on the final impact results. Finally, since the grazing phase is highly variable, impacts on climate change of the analyzed intermediate products have been re-calculated using, for the greasy wool, the lowest and highest values of impact found in literature. Impacts of the analyzed products vary sensibly according to the value considered for the greasy wool, but the relationship between them is rather stable.This paper contributes with detailed information and easily replicable data which could be used as a basis for the environmental assessment of wool garments and for improving the sustainability in the wool sector.

A Novel Honestly Adjustable Replication Algorithm to Minimize Data Replication and Enhance Data Reliable Transport in Wireless Sensor Networks

A Novel Honestly Adjustable Replication Algorithm to Minimize Data Replication and Enhance Data Reliable Transport in Wireless Sensor Networks

Modular Software for Generating and Modeling Diverse Polymer Databases.

Machine learning methods offer the opportunity to design new functional materials on an unprecedented scale; however, building the large, diverse databases of molecules on which to train such methods remains a daunting task. Automated computational chemistry modeling workflows are therefore becoming essential tools in this data-driven hunt for new materials with novel properties, since they offer a means by which to create and curate molecular databases without requiring significant levels of user input. This ensures that well-founded concerns regarding data provenance, reproducibility, and replicability are mitigated. We have developed a versatile and flexible software package, PySoftK (Python Soft Matter at King's College London) that provides flexible, automated computational workflows to create, model, and curate libraries of polymers with minimal user intervention. PySoftK is available as an efficient, fully tested, and easily installable Python package. Key features of the software include the wide range of different polymer topologies that can be automatically generated and its fully parallelized library generation tools. It is anticipated that PySoftK will support the generation, modeling, and curation of large polymer libraries to support functional materials discovery in the nanotechnology and biotechnology arenas.

An extended period of elevated influenza mortality risk follows the main waves of influenza pandemics: Replication code and data

An extended period of elevated influenza mortality risk follows the main waves of influenza pandemics: Replication code and data

Data Distribution for Heterogeneous Storage Systems

The exponential growth of data in many science and engineering domains poses significant challenges to storage systems. Data distribution is a critical component in large-scale distributed storage systems and plays a vital role in placing petabytes of data and beyond, among tens to hundreds of thousands of storage devices. Meantime, heterogeneous storage systems, such as those having devices with hard disk drives (HDDs) and storage class memories (SCMs), have become increasingly popular for massive data storage due to their distinct and complement characteristics. This paper presents a new data distribution algorithm called SUORA (Scalable and Uniform storage via Optimally-adaptive and Random number Addressing) specifically for heterogeneous devices to maximize the benefits of them. SUORA provides a fully symmetric, highly efficient methodology to distribute data across a hybrid and tiered storage cluster. It divides heterogeneous devices into different buckets and segments, and adopts pseudo-random functions to map data onto them with the balanced consideration of capacity, performance and life-time. By analyzing hotness and access patterns, SUORA gradually moves hot data from HDDs to SCMs to optimize the throughput, and moves cold data reversely for load balance. It combines data replication with migration to significantly reduce movement overhead while making data placement more adaptive to different workloads. Extensive evaluations on simulation and Sheepdog storage system show that, with considering distinct characteristics of various devices thoroughly, SUORA improves the overall performance efficiency of heterogeneous storage systems.

A divide and conquer based development of gray wolf optimizer and its application in data replication problem in distributed systems

A divide and conquer based development of gray wolf optimizer and its application in data replication problem in distributed systems

GeoGauss: Strongly Consistent and Light-Coordinated OLTP for Geo-Replicated SQL Database

Multinational enterprises conduct global business that has a demand for geo-distributed transactional databases. Existing state-of-the-art databases adopt a sharded master-follower replication architecture. However, the single-master serving mode incurs massive cross-region writes from clients, and the sharded architecture requires multiple round-trip acknowledgments (e.g., 2PC) to ensure atomicity for cross-shard transactions. These limitations drive us to seek yet another design choice. In this paper, we propose a strongly consistent OLTP database GeoGauss with full replica multi-master architecture. To efficiently merge the updates from different master nodes, we propose a multi-master OCC that unifies data replication and concurrent transaction processing. By leveraging an epoch-based delta state merge rule and the optimistic asynchronous execution, GeoGauss ensures strong consistency with light-coordinated protocol and allows more concurrency with weak isolation, which are sufficient to meet our needs. Our geo-distributed experimental results show that GeoGauss achieves 7.06X higher throughput and 17.41X lower latency than the state-of-the-art geo-distributed database CockroachDB on the TPC-C benchmark.

Consistency issue and related trade-offs in distributed replicated systems and databases: a review

Distributed replicated databases play a crucial role in modern computer systems enabling scalable, fault-tolerant, and high-performance data management. However, achieving these qualities requires resolving a number of trade-offs between various properties during system design and operation. This paper reviews trade-offs in distributed replicated databases and provides a survey of recent research papers studying distributed data storage. The paper first discusses a compromise between consistency and latency that appears in distributed replicated data storages and directly follows from CAP and PACELC theorems. Consistency refers to the guarantee that all clients in a distributed system observe the same data at the same time. To ensure strong consistency, distributed systems typically employ coordination mechanisms and synchronization protocols that involve communication and agreement among distributed replicas. These mechanisms introduce additional overhead and latency and can dramatically increase the time taken to complete operations when replicas are globally distributed across the Internet. In addition, we study trade-offs between other system properties including availability, durability, cost, energy consumption, read and write latency, etc. In this paper we also provide a comprehensive review and classification of recent research works in distributed replicated databases. Reviewed papers showcase several major areas of research, ranging from performance evaluation and comparison of various NoSQL databases to suggest new strategies for data replication and putting forward new consistency models. In particular, we observed a shift towards exploring hybrid consistency models of causal consistency and eventual consistency with causal ordering due to their ability to strike a balance between operations ordering guarantees and high performance. Researchers have also proposed various consistency control algorithms and consensus quorum protocols to coordinate distributed replicas. Insights from this review can empower practitioners to make informed decisions in designing and managing distributed data storage systems as well as help identify existing gaps in the body of knowledge and suggest further research directions.

The Inner Complexities of Multimodal Medical Data: Bitmap-Based 3D Printing for Surgical Planning Using Dynamic Physiology.

Motivated by the need to develop more informative and data-rich patient-specific presurgical planning models, we present a high-resolution method that enables the tangible replication of multimodal medical data. By leveraging voxel-level control of multimaterial three-dimensional (3D) printing, our method allows for the digital integration of disparate medical data types, such as functional magnetic resonance imaging, tractography, and four-dimensional flow, overlaid upon traditional magnetic resonance imaging and computed tomography data. While permitting the explicit translation of multimodal medical data into physical objects, this approach also bypasses the need to process data into mesh-based boundary representations, alleviating the potential loss and remodeling of information. After evaluating the optical characteristics of test specimens generated with our correlative data-driven method, we culminate with multimodal real-world 3D-printed examples, thus highlighting current and potential applications for improved surgical planning, communication, and clinical decision-making through this approach.

Online Task Scheduling of Big Data Applications in the Cloud Environment

The development of big data has generated data-intensive tasks that are usually time-consuming, with a high demand on cloud data centers for hosting big data applications. It becomes necessary to consider both data and task management to find the optimal resource allocation scheme, which is a challenging research issue. In this paper, we address the problem of online task scheduling combined with data migration and replication in order to reduce the overall response time as well as ensure that the available resources are efficiently used. We introduce a new scheduling technique, named Online Task Scheduling algorithm based on Data Migration and Data Replication (OTS-DMDR). The main objective is to efficiently assign online incoming tasks to the available servers while considering the access time of the required datasets and their replicas, the execution time of the task in different machines, and the computational power of each machine. The core idea is to achieve better data locality by performing an effective data migration while handling replicas. As a result, the overall response time of the online tasks is reduced, and the throughput is improved with enhanced machine resource utilization. To validate the performance of the proposed scheduling method, we run in-depth simulations with various scenarios and the results show that our proposed strategy performs better than the other existing approaches. In fact, it reduces the response time by 78% when compared to the First Come First Served scheduler (FCFS), by 58% compared to the Delay Scheduling, and by 46% compared to the technique of Li et al. Consequently, the present OTS-DMDR method is very effective and convenient for the problem of online task scheduling.

Replication data for Dongho Kwak, 2023, "Replication Data for: The effects of rhythmic vertical vibration of cell cultures on the F-actin filament structure"

Replication data for Dongho Kwak, 2023, "Replication Data for: The effects of rhythmic vertical vibration of cell cultures on the F-actin filament structure"

Replicating File Segments between Multi-Cloud Nodes in a Smart City: A Machine Learning Approach

The design and management of smart cities and the IoT is a multidimensional problem. One of those dimensions is cloud and edge computing management. Due to the complexity of the problem, resource sharing is one of the vital and major components that when enhanced, the performance of the whole system is enhanced. Research in data access and storage in multi-clouds and edge servers can broadly be classified to data centers and computational centers. The main aim of data centers is to provide services for accessing, sharing and modifying large databases. On the other hand, the aim of computational centers is to provide services for sharing resources. Present and future distributed applications need to deal with very large multi-petabyte datasets and increasing numbers of associated users and resources. The emergence of IoT-based, multi-cloud systems as a potential solution for large computational and data management problems has initiated significant research activity in the area. Due to the considerable increase in data production and data sharing within scientific communities, the need for improvements in data access and data availability cannot be overlooked. It can be argued that the current approaches of large dataset management do not solve all problems associated with big data and large datasets. The heterogeneity and veracity of big data require careful management. One of the issues for managing big data in a multi-cloud system is the scalability and expendability of the system under consideration. Data replication ensures server load balancing, data availability and improved data access time. The proposed model minimises the cost of data services through minimising a cost function that takes storage cost, host access cost and communication cost into consideration. The relative weights between different components is learned through history and it is different from a cloud to another. The model ensures that data are replicated in a way that increases availability while at the same time decreasing the overall cost of data storage and access time. Using the proposed model avoids the overheads of the traditional full replication techniques. The proposed model is mathematically proven to be sound and valid.

Investigating the effect of virtual machine migration accounting on reliability using a cluster model

The purpose of the article is to develop and verify with the help of mathematical modeling a software method of deploying a fault-tolerant computing cluster with a virtual machine, which consists of two physical servers (main and backup), on which a distributed data storage system with synchronous data replication from the source server to the backup server is deployed. For this purpose, the task is to conduct a computational experiment on a model of a fault-tolerant cluster, which neglects costs during recovery for the migration of virtual machines by means of the mathematical application Mathcad. Combining computing resources into clusters is a way to ensure high reliability, fault tolerance, and continuity of the computing process of computer systems. This is achieved through virtualization, which enables the movement of virtual resources, services, or applications between physical servers while maintaining the continuity of computing processes. The focus of this study is on a failover cluster, which is composed of two physical servers (primary and backup) connected through a switch, and each server has a local hard disk. A distributed storage system with synchronous data replication from the source server to the backup server is deployed on the local disks of the servers, and a virtual machine is running on the cluster. Markovian processes, flows of podias, and Kolmogorov's systems of differential equations are built into the mathematical tools of the model of a water cluster. To ensure the continuity of the computing process in case of a failure of the main server, a shadow copy of the virtual machine is launched on the backup server. The reliability of the failover cluster is measured by the coefficient of non-stationary readiness. A Markov model is proposed to assess the reliability of the failover cluster, taking into account the costs of migrating virtual machines and mechanisms that ensure the continuity of the computing process in the cluster in case of a failure of one physical server. The memory migration process maintains two copies of the virtual machine on different physical servers, enabling them to continue working on the other in the event of failure. A simplified model of the failover cluster neglects the cost of migrating virtual machines and provides an upper estimate of reliability. The study shows that the reliability of a failover cluster, as measured by the non-stationary availability factor, is significantly impacted by the virtual machine migration process. The findings of this study can be used to inform decisions about the technology chosen to ensure the failure stability and continuity of the computing process of computer systems with cluster architecture. The calculations allow us to draw a conclusion about the significant impact of virtual machine migration accounting on reliability. The calculations allow us to draw a conclusion about the significant impact of virtual machine migration accounting on reliability. The calculation was performed under the following failure rates of the server, disk, and switch: λ0 = 1,115×10-5 1/h, λ1 = 3,425×10-6 1/h, λ2 = 2,3×10-6 1/h recovery respectively: μ0 = 0,33 1/h, μ1 = 0,171/h, μ2 = 0,33 1/h. The intensity of synchronization of the distributed storage system: μ3 = 1 1/h, μ4 = 2 1/h. The difference of non-stationary cluster availability coefficients is d = К2(t) – К1(t) = 2.7×10-10

Replication Data for "How Responsive are Wages to Firm-Specific Changes in Labor Demand? Evidence from Idiosyncratic Export Demand Shocks"

Replication Data for "How Responsive are Wages to Firm-Specific Changes in Labor Demand? Evidence from Idiosyncratic Export Demand Shocks"

Quantifying Gender Disparity in Pre-Modern English Literature using Natural Language Processing

Research has continued to shed light on the extent and significance of gender disparity in social, cultural and economic spheres. More recently, computational tools from the data science and Natural Language Processing (NLP) communities have been proposed for measuring such disparity at scale using empirically rigorous methodologies. In this article, we contribute to this line of research by studying gender disparity in 2,443 copyright-expired literary texts published in the pre-modern period, defined in this work as the period ranging from the beginning of the nineteenth through the early twentieth century. Using a replicable data science methodology relying on publicly available and established NLP components, we extract three different gendered character prevalence measures within these texts. We use an extensive set of statistical tests to robustly demonstrate a significant disparity between the prevalence of female characters and male characters in pre-modern literature. We also show that the proportion of female characters in literary texts significantly increases in female-authored texts compared to the same proportion in male-authored texts. However, regression-based analysis shows that, over the 120 year period covered by the corpus, female character prevalence does not change significantly over time, and remains below the parity level of 50%, regardless of the gender of the author. Qualitative analyses further show that descriptions associated with female characters across the corpus are markedly different (and stereotypical) from the descriptions associated with male characters.

Review on data replication strategies in single vs. interconnected cloud systems: Focus on data correlation‐aware strategies

SummaryData replication is a well‐known technique in cloud systems for enhancing availability and performance. Various strategies and surveys have been proposed in this respect. These surveys include comprehensive analysis and classifications. However, to the best of the authors' knowledge, there is no survey concentrating on strategies designed for interconnected cloud systems. In this article, we provide an in‐depth analysis of existing data replication strategies in cloud systems, covering single and interconnected clouds. We also highlight data correlation‐aware strategies as well as their key steps. Furthermore, we examine the major strategies' features such as: () addressed replication issues, () orientation towards the provider and the consumer, () consideration of the service level agreement, () consideration of cost and economic aspects, and () evaluation tools. Finally, we provide a performance analysis through extensive simulations of several replication strategies dedicated for single and interconnected clouds.